Stage Harbor release files 52501-53000

tasks/tasksmith-749ff8d440e1/environment/source/LICENSE

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tasks/tasksmith-749ff8d440e1/environment/source/Makefile

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+.PHONY: quality style test docs
+
+check_dirs := src tests examples docs scripts docker
+
+# Check that source code meets quality standards
+
+# this target runs checks on all files
+quality:
+	ruff check $(check_dirs)
+	ruff format --check $(check_dirs)
+	doc-builder style src/peft tests docs/source --max_len 119 --check_only
+
+# Format source code automatically and check is there are any problems left that need manual fixing
+style:
+	ruff check --fix $(check_dirs)
+	ruff format $(check_dirs)
+	doc-builder style src/peft tests docs/source --max_len 119
+
+test:
+	python -m pytest -n 3 tests/ $(if $(IS_GITHUB_CI),--report-log "ci_tests.log",)
+
+tests_examples_multi_gpu:
+	python -m pytest -m multi_gpu_tests tests/test_gpu_examples.py $(if $(IS_GITHUB_CI),--report-log "multi_gpu_examples.log",)
+
+tests_examples_single_gpu:
+	python -m pytest -m single_gpu_tests tests/test_gpu_examples.py $(if $(IS_GITHUB_CI),--report-log "single_gpu_examples.log",)
+
+tests_core_multi_gpu:
+	python -m pytest -m multi_gpu_tests tests/test_common_gpu.py $(if $(IS_GITHUB_CI),--report-log "core_multi_gpu.log",)
+
+tests_core_single_gpu:
+	python -m pytest -m single_gpu_tests tests/test_common_gpu.py $(if $(IS_GITHUB_CI),--report-log "core_single_gpu.log",)
+
+# exclude gemma tests, as generation fails with torch.compile, these failures
+# trigger side effects that make other tests fail with 'RuntimeError: Offset
+# increment outside graph capture encountered unexpectedly.' 
+# TODO re-enable gemma once/if it is fixed
+tests_common_gpu:
+	python -m pytest tests/test_decoder_models.py -k "not gemma" $(if $(IS_GITHUB_CI),--report-log "common_decoder.log",)
+	python -m pytest tests/test_encoder_decoder_models.py $(if $(IS_GITHUB_CI),--report-log "common_encoder_decoder.log",)
+	python -m pytest tests/test_gptqmodel.py $(if $(IS_GITHUB_CI),--report-log "gptqmodel_gpu.log",)
+
+tests_examples_multi_gpu_bnb:
+	python -m pytest -m "multi_gpu_tests and bitsandbytes" tests/test_gpu_examples.py $(if $(IS_GITHUB_CI),--report-log "multi_gpu_bnb_examples.log",)
+
+tests_examples_single_gpu_bnb:
+	python -m pytest -m "single_gpu_tests and bitsandbytes" tests/test_gpu_examples.py $(if $(IS_GITHUB_CI),--report-log "single_gpu_bnb_examples.log",)
+
+tests_core_multi_gpu_bnb:
+	python -m pytest -m "multi_gpu_tests and bitsandbytes" tests/test_common_gpu.py $(if $(IS_GITHUB_CI),--report-log "core_multi_gpu_bnb.log",)
+
+tests_core_single_gpu_bnb:
+	python -m pytest -m "single_gpu_tests and bitsandbytes" tests/test_common_gpu.py $(if $(IS_GITHUB_CI),--report-log "core_single_gpu_bnb.log",)
+
+# For testing transformers tests for bnb runners
+transformers_tests:
+	RUN_SLOW=1 python -m pytest transformers-clone/tests/quantization/bnb $(if $(IS_GITHUB_CI),--report-log "transformers_tests.log",)
+
+tests_regression:
+	python -m pytest -s --regression tests/regression/ $(if $(IS_GITHUB_CI),--report-log "regression_tests.log",)
+
+tests_torch_compile:
+	python -m pytest tests/test_torch_compile.py $(if $(IS_GITHUB_CI),--report-log "compile_tests.log",)
+
+tests_training:
+	accelerate launch --config_file tests/training/deepspeed_config.yaml tests/training/training.py
+	accelerate launch --config_file tests/training/deepspeed_config.yaml tests/training/training.py --quant 4bit
+	accelerate launch --config_file tests/training/deepspeed_config.yaml tests/training/training.py --quant 8bit
+	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/training.py
+	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/training.py --quant 4bit
+	accelerate launch --config_file tests/training/fsdp2_config.yaml tests/training/training.py
+	accelerate launch --config_file tests/training/fsdp2_config.yaml tests/training/training.py --quant 4bit
+	accelerate launch --config_file tests/training/fsdp2_config.yaml tests/training/training.py --quant 4bit --target_modules q_proj --target_parameters v_proj.weight
+	accelerate launch --config_file tests/training/fsdp_config.yaml tests/training/adapters.py
+	accelerate launch --config_file tests/training/tp_config.yaml tests/training/lora_tp.py

tasks/tasksmith-749ff8d440e1/environment/source/README.md

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+<!---
+Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+<h1 align="center"> <p>🤗 PEFT</p></h1>
+<h3 align="center">
+    <p>State-of-the-art Parameter-Efficient Fine-Tuning (PEFT) methods</p>
+</h3>
+
+Fine-tuning large pretrained models is often prohibitively costly due to their scale. Parameter-Efficient Fine-Tuning (PEFT) methods enable efficient adaptation of large pretrained models to various downstream applications by only fine-tuning a small number of (extra) model parameters instead of all the model's parameters. This significantly decreases the computational and storage costs. Recent state-of-the-art PEFT techniques achieve performance comparable to fully fine-tuned models.
+
+PEFT is integrated with Transformers for easy model training and inference, Diffusers for conveniently managing different adapters, and Accelerate for distributed training and inference for really big models.
+
+> [!TIP]
+> Visit the [PEFT](https://huggingface.co/PEFT) organization to read about the PEFT methods implemented in the library and to see notebooks demonstrating how to apply these methods to a variety of downstream tasks. Click the "Watch repos" button on the organization page to be notified of newly implemented methods and notebooks!
+
+Check the PEFT Adapters API Reference section for a list of supported PEFT methods, and read the [Adapters](https://huggingface.co/docs/peft/en/conceptual_guides/adapter), [Soft prompts](https://huggingface.co/docs/peft/en/conceptual_guides/prompting), and [IA3](https://huggingface.co/docs/peft/en/conceptual_guides/ia3) conceptual guides to learn more about how these methods work.
+
+## Quickstart
+
+Install PEFT from pip:
+
+```bash
+pip install peft
+```
+
+Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with `get_peft_model`. For the bigscience/mt0-large model, you're only training 0.19% of the parameters!
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import LoraConfig, TaskType, get_peft_model
+
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+model_id = "Qwen/Qwen2.5-3B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
+peft_config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    task_type=TaskType.CAUSAL_LM,
+    # target_modules=["q_proj", "v_proj", ...]  # optionally indicate target modules
+)
+model = get_peft_model(model, peft_config)
+model.print_trainable_parameters()
+# prints: trainable params: 3,686,400 || all params: 3,089,625,088 || trainable%: 0.1193
+
+# now perform training on your dataset, e.g. using transformers Trainer, then save the model
+model.save_pretrained("qwen2.5-3b-lora")
+```
+
+To load a PEFT model for inference:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+model_id = "Qwen/Qwen2.5-3B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map=device)
+model = PeftModel.from_pretrained(model, "qwen2.5-3b-lora")
+
+inputs = tokenizer("Preheat the oven to 350 degrees and place the cookie dough", return_tensors="pt")
+outputs = model.generate(**inputs.to(device), max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+# prints something like: Preheat the oven to 350 degrees and place the cookie dough in a baking dish [...]
+```
+
+## Why you should use PEFT
+
+There are many benefits of using PEFT but the main one is the huge savings in compute and storage, making PEFT applicable to many different use cases.
+
+### High performance on consumer hardware
+
+Consider the memory requirements for training the following models on the [ought/raft/twitter_complaints](https://huggingface.co/datasets/ought/raft/viewer/twitter_complaints) dataset with an A100 80GB GPU with more than 64GB of CPU RAM.
+
+|   Model         | Full Finetuning | PEFT-LoRA PyTorch  | PEFT-LoRA DeepSpeed with CPU Offloading |
+| --------- | ---- | ---- | ---- |
+| bigscience/T0_3B (3B params) | 47.14GB GPU / 2.96GB CPU  | 14.4GB GPU / 2.96GB CPU | 9.8GB GPU / 17.8GB CPU |
+| bigscience/mt0-xxl (12B params) | OOM GPU | 56GB GPU / 3GB CPU | 22GB GPU / 52GB CPU |
+| bigscience/bloomz-7b1 (7B params) | OOM GPU | 32GB GPU / 3.8GB CPU | 18.1GB GPU / 35GB CPU |
+
+With LoRA you can fully finetune a 12B parameter model that would've otherwise run out of memory on the 80GB GPU, and comfortably fit and train a 3B parameter model. When you look at the 3B parameter model's performance, it is comparable to a fully finetuned model at a fraction of the GPU memory.
+
+|   Submission Name        | Accuracy |
+| --------- | ---- |
+| Human baseline (crowdsourced) |	0.897 |
+| Flan-T5 | 0.892 |
+| lora-t0-3b | 0.863 |
+
+> [!TIP]
+> The bigscience/T0_3B model performance isn't optimized in the table above. You can squeeze even more performance out of it by playing around with the input instruction templates, LoRA hyperparameters, and other training related hyperparameters. The final checkpoint size of this model is just 19MB compared to 11GB of the full bigscience/T0_3B model. Learn more about the advantages of finetuning with PEFT in this [blog post](https://www.philschmid.de/fine-tune-flan-t5-peft).
+
+### Quantization
+
+Quantization is another method for reducing the memory requirements of a model by representing the data in a lower precision. It can be combined with PEFT methods to make it even easier to train and load LLMs for inference.
+
+* Learn how to finetune [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) with QLoRA and the [TRL](https://huggingface.co/docs/trl/index) library on a 16GB GPU in the [Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem](https://pytorch.org/blog/finetune-llms/) blog post.
+* Learn how to finetune a [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) model for multilingual automatic speech recognition with LoRA and 8-bit quantization in this [notebook](https://colab.research.google.com/drive/1DOkD_5OUjFa0r5Ik3SgywJLJtEo2qLxO?usp=sharing) (see this [notebook](https://colab.research.google.com/drive/1vhF8yueFqha3Y3CpTHN6q9EVcII9EYzs?usp=sharing) instead for an example of streaming a dataset).
+
+### Save compute and storage
+
+PEFT can help you save storage by avoiding full finetuning of models on each of downstream task or dataset. In many cases, you're only finetuning a very small fraction of a model's parameters and each checkpoint is only a few MBs in size (instead of GBs). These smaller PEFT adapters demonstrate performance comparable to a fully finetuned model. If you have many datasets, you can save a lot of storage with a PEFT model and not have to worry about catastrophic forgetting or overfitting the backbone or base model.
+
+## PEFT integrations
+
+PEFT is widely supported across the Hugging Face ecosystem because of the massive efficiency it brings to training and inference.
+
+### Diffusers
+
+The iterative diffusion process consumes a lot of memory which can make it difficult to train. PEFT can help reduce the memory requirements and reduce the storage size of the final model checkpoint. For example, consider the memory required for training a Stable Diffusion model with LoRA on an A100 80GB GPU with more than 64GB of CPU RAM. The final model checkpoint size is only 8.8MB!
+
+|   Model         | Full Finetuning | PEFT-LoRA  | PEFT-LoRA with Gradient Checkpointing  |
+| --------- | ---- | ---- | ---- |
+| CompVis/stable-diffusion-v1-4 | 27.5GB GPU / 3.97GB CPU | 15.5GB GPU / 3.84GB CPU | 8.12GB GPU / 3.77GB CPU | 
+
+> [!TIP]
+> Take a look at the [examples/lora_dreambooth/train_dreambooth.py](examples/lora_dreambooth/train_dreambooth.py) training script to try training your own Stable Diffusion model with LoRA, and play around with the [smangrul/peft-lora-sd-dreambooth](https://huggingface.co/spaces/smangrul/peft-lora-sd-dreambooth) Space which is running on a T4 instance. Learn more about the PEFT integration in Diffusers in this [tutorial](https://huggingface.co/docs/peft/main/en/tutorial/peft_integrations#diffusers).
+
+### Transformers
+
+PEFT is directly integrated with [Transformers](https://huggingface.co/docs/transformers/main/en/peft). After loading a model, call `add_adapter` to add a new PEFT adapter to the model:
+
+```python
+from peft import LoraConfig
+model = ...  # transformers model
+peft_config = LoraConfig(...)
+model.add_adapter(lora_config, adapter_name="lora_1")
+```
+
+To load a trained PEFT adapter, call `load_adapter`:
+
+```python
+model = ...  # transformers model
+model.load_adapter(<path-to-adapter>, adapter_name="lora_1")
+```
+
+And to switch between different adapters, call `set_adapter`:
+
+```python
+model.set_adapter("lora_2")
+```
+
+The Transformers integration doesn't include all the functionalities offered in PEFT, such as methods for merging the adapter into the base model.
+
+### Accelerate
+
+[Accelerate](https://huggingface.co/docs/accelerate/index) is a library for distributed training and inference on various training setups and hardware (GPUs, TPUs, Apple Silicon, etc.). PEFT models work with Accelerate out of the box, making it really convenient to train really large models or use them for inference on consumer hardware with limited resources.
+
+### TRL
+
+PEFT can also be applied to training LLMs with RLHF components such as the ranker and policy. Get started by reading:
+
+* [Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library to learn more about the Direct Preference Optimization (DPO) method and how to apply it to a LLM.
+* [Fine-tuning 20B LLMs with RLHF on a 24GB consumer GPU](https://huggingface.co/blog/trl-peft) with PEFT and the [TRL](https://huggingface.co/docs/trl/index) library, and then try out the [gpt2-sentiment_peft.ipynb](https://github.com/huggingface/trl/blob/main/examples/notebooks/gpt2-sentiment.ipynb) notebook to optimize GPT2 to generate positive movie reviews.
+* [StackLLaMA: A hands-on guide to train LLaMA with RLHF](https://huggingface.co/blog/stackllama) with PEFT, and then try out the [stack_llama/scripts](https://github.com/huggingface/trl/tree/main/examples/research_projects/stack_llama/scripts) for supervised finetuning, reward modeling, and RL finetuning.
+
+## Model support
+
+Use this [Space](https://stevhliu-peft-methods.hf.space) or check out the [docs](https://huggingface.co/docs/peft/main/en/index) to find which models officially support a PEFT method out of the box. Even if you don't see a model listed below, you can manually configure the model config to enable PEFT for a model. Read the [New transformers architecture](https://huggingface.co/docs/peft/main/en/developer_guides/custom_models#new-transformers-architectures) guide to learn how.
+
+## Contribute
+
+If you would like to contribute to PEFT, please check out our [contribution guide](https://huggingface.co/docs/peft/developer_guides/contributing).
+
+## Citing 🤗 PEFT
+
+To use 🤗 PEFT in your publication, please cite it by using the following BibTeX entry.
+
+```bibtex
+@Misc{peft,
+  title =        {{PEFT}: State-of-the-art Parameter-Efficient Fine-Tuning methods},
+  author =       {Sourab Mangrulkar and Sylvain Gugger and Lysandre Debut and Younes Belkada and Sayak Paul and Benjamin Bossan and Marian Tietz},
+  howpublished = {\url{https://github.com/huggingface/peft}},
+  year =         {2022}
+}
+```

tasks/tasksmith-749ff8d440e1/environment/source/docker/README.md

@@ -0,0 +1,6 @@
+# PEFT Docker images
+
+Here we store all PEFT Docker images used in our testing infrastructure. We use python 3.11 for now on all our images.
+
+- `peft-cpu`: PEFT compiled on CPU with all other HF libraries installed on main branch
+- `peft-gpu`: PEFT complied for NVIDIA GPUs with all other HF libraries installed on main branch

tasks/tasksmith-749ff8d440e1/environment/source/docker/peft-cpu/Dockerfile

@@ -0,0 +1,46 @@
+# Builds GPU docker image of PyTorch
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+ENV PYTHON_VERSION=3.11
+# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN apt-get update && \
+    apt-get install -y curl git wget git-lfs ffmpeg libsndfile1-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN git lfs install
+
+# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+
+# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH=/opt/conda/envs/peft/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+# Activate the conda env and install transformers + accelerate from source
+RUN source activate peft && \
+    python3 -m pip install --no-cache-dir \
+    librosa \
+    "soundfile>=0.12.1" \
+    scipy \
+    git+https://github.com/huggingface/transformers \
+    git+https://github.com/huggingface/accelerate \
+    peft[test]@git+https://github.com/huggingface/peft
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN echo "source activate peft" >> ~/.profile
+
+# Activate the virtualenv
+CMD ["/bin/bash"]

tasks/tasksmith-749ff8d440e1/environment/source/docker/peft-gpu/Dockerfile

@@ -0,0 +1,85 @@
+# Builds GPU docker image of PyTorch
+# Uses multi-staged approach to reduce size
+# Stage 1
+# Use base conda image to reduce time
+FROM continuumio/miniconda3:latest AS compile-image
+# Specify py version
+ENV PYTHON_VERSION=3.11
+# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+# Install audio-related libraries
+RUN apt-get update && \
+    apt-get install -y curl git wget git-lfs ffmpeg libsndfile1-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN git lfs install
+
+# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
+
+# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
+# We don't install pytorch here yet since CUDA isn't available
+# instead we use the direct torch wheel
+ENV PATH=/opt/conda/envs/peft/bin:$PATH
+# Activate our bash shell
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+
+# Stage 2
+FROM nvidia/cuda:13.2.1-cudnn-devel-ubuntu24.04 AS build-image
+COPY --from=compile-image /opt/conda /opt/conda
+ENV PATH=/opt/conda/bin:$PATH
+
+# Install apt libs
+RUN apt-get update && \
+    apt-get install -y curl git wget && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+RUN chsh -s /bin/bash
+SHELL ["/bin/bash", "-c"]
+
+RUN conda run -n peft pip install --no-cache-dir bitsandbytes optimum
+
+# Note: we are hard-coding CUDA_ARCH_LIST here since `gptqmodel` requires either nvidia-smi
+# or CUDA_ARCH_LIST for compute capability information. Since the docker build is unlikely
+# to have compute hardware available we use the information from the CI runner (which hosts
+# a NVIDIA L4). So we fix the compute capability to 8.9. In the future we might extend this
+# to a list of compute capabilities (separated by ;).
+# TODO pcre, which is used by gptqmodel, is resulting in a core dump, remove once it's resolved
+# RUN CUDA_ARCH_LIST=8.9 conda run -n peft pip install "gptqmodel>=7.0.0"
+
+RUN \
+    # Add eetq for quantization testing; needs to run without build isolation since the setup
+    # script directly imports torch from the environment which would fail with isolation.
+    # Ninja should speed up build time.
+    conda run -n peft pip install ninja kernels && conda run -n peft pip install --no-build-isolation git+https://github.com/NetEase-FuXi/EETQ.git
+
+# TODO: Importing TE results in: undefined symbol: cublasLtGroupedMatrixLayoutInit_internal, version libcublasLt.so.13
+# Reinstate TE when the issue is resolved (probably this one: https://github.com/NVIDIA/TransformerEngine/issues/2504)
+# RUN NVTE_BUILD_USE_NVIDIA_WHEELS=1 \
+#     CPATH="/usr/local/cuda/include:${CPATH}" \
+#     conda run -n peft pip install --no-build-isolation "transformer_engine[pytorch]"
+
+# Activate the conda env and install transformers + accelerate from source
+RUN conda run -n peft pip install -U --no-cache-dir \
+        librosa \
+        "soundfile>=0.12.1" \
+        scipy \
+        torchao \
+        "fbgemm-gpu-genai>=1.2.0" \
+        git+https://github.com/huggingface/transformers \
+        git+https://github.com/huggingface/accelerate \
+        peft[test]@git+https://github.com/huggingface/peft \
+        # Add aqlm for quantization testing
+        aqlm[gpu]>=1.0.2 \
+        # Add HQQ for quantization testing
+        hqq \
+        deepspeed
+
+RUN conda run -n peft pip freeze | grep transformers
+
+RUN echo "source activate peft" >> ~/.profile
+
+# Activate the virtualenv
+CMD ["/bin/bash"]

tasks/tasksmith-749ff8d440e1/environment/source/docs/Makefile

@@ -0,0 +1,19 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+SOURCEDIR     = source
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

tasks/tasksmith-749ff8d440e1/environment/source/docs/README.md

@@ -0,0 +1,267 @@
+<!---
+Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# Generating the documentation
+
+To generate the documentation, you first have to build it. Several packages are necessary to build the doc, 
+you can install them with the following command, at the root of the code repository:
+
+```bash
+pip install -e ".[docs]"
+```
+
+Then you need to install our special tool that builds the documentation:
+
+```bash
+pip install git+https://github.com/huggingface/doc-builder
+```
+
+---
+**NOTE**
+
+You only need to generate the documentation to inspect it locally (if you're planning changes and want to
+check how they look before committing for instance). You don't have to commit to the built documentation.
+
+---
+
+## Building the documentation
+
+Once you have setup the `doc-builder` and additional packages, you can generate the documentation by 
+typing the following command:
+
+```bash
+doc-builder build peft docs/source/ --build_dir ~/tmp/test-build
+```
+
+You can adapt the `--build_dir` to set any temporary folder you prefer. This command will create it and generate
+the MDX files that will be rendered as the documentation on the main website. You can inspect them in your favorite
+Markdown editor.
+
+## Previewing the documentation
+
+To preview the docs, first install the `watchdog` module with:
+
+```bash
+pip install watchdog
+```
+
+Then run the following command:
+
+```bash
+doc-builder preview {package_name} {path_to_docs}
+```
+
+For example:
+
+```bash
+doc-builder preview peft docs/source
+```
+
+The docs will be viewable at [http://localhost:3000](http://localhost:3000). You can also preview the docs once you have opened a PR. You will see a bot add a comment to a link where the documentation with your changes lives.
+
+---
+**NOTE**
+
+The `preview` command only works with existing doc files. When you add a completely new file, you need to update `_toctree.yml` & restart `preview` command (`ctrl-c` to stop it & call `doc-builder preview ...` again).
+
+---
+
+## Adding a new element to the navigation bar
+
+Accepted files are Markdown (.md or .mdx).
+
+Create a file with its extension and put it in the source directory. You can then link it to the toc-tree by putting
+the filename without the extension in the [`_toctree.yml`](https://github.com/huggingface/peft/blob/main/docs/source/_toctree.yml) file.
+
+## Renaming section headers and moving sections
+
+It helps to keep the old links working when renaming the section header and/or moving sections from one document to another. This is because the old links are likely to be used in Issues, Forums, and Social media and it'd make for a much more superior user experience if users reading those months later could still easily navigate to the originally intended information.
+
+Therefore, we simply keep a little map of moved sections at the end of the document where the original section was. The key is to preserve the original anchor.
+
+So if you renamed a section from: "Section A" to "Section B", then you can add at the end of the file:
+
+```
+Sections that were moved:
+
+[ <a href="#section-b">Section A</a><a id="section-a"></a> ]
+```
+and of course, if you moved it to another file, then:
+
+```
+Sections that were moved:
+
+[ <a href="../new-file#section-b">Section A</a><a id="section-a"></a> ]
+```
+
+Use the relative style to link to the new file so that the versioned docs continue to work.
+
+
+## Writing Documentation - Specification
+
+The `huggingface/peft` documentation follows the
+[Google documentation](https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_google.html) style for docstrings,
+although we can write them directly in Markdown.
+
+### Adding a new tutorial
+
+Adding a new tutorial or section is done in two steps:
+
+- Add a new file under `./source`. This file can either be ReStructuredText (.rst) or Markdown (.md).
+- Link that file in `./source/_toctree.yml` on the correct toc-tree.
+
+Make sure to put your new file under the proper section. It's unlikely to go in the first section (*Get Started*), so
+depending on the intended targets (beginners, more advanced users, or researchers) it should go into sections two, three, or
+four.
+
+### Writing source documentation
+
+Values that should be put in `code` should either be surrounded by backticks: \`like so\`. Note that argument names
+and objects like True, None, or any strings should usually be put in `code`.
+
+When mentioning a class, function, or method, it is recommended to use our syntax for internal links so that our tool
+adds a link to its documentation with this syntax: \[\`XXXClass\`\] or \[\`function\`\]. This requires the class or 
+function to be in the main package.
+
+If you want to create a link to some internal class or function, you need to
+provide its path. For instance: \[\`utils.gather\`\]. This will be converted into a link with
+`utils.gather` in the description. To get rid of the path and only keep the name of the object you are
+linking to in the description, add a ~: \[\`~utils.gather\`\] will generate a link with `gather` in the description.
+
+The same works for methods so you can either use \[\`XXXClass.method\`\] or \[~\`XXXClass.method\`\].
+
+#### Defining arguments in a method
+
+Arguments should be defined with the `Args:` (or `Arguments:` or `Parameters:`) prefix, followed by a line return and
+an indentation. The argument should be followed by its type, with its shape if it is a tensor, a colon, and its
+description:
+
+```
+    Args:
+        n_layers (`int`): The number of layers of the model.
+```
+
+If the description is too long to fit in one line (more than 119 characters in total), another indentation is necessary 
+before writing the description after the argument.
+
+Finally, to maintain uniformity if any *one* description is too long to fit on one line, the 
+rest of the parameters should follow suit and have an indention before their description.
+
+Here's an example showcasing everything so far:
+
+```
+    Args:
+        gradient_accumulation_steps (`int`, *optional*, default to 1):
+            The number of steps that should pass before gradients are accumulated. A number > 1 should be combined with `Accelerator.accumulate`.
+        cpu (`bool`, *optional*):
+            Whether or not to force the script to execute on CPU. Will ignore GPU available if set to `True` and force the execution on one process only.
+```
+
+For optional arguments or arguments with defaults we follow the following syntax: imagine we have a function with the
+following signature:
+
+```
+def my_function(x: str = None, a: float = 1):
+```
+
+then its documentation should look like this:
+
+```
+    Args:
+        x (`str`, *optional*):
+            This argument controls ... and has a description longer than 119 chars.
+        a (`float`, *optional*, defaults to 1):
+            This argument is used to ... and has a description longer than 119 chars.
+```
+
+Note that we always omit the "defaults to \`None\`" when None is the default for any argument. Also note that even
+if the first line describing your argument type and its default gets long, you can't break it into several lines. You can
+however write as many lines as you want in the indented description (see the example above with `input_ids`).
+
+#### Writing a multi-line code block
+
+Multi-line code blocks can be useful for displaying examples. They are done between two lines of three backticks as usual in Markdown:
+
+
+````
+```python
+# first line of code
+# second line
+# etc
+```
+````
+
+#### Writing a return block
+
+The return block should be introduced with the `Returns:` prefix, followed by a line return and an indentation.
+The first line should be the type of the return, followed by a line return. No need to indent further for the elements
+building the return.
+
+Here's an example of a single value return:
+
+```
+    Returns:
+        `List[int]`: A list of integers in the range [0, 1] --- 1 for a special token, 0 for a sequence token.
+```
+
+Here's an example of a tuple return, comprising several objects:
+
+```
+    Returns:
+        `tuple(torch.FloatTensor)` comprising various elements depending on the configuration ([`BertConfig`]) and inputs:
+        - ** loss** (*optional*, returned when `masked_lm_labels` is provided) `torch.FloatTensor` of shape `(1,)` --
+          Total loss is the sum of the masked language modeling loss and the next sequence prediction (classification) loss.
+        - **prediction_scores** (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`) --
+          Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).
+```
+
+## Styling the docstring
+
+We have an automatic script running with the `make style` comment that will make sure that:
+- the docstrings fully take advantage of the line width
+- all code examples are formatted using black, like the code of the Transformers library
+
+This script may have some weird failures if you make a syntax mistake or if you uncover a bug. Therefore, it's
+recommended to commit your changes before running `make style`, so you can revert the changes done by that script
+easily.
+
+## Writing documentation examples
+
+The syntax, for example, docstrings can look as follows:
+
+```
+    Example:
+
+    ```python
+    >>> import time
+    >>> from accelerate import Accelerator
+    >>> accelerator = Accelerator()
+    >>> if accelerator.is_main_process:
+    ...     time.sleep(2)
+    >>> else:
+    ...     print("I'm waiting for the main process to finish its sleep...")
+    >>> accelerator.wait_for_everyone()
+    >>> # Should print on every process at the same time
+    >>> print("Everyone is here")
+    ```
+```
+
+The docstring should give a minimal, clear example of how the respective function 
+is to be used in inference and also include the expected (ideally sensible)
+output.
+Often, readers will try out the example before even going through the function 
+or class definitions. Therefore, it is of utmost importance that the example 
+works as expected.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/_config.py

@@ -0,0 +1,7 @@
+# docstyle-ignore
+INSTALL_CONTENT = """
+# PEFT installation
+! pip install peft accelerate transformers
+# To install from source instead of the last release, comment the command above and uncomment the following one.
+# ! pip install git+https://github.com/huggingface/peft.git
+"""

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/_toctree.yml

@@ -0,0 +1,175 @@
+- title: Get started
+  sections:
+  - local: index
+    title: 🤗 PEFT
+  - local: quicktour
+    title: Quicktour
+  - local: install
+    title: Installation
+
+- title: Tutorial
+  sections:
+  - local: tutorial/peft_model_config
+    title: Configurations and models
+  - local: tutorial/peft_integrations
+    title: Integrations
+
+- title: PEFT method guides
+  sections:
+  - local: task_guides/prompt_based_methods
+    title: Prompt-based methods
+  - local: task_guides/lora_based_methods
+    title: LoRA methods
+  - local: task_guides/ia3
+    title: IA3
+
+- title: Developer guides
+  sections:
+  - local: developer_guides/model_merging
+    title: Model merging
+  - local: developer_guides/quantization
+    title: Quantization
+  - local: developer_guides/lora
+    title: LoRA
+  - local: developer_guides/custom_models
+    title: Custom models
+  - local: developer_guides/low_level_api
+    title: Adapter injection
+  - local: developer_guides/mixed_models
+    title: Mixed adapter types
+  - local: developer_guides/torch_compile
+    title: torch.compile
+  - local: developer_guides/contributing
+    title: Contribute to PEFT
+  - local: developer_guides/troubleshooting
+    title: Troubleshooting
+  - local: developer_guides/checkpoint
+    title: PEFT checkpoint format
+
+- title: 🤗 Accelerate integrations
+  sections:
+  - local: accelerate/deepspeed
+    title: DeepSpeed
+  - local: accelerate/fsdp
+    title: Fully Sharded Data Parallel
+
+- title: Conceptual guides
+  sections:
+  - local: conceptual_guides/adapter
+    title: Adapters
+  - local: conceptual_guides/prompting
+    title: Soft prompts
+  - local: conceptual_guides/ia3
+    title: IA3
+  - local: conceptual_guides/oft
+    title: OFT/BOFT
+
+- sections:
+  - sections:
+    - local: package_reference/auto_class
+      title: AutoPeftModel
+    - local: package_reference/peft_model
+      title: PEFT model
+    - local: package_reference/peft_types
+      title: PEFT types
+    - local: package_reference/config
+      title: Configuration
+    - local: package_reference/tuners
+      title: Tuner
+    title: Main classes
+  - sections:
+    - local: package_reference/adalora
+      title: AdaLoRA
+    - local: package_reference/adamss
+      title: AdaMSS
+    - local: package_reference/ia3
+      title: IA3
+    - local: package_reference/llama_adapter
+      title: Llama-Adapter
+    - local: package_reference/loha
+      title: LoHa
+    - local: package_reference/lokr
+      title: LoKr
+    - local: package_reference/lora
+      title: LoRA
+    - local: package_reference/osf
+      title: OSF
+    - local: package_reference/xlora
+      title: X-LoRA
+    - local: package_reference/adapter_utils
+      title: LyCORIS
+    - local: package_reference/multitask_prompt_tuning
+      title: Multitask Prompt Tuning
+    - local: package_reference/oft
+      title: OFT
+    - local: package_reference/boft
+      title: BOFT
+    - local: package_reference/psoft
+      title: PSOFT
+    - local: package_reference/poly
+      title: Polytropon
+    - local: package_reference/p_tuning
+      title: P-tuning
+    - local: package_reference/prefix_tuning
+      title: Prefix tuning
+    - local: package_reference/cartridges
+      title: Cartridges
+    - local: package_reference/prompt_tuning
+      title: Prompt tuning
+    - local: package_reference/layernorm_tuning
+      title: Layernorm tuning
+    - local: package_reference/vera
+      title: VeRA
+    - local: package_reference/pvera
+      title: PVeRA
+    - local: package_reference/fourierft
+      title: FourierFT
+    - local: package_reference/gralora
+      title: GraLoRA
+    - local: package_reference/vblora
+      title: VB-LoRA
+    - local: package_reference/hira
+      title: HiRA
+    - local: package_reference/hra
+      title: HRA
+    - local: package_reference/cpt
+      title: CPT
+    - local: package_reference/trainable_tokens
+      title: Trainable Tokens
+    - local: package_reference/randlora
+      title: RandLora
+    - local: package_reference/shira
+      title: SHiRA
+    - local: package_reference/c3a
+      title: C3A
+    - local: package_reference/miss
+      title: MiSS
+    - local: package_reference/road
+      title: RoAd
+    - local: package_reference/waveft
+      title: WaveFT
+    - local: package_reference/delora
+      title: DeLoRA
+    - local: package_reference/tinylora
+      title: TinyLoRA
+    - local: package_reference/lily
+      title: Lily
+    - local: package_reference/peanut
+      title: PEANuT
+    - local: package_reference/beft
+      title: BEFT
+
+    title: Adapters
+  - sections:
+    - local: package_reference/merge_utils
+      title: Model merge
+    - local: package_reference/helpers
+      title: Helpers
+    - local: package_reference/hotswap
+      title: Hotswapping adapters
+    - local: package_reference/functional
+      title: Functions for PEFT integration
+    - local: package_reference/lora_conversion
+      title: Converting non-LoRA adapters to LoRA
+    title: Utilities
+  title: API reference

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/accelerate/deepspeed.md

@@ -0,0 +1,449 @@
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+
+# DeepSpeed
+
+[DeepSpeed](https://www.deepspeed.ai/) is a library designed for speed and scale for distributed training of large models with billions of parameters. At its core is the Zero Redundancy Optimizer (ZeRO) that shards optimizer states (ZeRO-1), gradients (ZeRO-2), and parameters (ZeRO-3) across data parallel processes. This drastically reduces memory usage, allowing you to scale your training to billion parameter models. To unlock even more memory efficiency, ZeRO-Offload reduces GPU compute and memory by leveraging CPU resources during optimization.
+
+Both of these features are supported in 🤗 Accelerate, and you can use them with 🤗 PEFT. 
+
+## Compatibility with `bitsandbytes` quantization + LoRA
+
+Below is a table that summarizes the compatibility between PEFT's LoRA, [`bitsandbytes`](https://github.com/TimDettmers/bitsandbytes) library and DeepSpeed Zero stages with respect to fine-tuning. DeepSpeed Zero-1 and 2 will have no effect at inference as stage 1 shards the optimizer states and stage 2 shards the optimizer states and gradients:
+
+| DeepSpeed stage   | Is compatible? |
+|---|---|
+| Zero-1 |  🟢 |
+| Zero-2   |  🟢 |
+| Zero-3  |  🟢 |
+
+For DeepSpeed Stage 3 + QLoRA, please refer to the section [Use PEFT QLoRA and DeepSpeed with ZeRO3 for finetuning large models on multiple GPUs](#use-peft-qlora-and-deepspeed-with-zero3-for-finetuning-large-models-on-multiple-gpus) below.
+
+For confirming these observations, we ran the SFT (Supervised Fine-tuning) [offical example scripts](https://github.com/huggingface/trl/tree/main/examples) of the [Transformers Reinforcement Learning (TRL) library](https://github.com/huggingface/trl) using QLoRA + PEFT and the accelerate configs available [here](https://github.com/huggingface/trl/tree/main/examples/accelerate_configs). We ran these experiments on a 2x NVIDIA T4 GPU.
+
+# Use PEFT and DeepSpeed with ZeRO3 for finetuning large models on multiple devices and multiple nodes
+
+This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/sft/train.py) for performing SFT. You'll configure the script to do SFT (supervised fine-tuning) of Llama-70B model with LoRA and ZeRO-3 on 8xH100 80GB GPUs on a single machine. You can configure it to scale to multiple machines by changing the accelerate config.
+
+## Configuration
+
+Start by running the following command to [create a DeepSpeed configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
+
+The configuration file is used to set the default options when you launch the training script.
+
+```bash
+accelerate config --config_file deepspeed_config.yaml
+```
+
+You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll use ZeRO-3 so make sure you pick those options.
+
+```bash
+`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
+`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them. Pass the same value as you would pass via cmd argument else you will encounter mismatch error.
+`gradient_clipping`: Enable gradient clipping with value. Don't set this as you will be passing it via cmd arguments.
+`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2. Set this as `none` as don't want to enable offloading.
+`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3. Set this as `none` as don't want to enable offloading.
+`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3. Set this to `True`.
+`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3. Set this to `True`.
+`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. Set this to `True`.
+```
+
+Once this is done, the corresponding config should look like below and you can find it in config folder at [deepspeed_config.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/deepspeed_config.yaml):
+
+```yml
+compute_environment: LOCAL_MACHINE                                                                                                                                           
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  gradient_accumulation_steps: 4
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+## Launch command
+
+The launch command is available at [run_peft_deepspeed.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_deepspeed.sh) and it is also shown below:
+```bash
+accelerate launch --config_file "configs/deepspeed_config.yaml"  train.py \
+--seed 100 \
+--model_name_or_path "meta-llama/Llama-2-70b-hf" \
+--dataset_name "smangrul/ultrachat-10k-chatml" \
+--chat_template_format "chatml" \
+--add_special_tokens False \
+--append_concat_token False \
+--splits "train,test" \
+--max_seq_len 2048 \
+--num_train_epochs 1 \
+--logging_steps 5 \
+--log_level "info" \
+--logging_strategy "steps" \
+--eval_strategy "epoch" \
+--save_strategy "epoch" \
+--push_to_hub \
+--hub_private_repo True \
+--hub_strategy "every_save" \
+--bf16 True \
+--packing True \
+--learning_rate 1e-4 \
+--lr_scheduler_type "cosine" \
+--weight_decay 1e-4 \
+--warmup_steps 0 \
+--max_grad_norm 1.0 \
+--output_dir "llama-sft-lora-deepspeed" \
+--per_device_train_batch_size 8 \
+--per_device_eval_batch_size 8 \
+--gradient_accumulation_steps 4 \
+--gradient_checkpointing True \
+--use_reentrant False \
+--dataset_text_field "content" \
+--use_flash_attn True \
+--use_peft_lora True \
+--lora_r 8 \
+--lora_alpha 16 \
+--lora_dropout 0.1 \
+--lora_target_modules "all-linear" \
+--use_4bit_quantization False
+```
+
+Notice that we are using LoRA with  rank=8, alpha=16 and targeting all linear layers. We are passing the deepspeed config file and finetuning 70B Llama model on a subset of the ultrachat dataset.
+
+## The important parts
+
+Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
+
+The first thing to know is that the script uses DeepSpeed for distributed training as the DeepSpeed config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating the PEFT model using the peft config that is passed. After that, when you call `trainer.train()`, [`~trl.SFTTrainer`] internally uses 🤗 Accelerate to prepare the model, optimizer and trainer using the DeepSpeed config to create DeepSpeed engine which is then trained. The main code snippet is below:
+
+```python
+# trainer
+trainer = SFTTrainer(
+    model=model,
+    processing_class=tokenizer,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=peft_config,
+)
+trainer.accelerator.print(f"{trainer.model}")
+
+# train
+checkpoint = None
+if training_args.resume_from_checkpoint is not None:
+    checkpoint = training_args.resume_from_checkpoint
+trainer.train(resume_from_checkpoint=checkpoint)
+
+# saving final model
+trainer.save_model()
+```
+
+## Memory usage
+
+In the above example, the memory consumed per GPU is 64 GB (80%) as seen in the screenshot below:
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/peft_deepspeed_mem_usage.png"/>
+</div>
+<small>GPU memory usage for the training run</small>
+
+## More resources
+You can also refer this blog post [Falcon 180B Finetuning using 🤗 PEFT and DeepSpeed](https://medium.com/@sourabmangrulkar/falcon-180b-finetuning-using-peft-and-deepspeed-b92643091d99) on how to finetune 180B Falcon model on 16 A100 GPUs on 2 machines.
+
+
+# Use PEFT QLoRA and DeepSpeed with ZeRO3 for finetuning large models on multiple GPUs
+
+In this section, we will look at how to use QLoRA and DeepSpeed Stage-3 for finetuning 70B llama model on 2X40GB GPUs.
+For this, we first need `bitsandbytes>=0.43.3`, `accelerate>=1.0.1`, `transformers>4.44.2`, `trl>0.11.4` and `peft>0.13.0`. We need to set `zero3_init_flag` to true when using Accelerate config. Below is the config which can be found at [deepspeed_config_z3_qlora.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/deepspeed_config_z3_qlora.yaml):
+
+```yml
+compute_environment: LOCAL_MACHINE                                                                                                                                           
+debug: false
+deepspeed_config:
+  deepspeed_multinode_launcher: standard
+  offload_optimizer_device: none
+  offload_param_device: none
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 2
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+Launch command is given below which is available at [run_peft_qlora_deepspeed_stage3.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_qlora_deepspeed_stage3.sh):
+```
+accelerate launch --config_file "configs/deepspeed_config_z3_qlora.yaml"  train.py \
+--seed 100 \
+--model_name_or_path "meta-llama/Llama-2-70b-hf" \
+--dataset_name "smangrul/ultrachat-10k-chatml" \
+--chat_template_format "chatml" \
+--add_special_tokens False \
+--append_concat_token False \
+--splits "train,test" \
+--max_seq_len 2048 \
+--num_train_epochs 1 \
+--logging_steps 5 \
+--log_level "info" \
+--logging_strategy "steps" \
+--eval_strategy "epoch" \
+--save_strategy "epoch" \
+--push_to_hub \
+--hub_private_repo True \
+--hub_strategy "every_save" \
+--bf16 True \
+--packing True \
+--learning_rate 1e-4 \
+--lr_scheduler_type "cosine" \
+--weight_decay 1e-4 \
+--warmup_steps 0 \
+--max_grad_norm 1.0 \
+--output_dir "llama-sft-qlora-dsz3" \
+--per_device_train_batch_size 2 \
+--per_device_eval_batch_size 2 \
+--gradient_accumulation_steps 2 \
+--gradient_checkpointing True \
+--use_reentrant True \
+--dataset_text_field "content" \
+--use_flash_attn True \
+--use_peft_lora True \
+--lora_r 8 \
+--lora_alpha 16 \
+--lora_dropout 0.1 \
+--lora_target_modules "all-linear" \
+--use_4bit_quantization True \
+--use_nested_quant True \
+--bnb_4bit_compute_dtype "bfloat16" \
+--bnb_4bit_quant_storage_dtype "bfloat16"
+```
+
+Notice the new argument being passed `bnb_4bit_quant_storage_dtype` which denotes the data type for packing the 4-bit parameters. For example, when it is set to `bfloat16`, **32/4 = 8** 4-bit params are packed together post quantization.
+
+In terms of training code, the important code changes are: 
+
+```diff
+...
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=args.use_4bit_quantization,
+    bnb_4bit_quant_type=args.bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=args.use_nested_quant,
++   bnb_4bit_quant_storage=quant_storage_dtype,
+)
+
+...
+
+model = AutoModelForCausalLM.from_pretrained(
+    args.model_name_or_path,
+    quantization_config=bnb_config,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2" if args.use_flash_attn else "eager",
++   dtype=quant_storage_dtype or torch.float32,
+)
+```
+
+Notice that `dtype` for `AutoModelForCausalLM` is same as the `bnb_4bit_quant_storage` data type. That's it. Everything else is handled by Trainer and TRL.
+
+## Memory usage
+
+In the above example, the memory consumed per GPU is **36.6 GB**. Therefore, what took 8X80GB GPUs with DeepSpeed Stage 3+LoRA and a couple of 80GB GPUs with DDP+QLoRA now requires 2X40GB GPUs. This makes finetuning of large models more accessible.
+
+# Use PEFT and DeepSpeed with ZeRO3 and CPU Offloading for finetuning large models on a single GPU
+This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py). You'll configure the script to train a large model for conditional generation with ZeRO-3 and CPU Offload.
+
+> [!TIP]
+> 💡 To help you get started, check out our example training scripts for [causal language modeling](https://github.com/huggingface/peft/blob/main/examples/causal_language_modeling/peft_lora_clm_accelerate_ds_zero3_offload.py) and [conditional generation](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py). You can adapt these scripts for your own applications or even use them out of the box if your task is similar to the one in the scripts.
+
+## Configuration
+
+Start by running the following command to [create a DeepSpeed configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
+
+The configuration file is used to set the default options when you launch the training script.
+
+```bash
+accelerate config --config_file ds_zero3_cpu.yaml
+```
+
+You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll use ZeRO-3 along with CPU-Offload so make sure you pick those options.
+
+```bash
+`zero_stage`: [0] Disabled, [1] optimizer state partitioning, [2] optimizer+gradient state partitioning and [3] optimizer+gradient+parameter partitioning
+`gradient_accumulation_steps`: Number of training steps to accumulate gradients before averaging and applying them.
+`gradient_clipping`: Enable gradient clipping with value.
+`offload_optimizer_device`: [none] Disable optimizer offloading, [cpu] offload optimizer to CPU, [nvme] offload optimizer to NVMe SSD. Only applicable with ZeRO >= Stage-2.
+`offload_param_device`: [none] Disable parameter offloading, [cpu] offload parameters to CPU, [nvme] offload parameters to NVMe SSD. Only applicable with ZeRO Stage-3.
+`zero3_init_flag`: Decides whether to enable `deepspeed.zero.Init` for constructing massive models. Only applicable with ZeRO Stage-3.
+`zero3_save_16bit_model`: Decides whether to save 16-bit model weights when using ZeRO Stage-3.
+`mixed_precision`: `no` for FP32 training, `fp16` for FP16 mixed-precision training and `bf16` for BF16 mixed-precision training. 
+```
+
+An example [configuration file](https://github.com/huggingface/peft/blob/main/examples/conditional_generation/accelerate_ds_zero3_cpu_offload_config.yaml) might look like the following. The most important thing to notice is that `zero_stage` is set to `3`, and `offload_optimizer_device` and `offload_param_device` are set to the `cpu`.
+
+```yml
+compute_environment: LOCAL_MACHINE
+deepspeed_config:
+  gradient_accumulation_steps: 1
+  gradient_clipping: 1.0
+  offload_optimizer_device: cpu
+  offload_param_device: cpu
+  zero3_init_flag: true
+  zero3_save_16bit_model: true
+  zero_stage: 3
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+dynamo_backend: 'NO'
+fsdp_config: {}
+machine_rank: 0
+main_training_function: main
+megatron_lm_config: {}
+mixed_precision: 'no'
+num_machines: 1
+num_processes: 1
+rdzv_backend: static
+same_network: true
+use_cpu: false
+```
+
+## The important parts
+
+Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
+
+Within the [`main`](https://github.com/huggingface/peft/blob/2822398fbe896f25d4dac5e468624dc5fd65a51b/examples/conditional_generation/peft_lora_seq2seq_accelerate_ds_zero3_offload.py#L103) function, the script creates an [`~accelerate.Accelerator`] class to initialize all the necessary requirements for distributed training.
+
+> [!TIP]
+> 💡 Feel free to change the model and dataset inside the `main` function. If your dataset format is different from the one in the script, you may also need to write your own preprocessing function.
+
+The script also creates a configuration for the 🤗 PEFT method you're using, which in this case, is LoRA. The [`LoraConfig`] specifies the task type and important parameters such as the dimension of the low-rank matrices, the matrices scaling factor, and the dropout probability of the LoRA layers. If you want to use a different 🤗 PEFT method, make sure you replace `LoraConfig` with the appropriate [class](../package_reference/tuners).
+
+```diff
+ def main():
++    accelerator = Accelerator()
+     model_name_or_path = "facebook/bart-large"
+     dataset_name = "twitter_complaints"
++    peft_config = LoraConfig(
+         task_type=TaskType.SEQ_2_SEQ_LM, inference_mode=False, r=8, lora_alpha=32, lora_dropout=0.1
+     )
+```
+
+Throughout the script, you'll see the [`~accelerate.Accelerator.main_process_first`] and [`~accelerate.Accelerator.wait_for_everyone`] functions which help control and synchronize when processes are executed.
+
+The [`get_peft_model`] function takes a base model and the [`peft_config`] you prepared earlier to create a [`PeftModel`]:
+
+```diff
+  model = AutoModelForSeq2SeqLM.from_pretrained(model_name_or_path)
++ model = get_peft_model(model, peft_config)
+```
+
+Pass all the relevant training objects to 🤗 Accelerate's [`~accelerate.Accelerator.prepare`] which makes sure everything is ready for training:
+
+```py
+model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler = accelerator.prepare(
+    model, train_dataloader, eval_dataloader, test_dataloader, optimizer, lr_scheduler
+)
+```
+
+The next bit of code checks whether the DeepSpeed plugin is used in the `Accelerator`, and if the plugin exists, then we check if we are using ZeRO-3. This conditional flag is used when calling `generate` function call during inference for syncing GPUs when the model parameters are sharded:
+
+```py
+is_ds_zero_3 = False
+if getattr(accelerator.state, "deepspeed_plugin", None):
+    is_ds_zero_3 = accelerator.state.deepspeed_plugin.zero_stage == 3
+```
+
+Inside the training loop, the usual `loss.backward()` is replaced by 🤗 Accelerate's [`~accelerate.Accelerator.backward`] which uses the correct `backward()` method based on your configuration:
+
+```diff
+  for epoch in range(num_epochs):
+      with TorchTracemalloc() as tracemalloc:
+          model.train()
+          total_loss = 0
+          for step, batch in enumerate(tqdm(train_dataloader)):
+              outputs = model(**batch)
+              loss = outputs.loss
+              total_loss += loss.detach().float()
++             accelerator.backward(loss)
+              optimizer.step()
+              lr_scheduler.step()
+              optimizer.zero_grad()
+```
+
+That is all! The rest of the script handles the training loop, evaluation, and even pushes it to the Hub for you.
+
+## Train
+
+Run the following command to launch the training script. Earlier, you saved the configuration file to `ds_zero3_cpu.yaml`, so you'll need to pass the path to the launcher with the `--config_file` argument like this:
+
+```bash
+accelerate launch --config_file ds_zero3_cpu.yaml examples/peft_lora_seq2seq_accelerate_ds_zero3_offload.py
+```
+
+You'll see some output logs that track memory usage during training, and once it's completed, the script returns the accuracy and compares the predictions to the labels:
+
+```bash
+GPU Memory before entering the train : 1916
+GPU Memory consumed at the end of the train (end-begin): 66
+GPU Peak Memory consumed during the train (max-begin): 7488
+GPU Total Peak Memory consumed during the train (max): 9404
+CPU Memory before entering the train : 19411
+CPU Memory consumed at the end of the train (end-begin): 0
+CPU Peak Memory consumed during the train (max-begin): 0
+CPU Total Peak Memory consumed during the train (max): 19411
+epoch=4: train_ppl=tensor(1.0705, device='cuda:0') train_epoch_loss=tensor(0.0681, device='cuda:0')
+100%|████████████████████████████████████████████████████████████████████████████████████████████| 7/7 [00:27<00:00,  3.92s/it]
+GPU Memory before entering the eval : 1982
+GPU Memory consumed at the end of the eval (end-begin): -66
+GPU Peak Memory consumed during the eval (max-begin): 672
+GPU Total Peak Memory consumed during the eval (max): 2654
+CPU Memory before entering the eval : 19411
+CPU Memory consumed at the end of the eval (end-begin): 0
+CPU Peak Memory consumed during the eval (max-begin): 0
+CPU Total Peak Memory consumed during the eval (max): 19411
+accuracy=100.0
+eval_preds[:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
+dataset['train'][label_column][:10]=['no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint', 'no complaint', 'no complaint', 'complaint', 'complaint', 'no complaint']
+```
+
+# Caveats
+1. Merging when using PEFT and DeepSpeed is currently unsupported and will raise error.
+2. When using CPU offloading, the major gains from using PEFT to shrink the optimizer states and gradients to that of the adapter weights would be realized on CPU RAM and there won't be savings with respect to GPU memory.
+3. DeepSpeed Stage 3 and qlora when used with CPU offloading leads to more GPU memory usage when compared to disabling CPU offloading. 
+
+> [!TIP]
+> 💡 When you have code that requires merging (and unmerging) of weights, try to manually collect the parameters with DeepSpeed Zero-3 beforehand:
+>
+> ```python
+> import deepspeed
+>
+> is_ds_zero_3 = ... # check if Zero-3
+>
+> with deepspeed.zero.GatheredParameters(list(model.parameters()), enabled= is_ds_zero_3):
+>     model.merge_adapter()
+>     # do whatever is needed, then unmerge in the same context if unmerging is required
+>     ...
+>     model.unmerge_adapter()
+> ```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/accelerate/fsdp.md

@@ -0,0 +1,285 @@
+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Fully Sharded Data Parallel
+
+[Fully sharded data parallel](https://pytorch.org/docs/stable/fsdp.html) (FSDP) is developed for distributed training of large pretrained models up to 1T parameters. FSDP achieves this by sharding the model parameters, gradients, and optimizer states across data parallel processes and it can also offload sharded model parameters to a CPU. The memory efficiency afforded by FSDP allows you to scale training to larger batch or model sizes.
+
+Both of these features are supported in 🤗 Accelerate, and you can use them with 🤗 PEFT. 
+
+# Use PEFT and FSDP
+This section of guide will help you learn how to use our DeepSpeed [training script](https://github.com/huggingface/peft/blob/main/examples/sft/train.py) for performing SFT. You'll configure the script to do SFT (supervised fine-tuning) of Llama-70B model with LoRA and FSDP on 8xH100 80GB GPUs on a single machine. You can configure it to scale to multiple machines by changing the accelerate config.
+
+## Configuration
+
+Start by running the following command to [create a FSDP configuration file](https://huggingface.co/docs/accelerate/quicktour#launching-your-distributed-script) with 🤗 Accelerate. The `--config_file` flag allows you to save the configuration file to a specific location, otherwise it is saved as a `default_config.yaml` file in the 🤗 Accelerate cache.
+
+The configuration file is used to set the default options when you launch the training script.
+
+```bash
+accelerate config --config_file fsdp_config.yaml
+```
+
+You'll be asked a few questions about your setup, and configure the following arguments. In this example, you'll answer the questionnaire as shown in the image below.
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/fsdp-peft-config.png"/>
+</div>
+<small>Creating Accelerate's config to use FSDP</small>
+
+Once this is done, the corresponding config should look like below and you can find it in config folder at [fsdp_config.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/fsdp_config.yaml):
+
+```yml
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch: BACKWARD_PRE
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: false
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: bf16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+## Launch command
+
+The launch command is available at [run_peft_fsdp.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_fsdp.sh) and it is also shown below:
+```bash
+accelerate launch --config_file "configs/fsdp_config.yaml"  train.py \
+--seed 100 \
+--model_name_or_path "meta-llama/Llama-2-70b-hf" \
+--dataset_name "smangrul/ultrachat-10k-chatml" \
+--chat_template_format "chatml" \
+--add_special_tokens False \
+--append_concat_token False \
+--splits "train,test" \
+--max_seq_len 2048 \
+--num_train_epochs 1 \
+--logging_steps 5 \
+--log_level "info" \
+--logging_strategy "steps" \
+--eval_strategy "epoch" \
+--save_strategy "epoch" \
+--push_to_hub \
+--hub_private_repo True \
+--hub_strategy "every_save" \
+--bf16 True \
+--packing True \
+--learning_rate 1e-4 \
+--lr_scheduler_type "cosine" \
+--weight_decay 1e-4 \
+--warmup_steps 0 \
+--max_grad_norm 1.0 \
+--output_dir "llama-sft-lora-fsdp" \
+--per_device_train_batch_size 8 \
+--per_device_eval_batch_size 8 \
+--gradient_accumulation_steps 4 \
+--gradient_checkpointing True \
+--use_reentrant False \
+--dataset_text_field "content" \
+--use_flash_attn True \
+--use_peft_lora True \
+--lora_r 8 \
+--lora_alpha 16 \
+--lora_dropout 0.1 \
+--lora_target_modules "all-linear" \
+--use_4bit_quantization False
+```
+
+Notice that we are using LoRA with  rank=8, alpha=16 and targeting all linear layers. We are passing the FSDP config file and finetuning the 70B Llama model on a subset of the [ultrachat dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k).
+
+## The important parts
+
+Let's dive a little deeper into the script so you can see what's going on, and understand how it works.
+
+The first thing to know is that the script uses FSDP for distributed training as the FSDP config has been passed. The [`~trl.SFTTrainer`] class handles all the heavy lifting of creating PEFT model using the peft config that is passed. After that when you call `trainer.train()`, Trainer internally uses 🤗 Accelerate to prepare model, optimizer and trainer using the FSDP config to create FSDP wrapped model which is then trained. The main code snippet is below:
+
+```python
+# trainer
+trainer = SFTTrainer(
+    model=model,
+    processing_class=tokenizer,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    peft_config=peft_config,
+)
+trainer.accelerator.print(f"{trainer.model}")
+if model_args.use_peft_lora:
+    # handle PEFT+FSDP case
+    trainer.model.print_trainable_parameters()
+    if getattr(trainer.accelerator.state, "fsdp_plugin", None):
+        from peft.utils.other import fsdp_auto_wrap_policy
+
+        fsdp_plugin = trainer.accelerator.state.fsdp_plugin
+        fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(trainer.model)
+
+# train
+checkpoint = None
+if training_args.resume_from_checkpoint is not None:
+    checkpoint = training_args.resume_from_checkpoint
+trainer.train(resume_from_checkpoint=checkpoint)
+
+# saving final model
+if trainer.is_fsdp_enabled:
+    trainer.accelerator.state.fsdp_plugin.set_state_dict_type("FULL_STATE_DICT")
+trainer.save_model()
+```
+
+
+Here, one main thing to note currently when using FSDP with PEFT is that `use_orig_params` needs to be `False` to realize GPU memory savings. Due to `use_orig_params=False`, the auto wrap policy for FSDP needs to change so that trainable and non-trainable parameters are wrapped separately. This is done by the code snippt below which uses the util function `fsdp_auto_wrap_policy` from PEFT:
+
+```
+if getattr(trainer.accelerator.state, "fsdp_plugin", None):
+    from peft.utils.other import fsdp_auto_wrap_policy
+
+    fsdp_plugin = trainer.accelerator.state.fsdp_plugin
+    fsdp_plugin.auto_wrap_policy = fsdp_auto_wrap_policy(trainer.model)
+```
+
+## Memory usage
+
+In the above example, the memory consumed per GPU is  72-80 GB (90-98%) as seen in the screenshot below. The slight increase in GPU memory at the end is when saving the model using `FULL_STATE_DICT` state dict type instead of the `SHARDED_STATE_DICT` so that the model has adapter weights that can be loaded normally with `from_pretrained` method during inference:
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/peft_fsdp_mem_usage.png"/>
+</div>
+<small>GPU memory usage for the training run</small>
+
+# Use PEFT QLoRA and FSDP for finetuning large models on multiple GPUs
+
+In this section, we will look at how to use QLoRA and FSDP for finetuning 70B llama model on 2X24GB GPUs. [Answer.AI](https://www.answer.ai/) in collaboration with bitsandbytes and Hugging Face 🤗 open sourced code enabling the usage of FSDP+QLoRA and explained the whole process in their insightful blogpost [You can now train a 70b language model at home](https://www.answer.ai/posts/2024-03-06-fsdp-qlora.html). This is now integrated in Hugging Face ecosystem. 
+
+For this, we first need `bitsandbytes>=0.43.3`, `accelerate>=1.0.1`, `transformers>4.44.2`, `trl>0.11.4` and `peft>0.13.0`. We need to set `fsdp_cpu_ram_efficient_loading=true`, `fsdp_use_orig_params=false` and `fsdp_offload_params=true`(cpu offloading) when using Accelerate config. When not using accelerate launcher, you can alternately set the environment variable `export FSDP_CPU_RAM_EFFICIENT_LOADING=true`.  Here, we will be using accelerate config and below is the config which can be found at [fsdp_config_qlora.yaml](https://github.com/huggingface/peft/blob/main/examples/sft/configs/fsdp_config_qlora.yaml):
+
+```yml
+compute_environment: LOCAL_MACHINE                                                                                                                                           
+debug: false                                                                                                                                                                 
+distributed_type: FSDP
+downcast_bf16: 'no'
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_backward_prefetch: BACKWARD_PRE
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+machine_rank: 0
+main_training_function: main
+mixed_precision: 'no'
+num_machines: 1
+num_processes: 2
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false
+```
+
+Launch command is given below which is available at [run_peft_qlora_fsdp.sh](https://github.com/huggingface/peft/blob/main/examples/sft/run_peft_qlora_fsdp.sh):
+```
+accelerate launch --config_file "configs/fsdp_config_qlora.yaml"  train.py \
+--seed 100 \
+--model_name_or_path "meta-llama/Llama-2-70b-hf" \
+--dataset_name "smangrul/ultrachat-10k-chatml" \
+--chat_template_format "chatml" \
+--add_special_tokens False \
+--append_concat_token False \
+--splits "train,test" \
+--max_seq_len 2048 \
+--num_train_epochs 1 \
+--logging_steps 5 \
+--log_level "info" \
+--logging_strategy "steps" \
+--eval_strategy "epoch" \
+--save_strategy "epoch" \
+--push_to_hub \
+--hub_private_repo True \
+--hub_strategy "every_save" \
+--bf16 True \
+--packing True \
+--learning_rate 1e-4 \
+--lr_scheduler_type "cosine" \
+--weight_decay 1e-4 \
+--warmup_steps 0 \
+--max_grad_norm 1.0 \
+--output_dir "llama-sft-qlora-fsdp" \
+--per_device_train_batch_size 2 \
+--per_device_eval_batch_size 2 \
+--gradient_accumulation_steps 2 \
+--gradient_checkpointing True \
+--use_reentrant True \
+--dataset_text_field "content" \
+--use_flash_attn True \
+--use_peft_lora True \
+--lora_r 8 \
+--lora_alpha 16 \
+--lora_dropout 0.1 \
+--lora_target_modules "all-linear" \
+--use_4bit_quantization True \
+--use_nested_quant True \
+--bnb_4bit_compute_dtype "bfloat16" \
+--bnb_4bit_quant_storage_dtype "bfloat16"
+```
+
+Notice the new argument being passed, `bnb_4bit_quant_storage_dtype`, which denotes the data type for packing the 4-bit parameters. For example, when it is set to `bfloat16`, **16/4 = 4** 4-bit params are packed together post quantization. When using mixed precision training with `bfloat16`, `bnb_4bit_quant_storage_dtype` can be either `bfloat16` for pure `bfloat16` finetuning, or `float32` for automatic mixed precision (this consumes more GPU memory). When using mixed precision training with `float16`, `bnb_4bit_quant_storage_dtype` should be set to `float32` for stable automatic mixed precision training.
+
+In terms of training code, the important code changes are: 
+
+```diff
+...
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=args.use_4bit_quantization,
+    bnb_4bit_quant_type=args.bnb_4bit_quant_type,
+    bnb_4bit_compute_dtype=compute_dtype,
+    bnb_4bit_use_double_quant=args.use_nested_quant,
++   bnb_4bit_quant_storage=quant_storage_dtype,
+)
+
+...
+
+model = AutoModelForCausalLM.from_pretrained(
+    args.model_name_or_path,
+    quantization_config=bnb_config,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2" if args.use_flash_attn else "eager",
++   dtype=quant_storage_dtype or torch.float32,
+)
+```
+
+Notice that `dtype` for `AutoModelForCausalLM` is same as the `bnb_4bit_quant_storage` data type. That's it. Everything else is handled by Trainer and TRL.
+
+## Memory usage
+
+In the above example, the memory consumed per GPU is **19.6 GB** while CPU RAM usage is around **107 GB**. When disabling CPU offloading, the GPU memory usage is  **35.6 GB/ GPU**. Therefore, what took 16X80GB GPUs for full finetuning, 8X80GB GPUs with FSDP+LoRA, and a couple of 80GB GPUs with DDP+QLoRA, now requires 2X24GB GPUs. This makes finetuning of large models more accessible.
+
+## More resources
+You can also refer the [llama-recipes](https://github.com/facebookresearch/llama-recipes/?tab=readme-ov-file#fine-tuning) repo and [Getting started with Llama](https://llama.meta.com/get-started/#fine-tuning) guide on how to finetune using FSDP and PEFT.
+
+## Caveats
+1. Merging when using PEFT and FSDP is currently unsupported and will raise error.
+2. Passing `modules_to_save` config parameter to is untested at present.
+3. GPU Memory saving when using CPU Offloading is untested at present.
+4. When using FSDP+QLoRA, `paged_adamw_8bit` currently results in an error when saving a checkpoint.
+5. DoRA training with FSDP should work (albeit at lower speed than LoRA). If combined with bitsandbytes (QDoRA), 4-bit quantization should also work, but 8-bit quantization has known issues and is not recommended.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/conceptual_guides/adapter.md

@@ -0,0 +1,136 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Adapters
+
+Adapter-based methods add extra trainable parameters after the attention and fully-connected layers of a frozen pretrained model to reduce memory-usage and speed up training. The method varies depending on the adapter, it could simply be an extra added layer or it could be expressing the weight updates ∆W as a low-rank decomposition of the weight matrix. Either way, the adapters are typically small but demonstrate comparable performance to a fully finetuned model and enable training larger models with fewer resources.
+
+This guide will give you a brief overview of the adapter methods supported by PEFT (if you're interested in learning more details about a specific method, take a look at the linked paper).
+
+## Low-Rank Adaptation (LoRA)
+
+> [!TIP]
+> LoRA is one of the most popular PEFT methods and a good starting point if you're just getting started with PEFT. It was originally developed for large language models but it is a tremendously popular training method for diffusion models because of its efficiency and effectiveness.
+
+As mentioned briefly earlier, [LoRA](https://hf.co/papers/2106.09685) is a technique that accelerates finetuning large models while consuming less memory.
+
+LoRA represents the weight updates ∆W with two smaller matrices (called *update matrices*) through low-rank decomposition. These new matrices can be trained to adapt to the new data while keeping the overall number of parameters low. The original weight matrix remains frozen and doesn't receive any further updates. To produce the final results, the original and extra adapted weights are combined. You could also merge the adapter weights with the base model to eliminate inference latency.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_animated.gif"/>
+</div>
+
+This approach has a number of advantages:
+
+* LoRA makes finetuning more efficient by drastically reducing the number of trainable parameters.
+* The original pretrained weights are kept frozen, which means you can have multiple lightweight and portable LoRA models for various downstream tasks built on top of them.
+* LoRA is orthogonal to other parameter-efficient methods and can be combined with many of them.
+* Performance of models finetuned using LoRA is comparable to the performance of fully finetuned models.
+
+In principle, LoRA can be applied to any subset of weight matrices in a neural network to reduce the number of trainable parameters. However, for simplicity and further parameter efficiency, LoRA is typically only applied to the attention blocks in Transformer models. The resulting number of trainable parameters in a LoRA model depends on the size of the update matrices, which is determined mainly by the rank `r` and the shape of the original weight matrix.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora.png"/>
+</div>
+<small><a href="https://hf.co/papers/2103.10385">Navigating Text-To-Image Customization: From LyCORIS Fine-Tuning to Model Evaluation</a></small>
+
+## Mixture of LoRA Experts (X-LoRA)
+
+[X-LoRA](https://huggingface.co/papers/2402.07148) is a mixture of experts method for LoRA which works by using dense or sparse gating to dynamically activate LoRA experts. The LoRA experts as well as the base model are frozen during training, resulting in a low parameter count as only the gating layers must be trained. In particular, the gating layers output scalings which (depending on config) are granular on the layer and token level. Additionally, during inference, X-LoRA dynamically activates LoRA adapters to recall knowledge and effectively mix them:
+
+The below graphic demonstrates how the scalings change for different prompts for each token. This highlights the activation of different adapters as the generation progresses and the sequence creates new context.
+
+![Token-by-token scalings](https://github.com/EricLBuehler/xlora/raw/master/res/token_by_token_scalings.gif)
+
+For each step, X-LoRA requires the base model to be run twice: first, to get hidden states without any LoRA adapters, and secondly, the hidden states are used to calculate scalings which are applied to the LoRA adapters and the model is run a second time. The output of the second run is the result of the model step.
+
+Ultimately, X-LoRA allows the model to reflect upon its knowledge because of the dual forward pass scheme, and dynamically reconfigure the architecture.
+
+## Low-Rank Hadamard Product (LoHa)
+
+Low-rank decomposition can impact performance because the weight updates are limited to the low-rank space, which can constrain a model's expressiveness. However, you don't necessarily want to use a larger rank because it increases the number of trainable parameters. To address this, [LoHa](https://huggingface.co/papers/2108.06098) (a method originally developed for computer vision) was applied to diffusion models where the ability to generate diverse images is an important consideration. LoHa should also work with general model types, but the embedding layers aren't currently implemented in PEFT.
+
+LoHa uses the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_(matrices)) (element-wise product) instead of the matrix product. ∆W is represented by four smaller matrices instead of two - like in LoRA - and each pair of these low-rank matrices are combined with the Hadamard product. As a result, ∆W can have the same number of trainable parameters but a higher rank and expressivity.
+
+## Low-Rank Kronecker Product (LoKr)
+
+[LoKr](https://hf.co/papers/2309.14859) is very similar to LoRA and LoHa, and it is also mainly applied to diffusion models, though you could also use it with other model types. LoKr replaces the matrix product with the [Kronecker product](https://en.wikipedia.org/wiki/Kronecker_product) instead. The Kronecker product decomposition creates a block matrix which preserves the rank of the original weight matrix. Another benefit of the Kronecker product is that it can be vectorized by stacking the matrix columns. This can speed up the process because you're avoiding fully reconstructing ∆W.
+
+## Orthogonal Finetuning (OFT)
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/oft.png"/>
+</div>
+<small><a href="https://hf.co/papers/2306.07280">Controlling Text-to-Image Diffusion by Orthogonal Finetuning</a></small>
+
+[OFT](https://hf.co/papers/2306.07280) is a method that primarily focuses on preserving a pretrained model's generative performance in the finetuned model. It tries to maintain the same cosine similarity (hyperspherical energy) between all pairwise neurons in a layer because this better captures the semantic information among neurons. This means OFT is more capable at preserving the subject and it is better for controllable generation (similar to [ControlNet](https://huggingface.co/docs/diffusers/using-diffusers/controlnet)).
+
+OFT preserves the hyperspherical energy by learning an orthogonal transformation for neurons to keep the cosine similarity between them unchanged. In practice, this means taking the matrix product of an orthogonal matrix with the pretrained weight matrix. However, to be parameter-efficient, the orthogonal matrix is represented as a block-diagonal matrix with rank `r` blocks. Whereas LoRA reduces the number of trainable parameters with low-rank structures, OFT reduces the number of trainable parameters with a sparse block-diagonal matrix structure.
+
+## Orthogonal Butterfly (BOFT)
+
+[BOFT](https://hf.co/papers/2311.06243) is an improved orthogonal finetuning method that focuses on preserving a pretrained model's generative capabilities while being significantly more parameter-efficient than standard OFT. Like OFT, BOFT maintains the same cosine similarity (hyperspherical energy) between all pairwise neurons in a layer by applying an orthogonal transformation to the pretrained weight matrix, ensuring the semantic relationships among neurons are preserved.
+
+Instead of using a block-diagonal orthogonal matrix, BOFT factorizes the orthogonal transformation into a product of **sparse butterfly matrices** (originally introduced in the [Cooley–Tukey FFT](https://en.wikipedia.org/wiki/Cooley%E2%80%93Tukey_FFT_algorithm)). Unlike OFT's block-diagonal rotations, which only mix inputs within each block, the butterfly structure guarantees that every input can influence every output, producing a **dense connectivity** with just `O(d log d)` parameters. This factorization preserves expressivity while drastically reducing the parameter count compared to OFT (at the expense of computation time).
+
+In practice, BOFT multiplies each pretrained weight matrix by a sequence of butterfly-structured orthogonal factors, enabling efficient and expressive neuron rotations. This makes BOFT well-suited for controllable generation and tasks where maintaining the pretrained model's subject representation is critical, while also scaling to larger models with lower memory and compute overhead.
+
+## Adaptive Low-Rank Adaptation (AdaLoRA)
+
+[AdaLoRA](https://hf.co/papers/2303.10512) manages the parameter budget introduced from LoRA by allocating more parameters - in other words, a higher rank `r` - for important weight matrices that are better adapted for a task and pruning less important ones. The rank is controlled by a method similar to singular value decomposition (SVD). The ∆W is parameterized with two orthogonal matrices and a diagonal matrix which contains singular values. This parametrization method avoids iteratively applying SVD which is computationally expensive. Based on this method, the rank of ∆W is adjusted according to an importance score. ∆W is divided into triplets and each triplet is scored according to its contribution to model performance. Triplets with low importance scores are pruned and triplets with high importance scores are kept for finetuning.
+
+Training with AdaLoRA has three phases: the init phase, the budgeting phase and the final phase. In the initial phase, no budgeting is applied, therefore the ranks are not touched. During the budgeting phase the process described above is applied and the rank is redistributed according to a budget, aiming to give more important adapters more rank and less important layers less. When reaching the final phase, budgeting has ended, the ranks are redistributed but we may continue training for a while with the redistributed ranks to further improve performance.
+
+## Llama-Adapter
+
+[Llama-Adapter](https://hf.co/papers/2303.16199) is a method for adapting Llama into an instruction-following model. To help adapt the model for instruction-following, the adapter is trained with a 52K instruction-output dataset.
+
+A set of learnable adaption prompts are prefixed to the input instruction tokens. These are inserted into the upper layers of the model because it is better to learn with the higher-level semantics of the pretrained model. The instruction-output tokens prefixed to the input guide the adaption prompt to generate a contextual response.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/llama-adapter.png"/>
+</div>
+<small><a href="https://hf.co/papers/2303.16199">LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention</a></small>
+
+To avoid adding noise to the tokens, the adapter uses zero-initialized attention. On top of this, the adapter adds a learnable gating factor (initialized with zeros) to progressively add information to the model during training. This prevents overwhelming the model's pretrained knowledge with the newly learned instructions.
+
+## Householder Reflection Adaptation (HRA)
+
+[HRA](https://huggingface.co/papers/2405.17484) provides a new perspective connecting LoRA to OFT, which means it can harness the advantages of both strategies, reduce parameters and computation costs while penalizing the loss of pre-training knowledge. 
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/hra.png"/>
+</div>
+<small><a href="https://huggingface.co/papers/2405.17484">Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation</a></small>
+
+HRA constructs a chain of `r` trainable Householder reflections (HRs). Because the Householder reflection matrix is an orthogonal matrix and the product of orthogonal matrices is also an orthogonal matrix, HRA satisfies the theoretical guarantee of Orthogonal Finetuning (OFT). Meanwhile, HRA can also be viewed as a low-rank fine-tuning adapter by rewriting formula. 
+
+The higher `r`, the more trainable parameters, resulting in a larger model capacity and better performance. Besides, due to the chain structure, the orthogonality of HR planes impacts the capacity and regularity of HRA. To achieve a trade-off between the model capacity and regularity, an orthogonality regularizer of the HR planes is added to the loss function. The weight \\(\lambda\\) can control the strength of the regularizer. 
+
+## Bone
+
+Bone was deprecated and removed in PEFT v0.19.0 in favor of [MiSS](https://huggingface.co/papers/2409.15371) (new version of paper: "MiSS: Balancing LoRA Performance and Efficiency with Simple Shard Sharing"). If you already have a Bone checkpoint, you can use `/scripts/convert-bone-to-miss.py` to convert it into a MiSS checkpoint and proceed with training using MiSS.
+
+## MiSS
+[MiSS](https://github.com/Joluck/MiSS) Matrix Shard Sharing is a novel Parameter-Efficient Fine-Tuning (PEFT) method designed to address the trade-off between adaptability and efficiency in Large Language Models. The core approach of MiSS involves a simple shard-sharing mechanism. It achieves low-rank adaptation by decomposing a weight matrix into multiple fragments and then utilizing a shared, trainable "common fragment." The final low-rank update matrix is constructed by replicating these shared, partitioned shards. (MiSS is a novel PEFT method that adopts a low-rank structure, requires only a single trainable matrix, and introduces a new update mechanism distinct from LoRA, achieving an excellent balance between performance and efficiency.)
+
+<small><a href="https://arxiv.org/abs/2409.15371">MiSS: Revisiting the Trade-off in LoRA with an Efficient Shard-Sharing Structure</a></small>
+
+Intuitively, the shape of a single trainable matrix in MiSS is consistent with `lora_B`, so the `r` parameter in MiSS is less than the `r` in LoRA by (`in_feature * r`).
+
+Note: Bat's r (b) is special and requires that weight W satisfies the conditions `in_features % r == 0` and `out_features % r == 0`. Additionally, when `in_features == out_features` and MiSS-r equals LoRA-r, MiSS's number of trainable parameters is only half that of LoRA.
+
+Although the nonlinear updates of Bat bring some performance improvements, they also increase computational overhead. Its main purpose is to provide researchers with a direction for improvement. Therefore, we recommend fine-tuning the comprehensive MiSS model instead.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/conceptual_guides/ia3.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# IA3 
+
+This conceptual guide gives a brief overview of [IA3](https://huggingface.co/papers/2205.05638), a parameter-efficient fine tuning technique that is 
+intended to improve over [LoRA](./lora).
+
+To make fine-tuning more efficient, IA3 (Infused Adapter by Inhibiting and Amplifying Inner Activations) 
+rescales inner activations with learned vectors. These learned vectors are injected in the attention and feedforward modules 
+in a typical transformer-based architecture. These learned vectors are the only trainable parameters during fine-tuning, and thus the original 
+weights remain frozen. Dealing with learned vectors (as opposed to learned low-rank updates to a weight matrix like LoRA)
+keeps the number of trainable parameters much smaller. 
+
+Being similar to LoRA, IA3 carries many of the same advantages: 
+
+* IA3 makes fine-tuning more efficient by drastically reducing the number of trainable parameters. (For T0, an IA3 model only has about 0.01% trainable parameters, while even LoRA has > 0.1%)
+* The original pre-trained weights are kept frozen, which means you can have multiple lightweight and portable IA3 models for various downstream tasks built on top of them.
+* Performance of models fine-tuned using IA3 is comparable to the performance of fully fine-tuned models.
+* IA3 does not add any inference latency because adapter weights can be merged with the base model.
+
+In principle, IA3 can be applied to any subset of weight matrices in a neural network to reduce the number of trainable
+parameters. Following the authors' implementation, IA3 weights are added to the key, value and feedforward layers
+of a Transformer model. To be specific, for transformer models, IA3 weights are added to the outputs of key and value layers, and to the input of the second feedforward layer
+in each transformer block.
+
+Given the target layers for injecting IA3 parameters, the number of trainable parameters
+can be determined based on the size of the weight matrices.
+
+
+## Common IA3 parameters in PEFT
+
+As with other methods supported by PEFT, to fine-tune a model using IA3, you need to:
+
+1. Instantiate a base model.
+2. Create a configuration (`IA3Config`) where you define IA3-specific parameters.
+3. Wrap the base model with `get_peft_model()` to get a trainable `PeftModel`.
+4. Train the `PeftModel` as you normally would train the base model.
+
+`IA3Config` allows you to control how IA3 is applied to the base model through the following parameters:
+
+- `target_modules`: The modules (for example, attention blocks) to apply the IA3 vectors.
+- `feedforward_modules`: The list of modules to be treated as feedforward layers in `target_modules`. While learned vectors are multiplied with
+the output activation for attention blocks, the vectors are multiplied with the input for classic feedforward layers. Note that `feedforward_modules` must be a subset of `target_modules`.
+- `modules_to_save`: List of modules apart from IA3 layers to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
+
+## Example Usage
+
+For the task of sequence classification, one can initialize the IA3 config for a Llama model as follows:
+
+```py
+peft_config = IA3Config(
+    task_type=TaskType.SEQ_CLS, target_modules=["k_proj", "v_proj", "down_proj"], feedforward_modules=["down_proj"]
+)
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/conceptual_guides/oft.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Orthogonal Finetuning (OFT and BOFT) 
+
+This conceptual guide gives a brief overview of [OFT](https://huggingface.co/papers/2306.07280), [OFTv2](https://huggingface.co/papers/2506.19847) and [BOFT](https://huggingface.co/papers/2311.06243), a parameter-efficient fine-tuning technique that utilizes orthogonal matrix to multiplicatively transform the pretrained weight matrices.
+
+To achieve efficient fine-tuning, OFT represents the weight updates with an orthogonal transformation. The orthogonal transformation is parameterized by an orthogonal matrix multiplied to the pretrained weight matrix. These new matrices can be trained to adapt to the new data while keeping the overall number of changes low. The original weight matrix remains frozen and doesn't receive any further adjustments. To produce the final results, both the original and the adapted weights are multiplied togethor.
+
+Orthogonal Butterfly (BOFT) generalizes OFT with Butterfly factorization and further improves its parameter efficiency and finetuning flexibility. In short, OFT can be viewed as a special case of BOFT. Different from LoRA that uses additive low-rank weight updates, BOFT uses multiplicative orthogonal weight updates. The comparison is shown below.
+
+<div class="flex justify-center">
+    <img src="https://raw.githubusercontent.com/wy1iu/butterfly-oft/main/assets/BOFT_comparison.png"/>
+</div>
+
+
+BOFT has some advantages compared to LoRA: 
+
+* BOFT proposes a simple yet generic way to finetune pretrained models to downstream tasks, yielding a better preservation of pretraining knowledge and a better parameter efficiency.
+* Through the orthogonality, BOFT introduces a structural constraint, i.e., keeping the [hyperspherical energy](https://huggingface.co/papers/1805.09298) unchanged during finetuning. This can effectively reduce the forgetting of pretraining knowledge.
+* BOFT uses the butterfly factorization to efficiently parameterize the orthogonal matrix, which yields a compact yet expressive learning space (i.e., hypothesis class).
+* The sparse matrix decomposition in BOFT brings in additional inductive biases that are beneficial to generalization.
+
+In principle, BOFT can be applied to any subset of weight matrices in a neural network to reduce the number of trainable parameters. Given the target layers for injecting BOFT parameters, the number of trainable parameters can be determined based on the size of the weight matrices.
+
+## Merge OFT/BOFT weights into the base model
+
+Similar to LoRA, the weights learned by OFT/BOFT can be integrated into the pretrained weight matrices using the merge_and_unload() function. This function merges the adapter weights with the base model which allows you to effectively use the newly merged model as a standalone model.
+
+<div class="flex justify-center">
+    <img src="https://raw.githubusercontent.com/wy1iu/butterfly-oft/main/assets/boft_merge.png"/>
+</div>
+
+This works because during training, the orthogonal weight matrix (R in the diagram above) and the pretrained weight matrices are separate. But once training is complete, these weights can actually be merged (multiplied) into a new weight matrix that is equivalent.
+
+## Utils for OFT / BOFT
+
+### Common OFT / BOFT parameters in PEFT
+
+As with other methods supported by PEFT, to fine-tune a model using OFT or BOFT, you need to:
+
+1. Instantiate a base model.
+2. Create a configuration (`OFTConfig` or `BOFTConfig`) where you define OFT/BOFT-specific parameters.
+3. Wrap the base model with `get_peft_model()` to get a trainable `PeftModel`.
+4. Train the `PeftModel` as you normally would train the base model.
+
+
+### OFT-specific parameters
+
+`OFTConfig` allows you to control how OFT is applied to the base model through the following parameters:
+
+- `r`: OFT rank, number of OFT blocks per injected layer. **Bigger** `r` results in more sparse update matrices with **fewer** trainable paramters. **Note**: You can only specify either `r` or `oft_block_size`, but not both simultaneously, because `r` × `oft_block_size` = layer dimension. For simplicity, we let the user speficy either `r` or `oft_block_size` and infer the other one. Default set to `r = 0`, the user is advised to set the `oft_block_size` instead for better clarity.
+- `oft_block_size`: OFT block size across different layers. **Bigger** `oft_block_size` results in more dense update matrices with **more** trainable parameters. **Note**: Please choose `oft_block_size` to be divisible by layer's input dimension (`in_features`), e.g., 4, 8, 16. You can only specify either `r` or `oft_block_size`, but not both simultaneously, because `r` × `oft_block_size` = layer dimension. For simplicity, we let the user speficy either `r` or `oft_block_size` and infer the other one. Default set to `oft_block_size = 32`. 
+- `use_cayley_neumann`: Specifies whether to use the Cayley-Neumann parameterization (efficient but approximate) or the vanilla Cayley parameterization (exact but computationally expensive because of matrix inverse). We recommend to set it to `True` for better efficiency, but performance may be slightly worse because of the approximation error. Please test both settings (`True` and `False`) depending on your needs. Default is `False`.
+- `module_dropout`: The multiplicative dropout probability, by setting OFT blocks to identity during training, similar to the dropout layer in LoRA.
+- `bias`: specify if the `bias` parameters should be trained. Can be `"none"`, `"all"` or `"oft_only"`.
+- `target_modules`: The modules (for example, attention blocks) to inject the OFT matrices.
+- `modules_to_save`: List of modules apart from OFT matrices to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
+
+### BOFT-specific parameters
+
+`BOFTConfig` allows you to control how BOFT is applied to the base model through the following parameters:
+
+- `boft_block_size`: the BOFT matrix block size across different layers, expressed in `int`. **Bigger** `boft_block_size` results in more dense update matrices with **more** trainable parameters. **Note**, please choose `boft_block_size` to be divisible by most layer's input dimension (`in_features`), e.g., 4, 8, 16. Also, please only 
+specify either `boft_block_size` or `boft_block_num`, but not both simultaneously or leaving both to 0, because `boft_block_size` x `boft_block_num` must equal the layer's input dimension.
+- `boft_block_num`: the number of BOFT matrix blocks across different layers, expressed in `int`. **Bigger** `boft_block_num` result in sparser update matrices with **fewer** trainable parameters. **Note**, please choose `boft_block_num` to be divisible by most layer's input dimension (`in_features`), e.g., 4, 8, 16. Also, please only 
+specify either `boft_block_size` or `boft_block_num`, but not both simultaneously or leaving both to 0, because `boft_block_size` x `boft_block_num` must equal the layer's input dimension.
+- `boft_n_butterfly_factor`: the number of butterfly factors. **Note**, for `boft_n_butterfly_factor=1`, BOFT is the same as vanilla OFT, for `boft_n_butterfly_factor=2`, the effective block size of OFT becomes twice as big and the number of blocks become half.
+- `bias`: specify if the `bias` parameters should be trained. Can be `"none"`, `"all"` or `"boft_only"`.
+- `boft_dropout`: specify the probability of multiplicative dropout.
+- `target_modules`: The modules (for example, attention blocks) to inject the OFT/BOFT matrices.
+- `modules_to_save`: List of modules apart from OFT/BOFT matrices to be set as trainable and saved in the final checkpoint. These typically include model's custom head that is randomly initialized for the fine-tuning task.
+
+
+
+## OFT Example Usage
+
+For using OFT for quantized finetuning with [TRL](https://github.com/huggingface/trl) for `SFT`, `PPO`, or `DPO` fine-tuning, follow the following outline:
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from trl import SFTTrainer
+from peft import OFTConfig
+
+if use_quantization:
+    bnb_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_storage=torch.bfloat16,
+    )
+
+model = AutoModelForCausalLM.from_pretrained(
+    "model_name", 
+    quantization_config=bnb_config
+)
+tokenizer = AutoTokenizer.from_pretrained("model_name")
+
+# Configure OFT
+peft_config = OFTConfig(
+    oft_block_size=32,
+    use_cayley_neumann=True,
+    target_modules="all-linear",
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+trainer = SFTTrainer(
+    model=model,
+    train_dataset=ds['train'],
+    peft_config=peft_config,
+    processing_class=tokenizer,
+    args=training_arguments,
+    data_collator=collator,
+)
+
+trainer.train()
+```
+
+
+## BOFT Example Usage
+
+For an example of the BOFT method application to various downstream tasks, please refer to the following guides:
+
+Take a look at the following step-by-step guides on how to finetune a model with BOFT:
+- [Dreambooth finetuning with BOFT](https://github.com/huggingface/peft/blob/main/examples/boft_dreambooth/boft_dreambooth.md)
+- [Controllable generation finetuning with BOFT (ControlNet)](https://github.com/huggingface/peft/blob/main/examples/boft_controlnet/boft_controlnet.md)
+
+For the task of image classification, one can initialize the BOFT config for a DinoV2 model as follows:
+
+```py
+import transformers
+from transformers import AutoModelForSeq2SeqLM, BOFTConfig
+from peft import BOFTConfig, get_peft_model
+
+config = BOFTConfig(
+    boft_block_size=4,
+    boft_n_butterfly_factor=2,
+    target_modules=["query", "value", "key", "output.dense", "mlp.fc1", "mlp.fc2"],
+    boft_dropout=0.1,
+    bias="boft_only",
+    modules_to_save=["classifier"],
+)
+
+model = transformers.Dinov2ForImageClassification.from_pretrained(
+    "facebook/dinov2-large",
+    num_labels=100,
+)
+
+boft_model = get_peft_model(model, config)
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/conceptual_guides/prompting.md

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+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Soft prompts
+
+Training large pretrained language models is very time-consuming and compute-intensive. As they continue to grow in size, there is increasing interest in more efficient training methods such as *prompting*. Prompting primes a frozen pretrained model for a specific downstream task by including a text prompt that describes the task or even demonstrates an example of the task. With prompting, you can avoid fully training a separate model for each downstream task, and use the same frozen pretrained model instead. This is a lot easier because you can use the same model for several different tasks, and it is significantly more efficient to train and store a smaller set of prompt parameters than to train all the model's parameters.
+
+There are two categories of prompting methods:
+
+- hard prompts are manually handcrafted text prompts with discrete input tokens; the downside is that it requires a lot of effort to create a good prompt
+- soft prompts are learnable tensors concatenated with the input embeddings that can be optimized to a dataset; the downside is that they aren't human readable because you aren't matching these "virtual tokens" to the embeddings of a real word
+
+This conceptual guide provides a brief overview of the soft prompt methods included in 🤗 PEFT: prompt tuning, prefix tuning, P-tuning, and multitask prompt tuning.
+
+## Prompt tuning
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/prompt-tuning.png"/>
+</div>
+<small>Only train and store a significantly smaller set of task-specific prompt parameters <a href="https://hf.co/papers/2104.08691">(image source)</a>.</small>
+
+[Prompt tuning](https://hf.co/papers/2104.08691) was developed for text classification tasks on T5 models, and all downstream tasks are cast as a text generation task. For example, sequence classification usually assigns a single class label to a sequence of text. By casting it as a text generation task, the tokens that make up the class label are *generated*. Prompts are added to the input as a series of tokens. Typically, the model parameters are fixed which means the prompt tokens are also fixed by the model parameters.
+
+The key idea behind prompt tuning is that prompt tokens have their own parameters that are updated independently. This means you can keep the pretrained model's parameters frozen, and only update the gradients of the prompt token embeddings. The results are comparable to the traditional method of training the entire model, and prompt tuning performance scales as model size increases.
+
+Take a look at [Prompt tuning for causal language modeling](../task_guides/clm-prompt-tuning) for a step-by-step guide on how to train a model with prompt tuning.
+
+## Prefix tuning
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/prefix-tuning.png"/>
+</div>
+<small>Optimize the prefix parameters for each task <a href="https://hf.co/papers/2101.00190">(image source)</a>.</small>
+
+[Prefix tuning](https://hf.co/papers/2101.00190) was designed for natural language generation (NLG) tasks on GPT models. It is very similar to prompt tuning; prefix tuning also prepends a sequence of task-specific vectors to the input that can be trained and updated while keeping the rest of the pretrained model's parameters frozen. 
+
+The main difference is that the prefix parameters are inserted in **all** of the model layers, whereas prompt tuning only adds the prompt parameters to the model input embeddings. The prefix parameters are also optimized by a separate feed-forward network (FFN) instead of training directly on the soft prompts because it causes instability and hurts performance. The FFN is discarded after updating the soft prompts.
+
+As a result, the authors found that prefix tuning demonstrates comparable performance to fully finetuning a model, despite having 1000x fewer parameters, and it performs even better in low-data settings.
+
+Take a look at [Prefix tuning for conditional generation](../task_guides/seq2seq-prefix-tuning) for a step-by-step guide on how to train a model with prefix tuning.
+
+## P-tuning
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/p-tuning.png"/>
+</div>
+<small>Prompt tokens can be inserted anywhere in the input sequence, and they are optimized by a prompt encoder <a href="https://hf.co/papers/2103.10385">(image source)</a>.</small>
+
+[P-tuning](https://hf.co/papers/2103.10385) is designed for natural language understanding (NLU) tasks and all language models. 
+It is another variation of a soft prompt method; P-tuning also adds a trainable embedding tensor that can be optimized to find better prompts, and it uses a prompt encoder (a bidirectional long-short term memory network or LSTM) to optimize the prompt parameters. Unlike prefix tuning though:
+
+- the prompt tokens can be inserted anywhere in the input sequence, and it isn't restricted to only the beginning
+- the prompt tokens are only added to the input instead of adding them to every layer of the model
+- introducing *anchor* tokens can improve performance because they indicate characteristics of a component in the input sequence
+
+The results suggest that P-tuning is more efficient than manually crafting prompts, and it enables GPT-like models to compete with BERT-like models on NLU tasks.
+
+Take a look at [P-tuning for sequence classification](../task_guides/ptuning-seq-classification) for a step-by-step guide on how to train a model with P-tuning.
+
+## Multitask prompt tuning
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/mpt.png"/>
+</div>
+<small><a href="https://hf.co/papers/2303.02861">Multitask prompt tuning enables parameter-efficient transfer learning</a>.</small>
+
+[Multitask prompt tuning (MPT)](https://hf.co/papers/2303.02861) learns a single prompt from data for multiple task types that can be shared for different target tasks. Other existing approaches learn a separate soft prompt for each task that need to be retrieved or aggregated for adaptation to target tasks. MPT consists of two stages:
+
+1. source training - for each task, its soft prompt is decomposed into task-specific vectors. The task-specific vectors are multiplied together to form another matrix W, and the Hadamard product is used between W and a shared prompt matrix P to generate a task-specific prompt matrix. The task-specific prompts are distilled into a single prompt matrix that is shared across all tasks. This prompt is trained with multitask training.
+2. target adaptation - to adapt the single prompt for a target task, a target prompt is initialized and expressed as the Hadamard product of the shared prompt matrix and the task-specific low-rank prompt matrix.
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/mpt-decomposition.png"/>
+</div>
+<small><a href="https://hf.co/papers/2103.10385">Prompt decomposition</a>.</small>
+
+
+## Context-Aware Prompt Tuning (CPT)
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/cpt.png"/>
+</div>
+<small>CPT optimizing only specific token embeddings while keeping the rest of the model frozen <a href="https://huggingface.co/papers/2410.17222">(image source)</a>.</small>
+
+[Context-Aware Prompt Tuning (CPT)](https://huggingface.co/papers/2410.17222) is designed to enhance few-shot classification by refining only context embeddings. 
+This approach combines ideas from In-Context Learning (ICL), Prompt Tuning (PT), and adversarial optimization, focusing on making model adaptation both parameter-efficient and effective.
+In CPT, only specific context token embeddings are optimized, while the rest of the model remains frozen. 
+To prevent overfitting and maintain stability, CPT uses controlled perturbations to limit the allowed changes to context embeddings within a defined range. 
+Additionally, to address the phenomenon of recency bias—where examples near the end of the context tend to be prioritized over earlier ones—CPT applies a decay loss factor.
+
+Take a look at [Example](https://github.com/huggingface/peft/blob/main/examples/cpt_finetuning/README.md) for a step-by-step guide on how to train a model with CPT.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/checkpoint.md

@@ -0,0 +1,244 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# PEFT checkpoint format
+
+This document describes how PEFT's checkpoint files are structured and how to convert between the PEFT format and other formats.
+
+## PEFT files
+
+PEFT (parameter-efficient fine-tuning) methods only update a small subset of a model's parameters rather than all of them. This is nice because checkpoint files can generally be much smaller than the original model files and are easier to store and share. However, this also means that to load a PEFT model, you need to have the original model available as well.
+
+When you call [`~PeftModel.save_pretrained`] on a PEFT model, the PEFT model saves three files, described below:
+
+1. `adapter_model.safetensors` or `adapter_model.bin`
+
+By default, the model is saved in the `safetensors` format, a secure alternative to the `bin` format, which is known to be susceptible to [security vulnerabilities](https://huggingface.co/docs/hub/security-pickle) because it uses the pickle utility under the hood. Both formats store the same `state_dict` though, and are interchangeable.
+
+The `state_dict` only contains the parameters of the adapter module, not the base model. To illustrate the difference in size, a normal BERT model requires ~420MB of disk space, whereas an IA³ adapter on top of this BERT model only requires ~260KB.
+
+2. `adapter_config.json`
+
+The `adapter_config.json` file contains the configuration of the adapter module, which is necessary to load the model. Below is an example of an `adapter_config.json` for an IA³ adapter with standard settings applied to a BERT model:
+
+```json
+{
+  "auto_mapping": {
+    "base_model_class": "BertModel",
+    "parent_library": "transformers.models.bert.modeling_bert"
+  },
+  "base_model_name_or_path": "bert-base-uncased",
+  "fan_in_fan_out": false,
+  "feedforward_modules": [
+    "output.dense"
+  ],
+  "inference_mode": true,
+  "init_ia3_weights": true,
+  "modules_to_save": null,
+  "peft_type": "IA3",
+  "revision": null,
+  "target_modules": [
+    "key",
+    "value",
+    "output.dense"
+  ],
+  "task_type": null
+}
+```
+
+The configuration file contains:
+
+- the adapter module type stored, `"peft_type": "IA3"`
+- information about the base model like `"base_model_name_or_path": "bert-base-uncased"`
+- the revision of the model (if any), `"revision": null`
+
+If the base model is not a pretrained Transformers model, the latter two entries will be `null`. Other than that, the settings are all related to the specific IA³ adapter that was used to fine-tune the model.
+
+3. `README.md`
+
+The generated `README.md` is the model card of a PEFT model and contains a few pre-filled entries. The intent of this is to make it easier to share the model with others and to provide some basic information about the model. This file is not needed to load the model.
+
+## Convert to PEFT format
+
+When converting from another format to the PEFT format, we require both the `adapter_model.safetensors` (or `adapter_model.bin`) file and the `adapter_config.json` file.
+
+### adapter_model
+
+For the model weights, it is important to use the correct mapping from parameter name to value for PEFT to load the file. Getting this mapping right is an exercise in checking the implementation details, as there is no generally agreed upon format for PEFT adapters.
+
+Fortunately, figuring out this mapping is not overly complicated for common base cases. Let's look at a concrete example, the [`LoraLayer`](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py):
+
+```python
+# showing only part of the code
+
+class LoraLayer(BaseTunerLayer):
+    # All names of layers that may contain (trainable) adapter weights
+    adapter_layer_names = ("lora_A", "lora_B", "lora_embedding_A", "lora_embedding_B")
+    # All names of other parameters that may contain adapter-related parameters
+    other_param_names = ("r", "lora_alpha", "scaling", "lora_dropout")
+
+    def __init__(self, base_layer: nn.Module, **kwargs) -> None:
+        self.base_layer = base_layer
+        self.r = {}
+        self.lora_alpha = {}
+        self.scaling = {}
+        self.lora_dropout = nn.ModuleDict({})
+        self.lora_A = nn.ModuleDict({})
+        self.lora_B = nn.ModuleDict({})
+        # For Embedding layer
+        self.lora_embedding_A = nn.ParameterDict({})
+        self.lora_embedding_B = nn.ParameterDict({})
+        # Mark the weight as unmerged
+        self._disable_adapters = False
+        self.merged_adapters = []
+        self.use_dora: dict[str, bool] = {}
+        self.lora_magnitude_vector: Optional[torch.nn.ParameterDict] = None  # for DoRA
+        self._caches: dict[str, Any] = {}
+        self.kwargs = kwargs
+```
+
+In the `__init__` code used by all `LoraLayer` classes in PEFT, there are a bunch of parameters used to initialize the model, but only a few are relevant for the checkpoint file: `lora_A`, `lora_B`, `lora_embedding_A`, and `lora_embedding_B`. These parameters are listed in the class attribute `adapter_layer_names` and contain the learnable parameters, so they must be included in the checkpoint file. All the other parameters, like the rank `r`, are derived from the `adapter_config.json` and must be included there (unless the default value is used).
+
+Let's check the `state_dict` of a PEFT LoRA model applied to BERT. When printing the first five keys using the default LoRA settings (the remaining keys are the same, just with different layer numbers), we get:
+
+- `base_model.model.encoder.layer.0.attention.self.query.lora_A.weight` 
+- `base_model.model.encoder.layer.0.attention.self.query.lora_B.weight` 
+- `base_model.model.encoder.layer.0.attention.self.value.lora_A.weight` 
+- `base_model.model.encoder.layer.0.attention.self.value.lora_B.weight` 
+- `base_model.model.encoder.layer.1.attention.self.query.lora_A.weight`
+- etc.
+
+Let's break this down:
+
+- By default, for BERT models, LoRA is applied to the `query` and `value` layers of the attention module. This is why you see `attention.self.query` and `attention.self.value` in the key names for each layer.
+- LoRA decomposes the weights into two low-rank matrices, `lora_A` and `lora_B`. This is where `lora_A` and `lora_B` come from in the key names.
+- These LoRA matrices are implemented as `nn.Linear` layers, so the parameters are stored in the `.weight` attribute (`lora_A.weight`, `lora_B.weight`).
+- By default, LoRA isn't applied to BERT's embedding layer, so there are _no entries_ for `lora_A_embedding` and `lora_B_embedding`.
+- The keys of the `state_dict` always start with `"base_model.model."`. The reason is that, in PEFT, we wrap the base model inside a tuner-specific model (`LoraModel` in this case), which itself is wrapped in a general PEFT model (`PeftModel`). For this reason, these two prefixes are added to the keys. When converting to the PEFT format, it is required to add these prefixes.
+
+> [!TIP]
+> This last point is not true for prefix tuning techniques like prompt tuning. There, the extra embeddings are directly stored in the `state_dict` without any prefixes added to the keys.
+
+When inspecting the parameter names in the loaded model, you might be surprised to find that they look a bit different, e.g. `base_model.model.encoder.layer.0.attention.self.query.lora_A.default.weight`. The difference is the *`.default`* part in the second to last segment. This part exists because PEFT generally allows the addition of multiple adapters at once (using an `nn.ModuleDict` or `nn.ParameterDict` to store them). For example, if you add another adapter called "other", the key for that adapter would be `base_model.model.encoder.layer.0.attention.self.query.lora_A.other.weight`.
+
+When you call [`~PeftModel.save_pretrained`], the adapter name is stripped from the keys. The reason is that the adapter name is not an important part of the model architecture; it is just an arbitrary name. When loading the adapter, you could choose a totally different name, and the model would still work the same way. This is why the adapter name is not stored in the checkpoint file.
+
+> [!TIP]
+> If you call `save_pretrained("some/path")` and the adapter name is not `"default"`, the adapter is stored in a sub-directory with the same name as the adapter. So if the name is "other", it would be stored inside of `some/path/other`.
+
+In some circumstances, deciding which values to add to the checkpoint file can become a bit more complicated. For example, in PEFT, DoRA is implemented as a special case of LoRA. If you want to convert a DoRA model to PEFT, you should create a LoRA checkpoint with extra entries for DoRA. You can see this in the `__init__` of the previous `LoraLayer` code:
+
+```python
+self.lora_magnitude_vector: Optional[torch.nn.ParameterDict] = None  # for DoRA
+```
+
+This indicates that there is an optional extra parameter per layer for DoRA.
+
+### adapter_config
+
+All the other information needed to load a PEFT model is contained in the `adapter_config.json` file. Let's check this file for a LoRA model applied to BERT:
+
+```json
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "BertModel",
+    "parent_library": "transformers.models.bert.modeling_bert"
+  },
+  "base_model_name_or_path": "bert-base-uncased",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 8,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "query",
+    "value"
+  ],
+  "task_type": null,
+  "use_dora": false,
+  "use_rslora": false
+}
+```
+
+This contains a lot of entries, and at first glance, it could feel overwhelming to figure out all the right values to put in there. However, most of the entries are not necessary to load the model. This is either because they use the default values and don't need to be added or because they only affect the initialization of the LoRA weights, which is irrelevant when it comes to loading the model. If you find that you don't know what a specific parameter does, e.g., `"use_rslora",` don't add it, and you should be fine. Also note that as more options are added, this file will get more entries in the future, but it should be backward compatible.
+
+At the minimum, you should include the following entries:
+
+```json
+{
+  "target_modules": ["query", "value"],
+  "peft_type": "LORA"
+}
+```
+
+However, adding as many entries as possible, like the rank `r` or the `base_model_name_or_path` (if it's a Transformers model) is recommended. This information can help others understand the model better and share it more easily. To check which keys and values are expected, check out the [config.py](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/config.py) file (as an example, this is the config file for LoRA) in the PEFT source code.
+
+## Model storage
+
+In some circumstances, you might want to store the whole PEFT model, including the base weights. This can be necessary if, for instance, the base model is not available to the users trying to load the PEFT model. You can merge the weights first or convert it into a Transformer model.
+
+### Merge the weights
+
+The most straightforward way to store the whole PEFT model is to merge the adapter weights into the base weights:
+
+```python
+merged_model = model.merge_and_unload()
+merged_model.save_pretrained(...)
+```
+
+There are some disadvantages to this approach, though:
+
+- Once [`~LoraModel.merge_and_unload`] is called, you get a basic model without any PEFT-specific functionality. This means you can't use any of the PEFT-specific methods anymore.
+- You cannot unmerge the weights, load multiple adapters at once, disable the adapter, etc.
+- Not all PEFT methods support merging weights.
+- Some PEFT methods may generally allow merging, but not with specific settings (e.g. when using certain quantization techniques).
+- The whole model will be much larger than the PEFT model, as it will contain all the base weights as well.
+
+But inference with a merged model should be a bit faster.
+
+### Convert to a Transformers model
+
+Another way to save the whole model, assuming the base model is a Transformers model, is to use this hacky approach to directly insert the PEFT weights into the base model and save it, which only works if you "trick" Transformers into believing the PEFT model is not a PEFT model. This only works with LoRA because other adapters are not implemented in Transformers.
+
+```python
+model = ...  # the PEFT model
+...
+# after you finish training the model, save it in a temporary location
+model.save_pretrained(<temp_location>)
+# now load this model directly into a transformers model, without the PEFT wrapper
+# the PEFT weights are directly injected into the base model
+model_loaded = AutoModel.from_pretrained(<temp_location>)
+# now make the loaded model believe that it is _not_ a PEFT model
+model_loaded._hf_peft_config_loaded = False
+# now when we save it, it will save the whole model
+model_loaded.save_pretrained(<final_location>)
+# or upload to Hugging Face Hub
+model_loaded.push_to_hub(<final_location>)
+```
+

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/contributing.md

@@ -0,0 +1,123 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Contribute to PEFT
+
+We are happy to accept contributions to PEFT. If you plan to contribute, please read this to make the process as smooth as possible.
+
+## Installation
+
+For code contributions to PEFT, you should choose the ["source"](../install#source) installation method.
+
+If you are new to creating a pull request, follow the [Creating a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request) guide by GitHub.
+
+## Tests and code quality checks
+
+Regardless of the contribution type (unless it’s only about the docs), you should run tests and code quality checks before creating a PR to ensure your contribution doesn’t break anything and follows the project standards.
+
+We provide a Makefile to execute the necessary tests. Run the code below for the unit test:
+
+```sh
+make test
+```
+
+Run one of the following to either only check or check and fix code quality and style:
+
+```sh
+make quality  # just check
+make style  # check and fix
+```
+
+You can also set up [`pre-commit`](https://pre-commit.com/) to run these fixes
+automatically as Git commit hooks.
+
+```bash
+$ pip install pre-commit
+$ pre-commit install
+```
+
+Running all the tests can take a while, so during development it can be more efficient to only [run tests specific to your change](https://docs.pytest.org/en/6.2.x/usage.html#specifying-tests-selecting-tests), e.g. via:
+
+```sh
+pytest tests/<test-file-name> -k <name-of-test>
+```
+
+This should finish much quicker and allow for faster iteration.
+
+If your change is specific to a hardware setting (e.g., it requires CUDA), take a look at [`tests/test_gpu_examples.py`](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/tests/test_gpu_examples.py) and [`tests/test_common_gpu.py`](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/tests/test_common_gpu.py) to see if it makes sense to add tests there. If your change could have an effect on saving and loading models, please run the tests with the `--regression` flag to trigger regression tests.
+
+It can happen that while you’re working on your PR, the underlying code base changes due to other changes being merged. If that happens – especially when there is a merge conflict – please update your branch with the latest changes. This can be a merge or a rebase, and we'll squash and merge the PR once it’s ready. If possible, **avoid force pushes** to make reviews easier.
+
+## PR description
+
+When opening a PR, please provide a nice description of the change you're proposing. If it relates to other issues or PRs, please reference them. Providing a good description not only helps the reviewers review your code better and faster, it can also be used later (as a basis) for the commit message which helps with long term maintenance of the project.
+
+If your code makes some non-trivial changes, it may also be a good idea to add comments to the code to explain those changes. For example, if you had to iterate on your implementation multiple times because the most obvious way didn’t work, it’s a good indication that a code comment is needed.
+
+## Bugfixes
+
+Please give a description of the circumstances that led to the bug. If there is an existing issue, please link to it (e.g., “Resolves #12345”).
+
+Ideally when a bugfix is provided, it should be accompanied by a test for the bug. The test should fail with the current code and pass with the bugfix. Add a comment to the test that references the issue or PR. Without a test, it is more difficult to prevent regressions in the future.
+
+## Documentation improvements
+
+We are happy to have fixes for broken links and missing or unclear documentation. Taking care of examples, making sure that they are up-to-date and running fine in this fast moving environment is also highly appreciated.
+
+Please refrain from sending pull requests that *only* correct typing errors as these generally create more work than they safe. Such changes are better combined with more substantial fixes (such as fixing broken links or extending/updating documentation).
+
+## Add a new PEFT fine-tuning method
+
+New parameter-efficient fine-tuning methods are developed all the time. If you would like to add a new and promising method to PEFT, please follow these steps.
+
+1. If you're _not_ an author of the original paper, check for existing implementations and double check with the authors that they don't plan to submit a PR themselves.
+2. Start with the core integration work listed below.
+3. Check recent commits for new PEFT methods being added to take as inspiration.
+4. It can be useful to open a draft PR early once the method basically works and first tests pass, then ask for feedback.
+5. After working through reviewer feedback, ping the reviewer so that they know the PR is ready to review.
+
+### Core integration of a new PEFT method
+
+- [ ] Open a proposal issue on `huggingface/peft` before investing too much work.
+- [ ] Link the source of the method, usually the final paper or another stable primary reference. We want to avoid work that is still under review, as the implementation should be stable.
+- [ ] Add a new `PeftType` entry in `src/peft/utils/peft_types.py`.
+- [ ] Create a new tuner package under `src/peft/tuners/` with the files your method needs (typically:  `config.py`, `model.py`, `layer.py`, and `__init__.py`).
+- [ ] Register the method in the tuner `__init__.py` with `register_peft_method(...)`.
+- [ ] Export the new config/model from `src/peft/tuners/__init__.py` and `src/peft/__init__.py`.
+- [ ] If the method needs default target modules for Transformers models, add the mapping in `src/peft/utils/constants.py`.
+- [ ] Add the method to the test matrix in `tests/test_custom_models.py` as these are the broadest and quickest tests. Check that the tests pass with `pytest tests/test_custom_models.py -k <method-name> -v`, fix failures if any.
+- [ ] Run style/quality checks with `make style` before pushing.
+- [ ] In the PR description, explain the method, link the paper, summarize tradeoffs, and list what was added.
+
+### Full PR to add a new PEFT method
+
+- [ ] Ensure that the configuration arguments that are specific to the method are well named and explained, don't assume that the user knows the paper inside out.
+- [ ] Follow the naming and coding conventions of PEFT.
+- [ ] Ensure that you didn't accidentally check in unrelated changes, e.g. the code formatter changing unrelated files.
+- [ ] If some implementation choices are non-trivial, document them with a code comment.
+- [ ] Complete the full test suite (`test_config.py`, `test_decoder_models.py`, etc.) by adding the PEFT method to the test matrix. Ensure that the tests pass.
+- [ ] Add docs in `docs/source/package_reference/` with a short explanation, paper link, usage snippet, and autodoc blocks. Explain the pros and cons compared to other methods like LoRA. Register that doc page in `docs/source/_toctree.yml`.
+- [ ] Add a runnable example under `examples/` (can be a copy of an existing example), with a short `README.md`.
+- [ ] Check the benchmarks in `method_comparison/` and add experiment settings for your new method. This is a good place to sanity check that the PEFT method trains as expected. Include one or two reasonable benchmark configurations (one default, one optimized for the benchmark).
+- [ ] Recommended: Add generic quantization support. Instead of having to explicitly add quantization layer types for each quantization method, support generic quantization. As an example, check how it's implemented in [BOFT](https://github.com/huggingface/peft/tree/main/src/peft/tuners/boft). Extend https://github.com/huggingface/peft/blob/main/tests/test_quantization.py by adding your PEFT method there. Ask maintainers for help if needed.
+
+## Add other features
+
+It is best if you first open an issue on GitHub with a proposal to add the new feature. This way, you can discuss with the maintainers if it makes sense to add the feature before spending too much time on implementing it.
+
+New features should generally be accompanied by tests and documentation or examples. Without the latter, users will have a hard time discovering your cool new feature.
+
+Changes to the code should be implemented in a backward-compatible way. For example, existing code should continue to work the same way after the feature is merged.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/custom_models.md

@@ -0,0 +1,304 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Custom models
+
+Some fine-tuning techniques, such as prompt tuning, are specific to language models. That means in 🤗 PEFT, it is
+assumed a 🤗 Transformers model is being used. However, other fine-tuning techniques - like
+[LoRA](../conceptual_guides/lora) - are not restricted to specific model types.
+
+In this guide, we will see how LoRA can be applied to a multilayer perceptron, a computer vision model from the [timm](https://huggingface.co/docs/timm/index) library, or a new 🤗 Transformers architecture.
+
+## Multilayer perceptron
+
+Let's assume that we want to fine-tune a multilayer perceptron with LoRA. Here is the definition:
+
+```python
+from torch import nn
+
+
+class MLP(nn.Module):
+    def __init__(self, num_units_hidden=2000):
+        super().__init__()
+        self.seq = nn.Sequential(
+            nn.Linear(20, num_units_hidden),
+            nn.ReLU(),
+            nn.Linear(num_units_hidden, num_units_hidden),
+            nn.ReLU(),
+            nn.Linear(num_units_hidden, 2),
+            nn.LogSoftmax(dim=-1),
+        )
+
+    def forward(self, X):
+        return self.seq(X)
+```
+
+This is a straightforward multilayer perceptron with an input layer, a hidden layer, and an output layer.
+
+> [!TIP]
+> For this toy example, we choose an exceedingly large number of hidden units to highlight the efficiency gains
+> from PEFT, but those gains are in line with more realistic examples.
+
+There are a few linear layers in this model that could be tuned with LoRA. When working with common 🤗 Transformers
+models, PEFT will know which layers to apply LoRA to, but in this case, it is up to us as a user to choose the layers.
+To determine the names of the layers to tune:
+
+```python
+print([(n, type(m)) for n, m in MLP().named_modules()])
+```
+
+This should print:
+
+```
+[('', __main__.MLP),
+ ('seq', torch.nn.modules.container.Sequential),
+ ('seq.0', torch.nn.modules.linear.Linear),
+ ('seq.1', torch.nn.modules.activation.ReLU),
+ ('seq.2', torch.nn.modules.linear.Linear),
+ ('seq.3', torch.nn.modules.activation.ReLU),
+ ('seq.4', torch.nn.modules.linear.Linear),
+ ('seq.5', torch.nn.modules.activation.LogSoftmax)]
+```
+
+Let's say we want to apply LoRA to the input layer and to the hidden layer, those are `'seq.0'` and `'seq.2'`. Moreover,
+let's assume we want to update the output layer without LoRA, that would be `'seq.4'`. The corresponding config would
+be:
+
+```python
+from peft import LoraConfig
+
+config = LoraConfig(
+    target_modules=["seq.0", "seq.2"],
+    modules_to_save=["seq.4"],
+)
+```
+
+With that, we can create our PEFT model and check the fraction of parameters trained:
+
+```python
+from peft import get_peft_model
+
+model = MLP()
+peft_model = get_peft_model(model, config)
+peft_model.print_trainable_parameters()
+# prints trainable params: 56,164 || all params: 4,100,164 || trainable%: 1.369798866581922
+```
+
+Finally, we can use any training framework we like, or write our own fit loop, to train the `peft_model`.
+
+For a complete example, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/multilayer_perceptron/multilayer_perceptron_lora.ipynb).
+
+## timm models
+
+The [timm](https://huggingface.co/docs/timm/index) library contains a large number of pretrained computer vision models.
+Those can also be fine-tuned with PEFT. Let's check out how this works in practice.
+
+To start, ensure that timm is installed in the Python environment:
+
+```bash
+python -m pip install -U timm
+```
+
+Next we load a timm model for an image classification task:
+
+```python
+import timm
+
+num_classes = ...
+model_id = "timm/poolformer_m36.sail_in1k"
+model = timm.create_model(model_id, pretrained=True, num_classes=num_classes)
+```
+
+Again, we need to make a decision about what layers to apply LoRA to. Since LoRA supports 2D conv layers, and since
+those are a major building block of this model, we should apply LoRA to the 2D conv layers. To identify the names of
+those layers, let's look at all the layer names:
+
+```python
+print([(n, type(m)) for n, m in model.named_modules()])
+```
+
+This will print a very long list, we'll only show the first few:
+
+```
+[('', timm.models.metaformer.MetaFormer),
+ ('stem', timm.models.metaformer.Stem),
+ ('stem.conv', torch.nn.modules.conv.Conv2d),
+ ('stem.norm', torch.nn.modules.linear.Identity),
+ ('stages', torch.nn.modules.container.Sequential),
+ ('stages.0', timm.models.metaformer.MetaFormerStage),
+ ('stages.0.downsample', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks', torch.nn.modules.container.Sequential),
+ ('stages.0.blocks.0', timm.models.metaformer.MetaFormerBlock),
+ ('stages.0.blocks.0.norm1', timm.layers.norm.GroupNorm1),
+ ('stages.0.blocks.0.token_mixer', timm.models.metaformer.Pooling),
+ ('stages.0.blocks.0.token_mixer.pool', torch.nn.modules.pooling.AvgPool2d),
+ ('stages.0.blocks.0.drop_path1', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks.0.layer_scale1', timm.models.metaformer.Scale),
+ ('stages.0.blocks.0.res_scale1', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks.0.norm2', timm.layers.norm.GroupNorm1),
+ ('stages.0.blocks.0.mlp', timm.layers.mlp.Mlp),
+ ('stages.0.blocks.0.mlp.fc1', torch.nn.modules.conv.Conv2d),
+ ('stages.0.blocks.0.mlp.act', torch.nn.modules.activation.GELU),
+ ('stages.0.blocks.0.mlp.drop1', torch.nn.modules.dropout.Dropout),
+ ('stages.0.blocks.0.mlp.norm', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks.0.mlp.fc2', torch.nn.modules.conv.Conv2d),
+ ('stages.0.blocks.0.mlp.drop2', torch.nn.modules.dropout.Dropout),
+ ('stages.0.blocks.0.drop_path2', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks.0.layer_scale2', timm.models.metaformer.Scale),
+ ('stages.0.blocks.0.res_scale2', torch.nn.modules.linear.Identity),
+ ('stages.0.blocks.1', timm.models.metaformer.MetaFormerBlock),
+ ('stages.0.blocks.1.norm1', timm.layers.norm.GroupNorm1),
+ ('stages.0.blocks.1.token_mixer', timm.models.metaformer.Pooling),
+ ('stages.0.blocks.1.token_mixer.pool', torch.nn.modules.pooling.AvgPool2d),
+ ...
+ ('head.global_pool.flatten', torch.nn.modules.linear.Identity),
+ ('head.norm', timm.layers.norm.LayerNorm2d),
+ ('head.flatten', torch.nn.modules.flatten.Flatten),
+ ('head.drop', torch.nn.modules.linear.Identity),
+ ('head.fc', torch.nn.modules.linear.Linear)]
+ ]
+```
+
+Upon closer inspection, we see that the 2D conv layers have names such as `"stages.0.blocks.0.mlp.fc1"` and
+`"stages.0.blocks.0.mlp.fc2"`. How can we match those layer names specifically? You can write a [regular
+expressions](https://docs.python.org/3/library/re.html) to match the layer names. For our case, the regex
+`r".*\.mlp\.fc\d"` should do the job.
+
+Furthermore, as in the first example, we should ensure that the output layer, in this case the classification head, is
+also updated. Looking at the end of the list printed above, we can see that it's named `'head.fc'`. With that in mind,
+here is our LoRA config:
+
+```python
+config = LoraConfig(target_modules=r".*\.mlp\.fc\d", modules_to_save=["head.fc"])
+```
+
+Then we only need to create the PEFT model by passing our base model and the config to `get_peft_model`:
+
+```python
+peft_model = get_peft_model(model, config)
+peft_model.print_trainable_parameters()
+# prints trainable params: 1,064,454 || all params: 56,467,974 || trainable%: 1.88505789139876
+```
+
+This shows us that we only need to train less than 2% of all parameters, which is a huge efficiency gain.
+
+For a complete example, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/image_classification/image_classification_timm_peft_lora.ipynb).
+
+## New transformers architectures
+
+When new popular transformers architectures are released, we do our best to quickly add them to PEFT. If you come across a transformers model that is not supported out of the box, don't worry, it will most likely still work if the config is set correctly. Specifically, you have to identify the layers that should be adapted and set them correctly when initializing the corresponding config class, e.g. `LoraConfig`. Here are some tips to help with this.
+
+As a first step, it is a good idea to check the existing models for inspiration. You can find them inside of [constants.py](https://github.com/huggingface/peft/blob/main/src/peft/utils/constants.py) in the PEFT repository. Often, you'll find a similar architecture that uses the same names. For example, if the new model architecture is a variation of the "mistral" model and you want to apply LoRA, you can see that the entry for "mistral" in `TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING` contains `["q_proj", "v_proj"]`. This tells you that for "mistral" models, the `target_modules` for LoRA should be `["q_proj", "v_proj"]`:
+
+```python
+from peft import LoraConfig, get_peft_model
+
+my_mistral_model = ...
+config = LoraConfig(
+    target_modules=["q_proj", "v_proj"],
+    ...,  # other LoRA arguments
+)
+peft_model = get_peft_model(my_mistral_model, config)
+```
+
+If that doesn't help, check the existing modules in your model architecture with the `named_modules` method and try to identify the attention layers, especially the key, query, and value layers. Those will often have names such as `c_attn`, `query`, `q_proj`, etc. The key layer is not always adapted, and ideally, you should check whether including it results in better performance.
+
+Additionally, linear layers are common targets to be adapted (e.g. in [QLoRA paper](https://huggingface.co/papers/2305.14314), authors suggest to adapt them as well). Their names will often contain the strings `fc` or `dense`.
+
+If you want to add a new model to PEFT, please create an entry in [constants.py](https://github.com/huggingface/peft/blob/main/src/peft/utils/constants.py) and open a pull request on the [repository](https://github.com/huggingface/peft/pulls). Don't forget to update the [README](https://github.com/huggingface/peft#models-support-matrix) as well.
+
+## Verify parameters and layers
+
+You can verify whether you've correctly applied a PEFT method to your model in a few ways.
+
+* Check the fraction of parameters that are trainable with the [`~PeftModel.print_trainable_parameters`] method. If this number is lower or higher than expected, check the model `repr` by printing the model. This shows the names of all the layer types in the model. Ensure that only the intended target layers are replaced by the adapter layers. For example, if LoRA is applied to `nn.Linear` layers, then you should only see `lora.Linear` layers being used.
+
+```py
+peft_model.print_trainable_parameters()
+```
+
+* Another way you can view the adapted layers is to use the `targeted_module_names` attribute to list the name of each module that was adapted.
+
+```python
+print(peft_model.targeted_module_names)
+```
+
+## Unsupported module types
+
+Methods like LoRA only work if the target modules are supported by PEFT. For example, it's possible to apply LoRA to `nn.Linear` and `nn.Conv2d` layers, but not, for instance, to `nn.LSTM`. If you find a layer class you want to apply PEFT to is not supported, you can:
+
+ - define a custom mapping to dynamically dispatch custom modules in LoRA
+ -  open an [issue](https://github.com/huggingface/peft/issues) and request the feature where maintainers will implement it or guide you on how to implement it yourself if demand for this module type is sufficiently high
+
+### Experimental support for dynamic dispatch of custom modules in LoRA
+
+> [!WARNING]
+> This feature is experimental and subject to change, depending on its reception by the community. We will introduce a public and stable API if there is significant demand for it.
+
+PEFT supports an experimental API for custom module types for LoRA. Let's assume you have a LoRA implementation for LSTMs. Normally, you would not be able to tell PEFT to use it, even if it would theoretically work with PEFT. However, this is possible with dynamic dispatch of custom layers.
+
+The experimental API currently looks like this:
+
+```python
+class MyLoraLSTMLayer:
+    ...
+
+base_model = ...  # load the base model that uses LSTMs
+
+# add the LSTM layer names to target_modules
+config = LoraConfig(..., target_modules=["lstm"])
+# define a mapping from base layer type to LoRA layer type
+custom_module_mapping = {nn.LSTM: MyLoraLSTMLayer}
+# register the new mapping
+config._register_custom_module(custom_module_mapping)
+# after registration, create the PEFT model
+peft_model = get_peft_model(base_model, config)
+# do training
+```
+
+> [!TIP]
+> When you call [`get_peft_model`], you will see a warning because PEFT does not recognize the targeted module type. In this case, you can ignore this warning.
+
+By supplying a custom mapping, PEFT first checks the base model's layers against the custom mapping and dispatches to the custom LoRA layer type if there is a match. If there is no match, PEFT checks the built-in LoRA layer types for a match.
+
+Therefore, this feature can also be used to override existing dispatch logic, e.g. if you want to use your own LoRA layer for `nn.Linear` instead of using the one provided by PEFT.
+
+When creating your custom LoRA module, please follow the same rules as the [existing LoRA modules](https://github.com/huggingface/peft/blob/main/src/peft/tuners/lora/layer.py). Some important constraints to consider:
+
+- The custom module should inherit from `nn.Module` and `peft.tuners.lora.layer.LoraLayer`.
+- The `__init__` method of the custom module should have the positional arguments `base_layer` and `adapter_name`. After this, there are additional `**kwargs` that you are free to use or ignore.
+- The learnable parameters should be stored in an `nn.ModuleDict` or `nn.ParameterDict`, where the key corresponds to the name of the specific adapter (remember that a model can have more than one adapter at a time).
+- The name of these learnable parameter attributes should start with `"lora_"`, e.g. `self.lora_new_param = ...`.
+- Some methods are optional, e.g. you only need to implement `merge` and `unmerge` if you want to support weight merging.
+
+Currently, the information about the custom module does not persist when you save the model. When loading the model, you have to register the custom modules again.
+
+```python
+# saving works as always and includes the parameters of the custom modules
+peft_model.save_pretrained(<model-path>)
+
+# loading the model later:
+base_model = ...
+# load the LoRA config that you saved earlier
+config = LoraConfig.from_pretrained(<model-path>)
+# register the custom module again, the same way as the first time
+custom_module_mapping = {nn.LSTM: MyLoraLSTMLayer}
+config._register_custom_module(custom_module_mapping)
+# pass the config instance to from_pretrained:
+peft_model = PeftModel.from_pretrained(model, tmp_path / "lora-custom-module", config=config)
+```
+
+If you use this feature and find it useful, or if you encounter problems, let us know by creating an issue or a discussion on GitHub. This allows us to estimate the demand for this feature and add a public API if it is sufficiently high.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/lora.md

@@ -0,0 +1,1126 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# LoRA
+
+LoRA is low-rank decomposition method to reduce the number of trainable parameters which speeds up finetuning large models and uses less memory. In PEFT, using LoRA is as easy as setting up a [`LoraConfig`] and wrapping it with [`get_peft_model`] to create a trainable [`PeftModel`].
+
+This guide explores in more detail other options and features for using LoRA.
+
+## Initialization
+
+The initialization of LoRA weights is controlled by the parameter `init_lora_weights` in [`LoraConfig`]. By default, PEFT initializes LoRA weights with Kaiming-uniform for weight A and zeros for weight B resulting in an identity transform (same as the reference [implementation](https://github.com/microsoft/LoRA)).
+
+It is also possible to pass `init_lora_weights="gaussian"`. As the name suggests, this initializes weight A with a Gaussian distribution and zeros for weight B (this is how [Diffusers](https://huggingface.co/docs/diffusers/index) initializes LoRA weights).
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(init_lora_weights="gaussian", ...)
+```
+
+There is also an option to set `init_lora_weights=False` which is useful for debugging and testing. This should be the only time you use this option. When choosing this option, the LoRA weights are initialized such that they do *not* result in an identity transform.
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(init_lora_weights=False, ...)
+```
+
+### PiSSA
+[PiSSA](https://huggingface.co/papers/2404.02948) initializes the LoRA adapter using the principal singular values and singular vectors. This straightforward modification allows PiSSA to converge more rapidly than LoRA and ultimately attain superior performance. Moreover, PiSSA reduces the quantization error compared to QLoRA, leading to further enhancements.
+
+Configure the initialization method to "pissa", which may take several minutes to execute SVD on the pre-trained model:
+```python
+from peft import LoraConfig
+config = LoraConfig(init_lora_weights="pissa", ...)
+```
+Alternatively, execute fast SVD, which takes only a few seconds. The number of iterations determines the trade-off between the error and computation time:
+```python
+lora_config = LoraConfig(init_lora_weights="pissa_niter_[number of iters]", ...)
+```
+For detailed instruction on using PiSSA, please follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/pissa_finetuning).
+
+### CorDA
+
+[CorDA](https://huggingface.co/papers/2406.05223) builds task-aware LoRA adapters from weight decomposition oriented by the context of downstream task to learn (instruction-previewed mode, IPM) or world knowledge to maintain (knowledge-preserved mode, KPM).
+The KPM not only achieves better performance than LoRA on fine-tuning tasks, but also mitigates the catastrophic forgetting of pre-trained world knowledge.
+When preserving pre-trained knowledge is not a concern,
+the IPM is favored because it can further accelerate convergence and enhance the fine-tuning performance.
+
+You need to configure the initialization method to "corda", and specify the mode of IPM or KPM and the dataset to collect covariance matrices.
+
+```py
+@torch.no_grad()
+def run_model():
+    # Assume `model` and `dataset` is in context...
+    model.eval()
+    for batch in dataset:
+        model(**batch)
+
+
+corda_config = CordaConfig(
+    corda_method="kpm",
+)
+lora_config = LoraConfig(
+    init_lora_weights="corda",
+    corda_config=corda_config,
+)
+preprocess_corda(model, lora_config, run_model=run_model)
+peft_model = get_peft_model(model, lora_config)
+```
+
+For detailed instruction on using CorDA, please follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/corda_finetuning).
+
+### OLoRA
+[OLoRA](https://huggingface.co/papers/2406.01775) utilizes QR decomposition to initialize the LoRA adapters. OLoRA translates the base weights of the model by a factor of their QR decompositions, i.e., it mutates the weights before performing any training on them. This approach significantly improves stability, accelerates convergence speed, and ultimately achieves superior performance.
+
+You just need to pass a single additional option to use OLoRA:
+```python
+from peft import LoraConfig
+config = LoraConfig(init_lora_weights="olora", ...)
+```
+For more advanced usage, please refer to our [documentation](https://github.com/huggingface/peft/tree/main/examples/olora_finetuning).
+
+### EVA
+[EVA](https://huggingface.co/papers/2410.07170) performs SVD on the input activations of each layer and uses the right-singular vectors to initialize LoRA weights. It is therefore a data-driven initialization scheme. Furthermore EVA adaptively allocates ranks across layers based on their "explained variance ratio" - a metric derived from the SVD analysis.
+
+You can use EVA by setting `init_lora_weights="eva"` and defining [`EvaConfig`] in [`LoraConfig`]:
+```python
+from peft import LoraConfig, EvaConfig
+peft_config = LoraConfig(
+    init_lora_weights = "eva",
+    eva_config = EvaConfig(rho = 2.0),
+    ...
+)
+```
+The parameter `rho` (≥ 1.0) determines how much redistribution is allowed. When `rho=1.0` and `r=16`, LoRA adapters are limited to exactly 16 ranks, preventing any redistribution from occurring. A recommended value for EVA with redistribution is 2.0, meaning the maximum rank allowed for a layer is 2r.
+
+It is recommended to perform EVA initialization on an accelerator(e.g. CUDA GPU, Intel XPU) as it is much faster. To optimize the amount of available memory for EVA, you can use the `low_cpu_mem_usage` flag in [`get_peft_model`]:
+```python
+peft_model = get_peft_model(model, peft_config, low_cpu_mem_usage=True)
+```
+Then, call [`initialize_lora_eva_weights`] to initialize the EVA weights (in most cases the dataloader used for eva initialization can be the same as the one used for finetuning):
+```python
+initialize_lora_eva_weights(peft_model, dataloader)
+```
+EVA works out of the box with bitsandbytes. Simply initialize the model with `quantization_config` and call [`initialize_lora_eva_weights`] as usual.
+
+> [!TIP]
+> For further instructions on using EVA, please refer to our [documentation](https://github.com/huggingface/peft/tree/main/examples/eva_finetuning).
+
+### LoftQ
+
+#### Standard approach
+
+When quantizing the base model for QLoRA training, consider using the [LoftQ initialization](https://huggingface.co/papers/2310.08659), which has been shown to improve performance when training quantized models. The idea is that the LoRA weights are initialized such that the quantization error is minimized. To use LoftQ, follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning).
+
+> [!TIP]
+> Learn more about how PEFT works with quantization and how to use LoftQ in the [Quantization](quantization) guide.
+
+### Rank-stabilized LoRA
+
+Another way to initialize [`LoraConfig`] is with the [rank-stabilized LoRA (rsLoRA)](https://huggingface.co/papers/2312.03732) method. The LoRA architecture scales each adapter during every forward pass by a fixed scalar which is set at initialization and depends on the rank `r`. The scalar is given by `lora_alpha/r` in the original implementation, but rsLoRA uses `lora_alpha/math.sqrt(r)` which stabilizes the adapters and increases the performance potential from using a higher `r`.
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(use_rslora=True, ...)
+```
+
+### LoRA-GA
+
+[LoRA-GA](../package_reference/lora#lora-ga) (Low-Rank Adaptation with Gradient Approximation) initializes the adapter
+weights by performing SVD on estimated gradients, so that the weights are aligning closer to full-finetuning for faster
+convergence.
+
+This method requires an initialization function to estimate the gradients
+before beginning the actual training:
+
+```python
+from peft.tuners.lora import preprocess_loraga
+
+def train_step():
+    """Run forward and backward passes for gradient estimation."""
+    dataloader_iter = iter(grad_dataloader)
+    for _ in range(N):
+        batch = next(dataloader_iter)
+        batch = {k: v.to(device) for k, v in batch.items()}
+        outputs = model(**batch)
+        loss = outputs.loss
+        loss.backward()
+
+preprocess_loraga(model, lora_config, train_step)
+```
+
+### KappaTuneSelector
+
+KappaTune implements the condition-number-based target selection strategy from the [KappaTune paper](https://arxiv.org/abs/2506.16289). It scans every `nn.Linear` module and, for models where MoE expert weights are stored as fused 3D `nn.Parameter` tensors (e.g. Llama-4, Qwen3-MoE), also those parameters, computes the matrix condition number κ = σ_max / σ_min for each, and selects the most isotropic layers (lowest κ). These isotropic layers serve as ideal candidates for fine-tuning, since their high-entropy nature allows them to absorb new information more readily, leaving the specialized, anisotropic layers intact to mitigate catastrophic forgetting during continual learning.
+
+Use `find_kappa_target_modules` as a one-liner to get the optimal `target_modules` for `LoraConfig`:
+
+```python
+from peft import LoraConfig, get_peft_model
+from peft.helpers import find_kappa_target_modules
+
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x7B-Instruct-v0.1")
+
+targets = find_kappa_target_modules(model, top_p=0.2)
+config = LoraConfig(
+    target_modules=targets["target_modules"],
+    target_parameters=targets["target_parameters"] if stable_modules_dic["target_parameters"] else None,
+    r=64,
+    lora_alpha=32,
+    task_type="CAUSAL_LM",
+)
+peft_model = get_peft_model(model, config)
+```
+
+See a complete example [here](https://github.com/huggingface/peft/blob/main/examples/KappaTune/experiments_kappatune_peft.py).
+
+## Variants
+
+PEFT implements LoRA variants that improve upon the original LoRA.
+
+### Weight-Decomposed Low-Rank Adaptation (DoRA)
+
+This technique decomposes the updates of the weights into two parts, magnitude and direction. Direction is handled by normal LoRA, whereas the magnitude is handled by a separate learnable parameter. This can improve the performance of LoRA, especially at low ranks. For more information on DoRA, see  https://huggingface.co/papers/2402.09353.
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(use_dora=True, ...)
+```
+
+If parts of the model or the DoRA adapter are offloaded to CPU you can get a significant speedup at the cost of some temporary (ephemeral) VRAM overhead by using `ephemeral_gpu_offload=True` in `config.runtime_config`.
+
+```py
+from peft import LoraConfig, LoraRuntimeConfig
+
+config = LoraConfig(use_dora=True, runtime_config=LoraRuntimeConfig(ephemeral_gpu_offload=True), ...)
+```
+
+A `PeftModel` with a DoRA adapter can also be loaded with `ephemeral_gpu_offload=True` flag using the `from_pretrained` method as well as the `load_adapter` method.
+
+```py
+from peft import PeftModel
+
+model = PeftModel.from_pretrained(base_model, peft_model_id, ephemeral_gpu_offload=True)
+```
+
+#### Optimization
+
+DoRA is optimized (computes faster and takes less memory) for models in the evaluation mode, or when dropout is set to 0. We reuse the
+base result at those times to get the speedup.
+Running [dora finetuning](https://github.com/huggingface/peft/blob/main/examples/dora_finetuning/dora_finetuning.py)
+with `CUDA_VISIBLE_DEVICES=0 ZE_AFFINITY_MASK=0 time python examples/dora_finetuning/dora_finetuning.py --quantize --lora_dropout 0 --batch_size 16 --eval_step 2 --use_dora` on a 4090 with gradient accumulation set to 2 and max step to 20 resulted with the following observations:
+
+| | Without Optimization | With Optimization |
+| :--: | :--: | :--: |
+| train runtime (sec) | 359.7298 | **279.2676** |
+| train samples per second | 1.779 | **2.292** |
+| train steps per second | 0.056 | **0.072** |
+
+Moreover, it is possible to further increase runtime performance of DoRA by using the [`DoraCaching`] helper context. This requires the model to be in `eval` mode:
+
+```py
+from peft.helpers import DoraCaching
+
+model.eval()
+with DoraCaching():
+    output = model(inputs)
+```
+
+For [`meta-llama/Llama-3.1-8B`](https://huggingface.co/meta-llama/Llama-3.1-8B), the [DoRA caching benchmark script](https://github.com/huggingface/peft/blob/main/examples/dora_finetuning/dora-caching.py) shows that, compared to LoRA:
+
+- DoRA without caching requires 139% more time
+- DoRA without caching requires 4% more memory
+- DoRA with caching requires 17% more time
+- DoRA with caching requires 41% more memory
+
+Caching can thus make inference with DoRA significantly faster but it also requires signficantly more memory. Ideally, if the use case allows it, just merge the DoRA adapter to avoid both memory and runtime overhead.
+
+#### Caveats
+
+- DoRA only supports embedding, linear, and Conv2d layers at the moment.
+- DoRA introduces a bigger overhead than pure LoRA, so it is recommended to merge weights for inference, see [`LoraModel.merge_and_unload`].
+- DoRA should work with weights quantized with bitsandbytes ("QDoRA"). However, issues have been reported when using QDoRA with DeepSpeed Zero2.
+
+### MonteCLoRA (Monte Carlo Low-Rank Adaptation)
+
+MonteCLoRA wraps a standard LoRA adapter with a small variational module that draws Monte Carlo samples of stochastic perturbations on top of the LoRA `A` matrix during training. Concretely, it learns variational parameters (a Wishart-based covariance, a per-sample multivariate-normal noise term, and a Dirichlet weighting over the samples) and adds the resulting averaged perturbation to `lora_A` at every forward pass. A KL-divergence + entropy term is added to the training loss to keep these variational parameters anchored to a sensible prior. At inference time the sampler is disabled and MonteCLoRA behaves exactly like a regular LoRA adapter, so there is **no extra inference cost or extra parameters to merge**. For the full method see https://huggingface.co/papers/2411.04358.
+
+You may want to consider MonteCLoRA when:
+
+- You are fine-tuning on a small or noisy dataset and want stronger regularization than vanilla LoRA. The Monte Carlo averaging and the KL term together act as a Bayesian-style regularizer.
+- You want better uncertainty calibration / robustness from your adapter without paying extra cost at inference time (the variational machinery is training-only).
+- Vanilla LoRA is overfitting and lowering `r` or increasing `lora_dropout` is not enough.
+
+You probably do *not* need MonteCLoRA when you have a large, clean dataset and vanilla LoRA already trains stably — in that regime the extra variational parameters mostly add training overhead without much benefit.
+
+To enable MonteCLoRA, pass a `MontecloraConfig` to `LoraConfig`:
+
+```py
+from peft import LoraConfig, MontecloraConfig
+
+monteclora_config = MontecloraConfig(
+    num_samples=8,         # number of Monte Carlo samples per forward pass
+    sample_scaler=1e-4,    # magnitude of the variational perturbation
+    kl_loss_weight=1e-5,   # weight of the KL term added to the training loss
+)
+config = LoraConfig(
+    r=16,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    monteclora_config=monteclora_config,
+)
+```
+
+During training you must add the variational regularization loss to the task loss. The simplest way is to call [`LoraModel._get_monteclora_loss`] on the underlying `LoraModel`:
+
+```py
+task_loss = ...  # standard loss returned by your model
+monteclora_loss = model._get_monteclora_loss()  # 0.0 if MonteCLoRA is not used
+total_loss = task_loss + monteclora_loss
+total_loss.backward()
+```
+
+If you train with the HF `Trainer`, you can simply mix in [`peft.helpers.MontecloraTrainerMixin`] which does this for you in `compute_loss`:
+
+```py
+from transformers import Trainer
+from peft.helpers import MontecloraTrainerMixin
+
+
+class MontecloraTrainer(MontecloraTrainerMixin, Trainer):
+    pass
+```
+
+A complete working example is available at [`examples/monteclora_finetuning`](https://github.com/huggingface/peft/tree/main/examples/monteclora_finetuning).
+
+### VeLoRA
+
+[VeLoRA](https://huggingface.co/papers/2405.17991) is a LoRA variant that reduces training memory by compressing the activations saved for the LoRA in the forward pass and then reconstructing them in the backwards pass to implement the update rules. In PEFT, VeLoRA is configured as a LoRA variant through the `velora_config` argument on [`LoraConfig`].
+
+```py
+from peft import LoraConfig, VeloraConfig
+
+config = LoraConfig(
+    target_modules=["q_proj", "v_proj"],
+    velora_config=VeloraConfig(
+        num_groups=64,
+        scale=0.2,
+        init_type="batch_average",
+    ),
+)
+```
+
+VeLoRA is applied to every LoRA layer selected by `target_modules`. `num_groups` controls how the input activation depth is split before compression. If the activation depth is not evenly divisible by `num_groups`, VeLoRA pads the grouped representation internally and removes the padding after reconstruction. `scale` rescales the reconstructed activations during the backward pass, and `init_type` chooses how the projection is initialized.
+
+Use `batch_average_once` to initialize the projection from the first training batch, `batch_average` to update it from every training forward pass, or `random` to initialize it immediately from a random normalized vector.
+
+Below are some results with the [MetaMathQA benchmark](https://github.com/huggingface/peft/tree/main/method_comparison/MetaMathQA). 
+
+| Variant | Training Loss | Max Memory (GiB) | Tokens/sec |
+|---|---:|---:|---:|
+| LoRA | 0.5427 | 27.69 | 2366.2 |
+| LoRA + GC | 0.5426 | 13.17 | 1671.8 |
+| LoRA+VeLoRA | 0.5427 | 19.94 | 2057.6 |
+
+#### Caveats
+
+- VeLoRA is currently supported on standard LoRA linear layers only.
+
+## Training
+
+This section shows how to handle more complex training scenarios instead of only applying a low-rank adapter
+to the model and feed data.
+
+### QLoRA-style training
+
+The default LoRA settings in PEFT add trainable weights to the query and value layers of each attention block. But [QLoRA](https://hf.co/papers/2305.14314), which adds trainable weights to all the linear layers of a transformer model, can provide performance equal to a fully finetuned model. To apply LoRA to all the linear layers, like in QLoRA, set `target_modules="all-linear"` (easier than specifying individual modules by name which can vary depending on the architecture).
+
+```py
+config = LoraConfig(target_modules="all-linear", ...)
+```
+
+For more information about how to apply quantization to PEFT adapters, refer to the [quantization guide](quantization).
+
+### Memory efficient Layer Replication with LoRA
+
+An approach used to improve the performance of models is to expand a model by duplicating layers in the model to build a larger model from a pretrained model of a given size. For example increasing a 7B model to a 10B model as described in the [SOLAR](https://huggingface.co/papers/2312.15166) paper. PEFT LoRA supports this kind of expansion in a memory efficient manner that supports further fine-tuning using LoRA adapters attached to the layers post replication of the layers. The replicated layers do not take additional memory as they share the underlying weights so the only additional memory required is the memory for the adapter weights. To use this feature you would create a config with the `layer_replication` argument.
+
+```py
+config = LoraConfig(layer_replication=[[0,4], [2,5]], ...)
+```
+
+Assuming the original model had 5 layers `[0, 1, 2 ,3, 4]`, this would create a model with 7 layers arranged as `[0, 1, 2, 3, 2, 3, 4]`. This follows the [mergekit](https://github.com/arcee-ai/mergekit) pass through merge convention where sequences of layers specified as start inclusive and end exclusive tuples are stacked to build the final model. Each layer in the final model gets its own distinct set of LoRA adapters.
+
+[Fewshot-Metamath-OrcaVicuna-Mistral-10B](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B) is an example of a model trained using this method on Mistral-7B expanded to 10B. The
+[adapter_config.json](https://huggingface.co/abacusai/Fewshot-Metamath-OrcaVicuna-Mistral-10B/blob/main/adapter_config.json) shows a sample LoRA adapter config applying this method for fine-tuning.
+
+### Fine grained control over ranks and alpha (scaling)
+
+By default, all layers targeted with LoRA will have the same rank `r` and the same `lora_alpha` (which determines the LoRA scaling), depending on what was specified in the [`LoraConfig`]. In some cases, however, you may want to indicate different values for different layers. This is possible by passing the `rank_pattern` and `alpha_pattern` arguments to [`LoraConfig`]. These arguments should be dictionaries with the key being the layer name and the value being the rank/alpha value. The keys can be [regular expressions](https://docs.python.org/3/library/re.html) (regex). All LoRA layers that are not explicitly mentioned in `rank_pattern` and `alpha_pattern` will take the default `r` and `lora_alpha` values.
+
+To give an example, let's assume that we have a model with the following structure:
+
+```python
+>>> print(model)
+Outer(
+  (foo): Linear(...)
+  (module): Middle(
+    (foo): Linear(...)
+    (foobar): Linear(...)
+    (module): Inner(
+      (foo): Linear(...)
+      (barfoo): Linear(...)
+    )
+  )
+)
+```
+
+- `rank_pattern={"foo": 42}` will match all 3 `foo` layers. Neither `foobar` nor `barfoo` are matched.
+- `rank_pattern={"^foo": 42}` will only match the `foo` layer of the model, but neither `module.foo` nor `module.module.foo`. This is because the `^` means "start of string" when using regular expressions, and only `foo` starts with `"foo"`, the other layer names have prefixes.
+- `rank_pattern={"^module.foo": 42}` matches only `module.foo`, but not `module.module.foo`, for the same reason.
+- `rank_pattern={"module.foo": 42}` matches both `module.foo` and `module.module.foo`, but not `foo`.
+- `rank_pattern={"^foo": 42, "^module.module.foo": 55}` matches `foo` and `module.module.foo`, respectively, but not `module.foo`.
+- There is no need to indicate `$` to mark the end of the match, as this is added automatically by PEFT.
+
+The same logic applies to `alpha_pattern`. If you're in doubt, don't try to get fancy with regular expressions -- just pass the full name for each module with a different rank/alpha, preceded by the `^` prefix, and you should be good.
+
+### Targeting `nn.Parameter` directly
+
+Generally, you should use `target_modules` to target the module (e.g. `nn.Linear`). However, in some circumstances, this is not possible. E.g., in many mixture of expert (MoE) layers in HF Transformers, instead of using `nn.Linear`, an `nn.Parameter` is used. PEFT normally overwrites the `forward` method for LoRA, but for `nn.Parameter`, there is none. Therefore, to apply LoRA to that parameter, it needs to be targeted with `target_parameters`. As an example, for [Llama4](https://huggingface.co/collections/meta-llama/llama-4-67f0c30d9fe03840bc9d0164), you can pass: `target_parameters=['feed_forward.experts.gate_up_proj', 'feed_forward.experts.down_proj]`.
+
+Note that when targeting expert parameters, PEFT can add a substantial runtime overhead. The reason is that PEFT always materializes the LoRA contribution for _each expert_ even if only a small amount of experts is required. During training, this is less relevant since, over the course of the sequence, typically a large fraction of experts is activated at least once. However, during inference, normally a KV cache is used and we thus need to only compute the last token, which means that only a small amount of experts is activated. Therefore, using LoRA on MoE layers can result in a substantial slowdown at inference time. The recommendation is thus to merge the weights (`model.merge_adapter()` or `model = model.merge_and_unload()`). This removes the PEFT overhead.
+
+A more detailed investigation of this issue can be found on this [pull request on MoE optimization](https://github.com/huggingface/peft/pull/3139).
+
+#### Caveats
+
+- At the moment, this argument allows to target 2-dim or 3-dim `nn.Parameter`s. It is assumed that in the case of a 3-dim parameter, the 0th dimension is the expert dimension.
+- It is currently not possible to add multiple LoRA adapters (via `model.add_adapter` or `model.load_adapter`) that use `target_parameters` at the same time.
+
+#### MoE expert parameters and vLLM
+
+Some MoE models in Transformers store expert weights as `nn.Parameter` tensors (often 3D), not `nn.Linear` modules.
+To apply LoRA to those experts, use `target_parameters` and set a per-layer rank with `rank_pattern`:
+
+```python
+num_experts = getattr(model.config, "num_local_experts", None) or model.config.num_experts
+effective_r = max(1, r // num_experts)
+config = LoraConfig(
+    r=r,
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],
+    target_parameters=[
+        # Mixtral / Qwen3-MoE / GPT-OSS
+        "mlp.experts.gate_up_proj",
+        "mlp.experts.down_proj",
+        # Llama4
+        # "feed_forward.experts.gate_up_proj",
+        # "feed_forward.experts.down_proj",
+    ],
+    rank_pattern={
+        "experts.gate_up_proj": effective_r,
+        "experts.down_proj": effective_r,
+    },
+)
+```
+
+This keeps the total LoRA parameter budget similar to dense layers (see
+[LoRA Without Regret](https://thinkingmachines.ai/blog/lora/) by Schulman et. al.).
+Non-expert modules use the default rank `r`.
+
+Accelerated inference with the fine-tuned model is possible with, for example, [vLLM](https://vllm.ai/) which supports fused MoE expert layers since v0.11.2.
+
+### Efficiently train tokens alongside LoRA
+
+PEFT LoRA adapters support adding new tokens with the `trainable_token_indices` parameter. This allows tuning of other tokens alongside fine-tuning specific layers. Only the specified tokens are trained and all other tokens are untouched. It saves memory and doesn't throw away learned context from existing token embeddings unlike training the whole embedding matrix. Under the hood this method uses the layer of [`TrainableTokensModel`].
+
+```py
+# for layer 'embed_tokens'
+config = LoraConfig(trainable_token_indices=[idx_1, idx_2, ...], ...)
+
+# specific embedding layer
+config = LoraConfig(trainable_token_indices={'emb_tokens': [idx_1, idx_2, ...]}, ...)
+```
+
+In the snippet below we show how to add new tokens to the model and how to train it alongside the other layers in the model.
+
+```py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import get_peft_model, LoraConfig
+
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+# we define our new tokens and add them to the tokenizer as special tokens
+special_tokens = ['<|start_think|>', '<|stop_think|>']
+tokenizer.add_special_tokens({'additional_special_tokens': special_tokens})
+
+# make room for new tokens in the embedding matrix if it isn't big enough already
+base_model.resize_token_embeddings(max(len(tokenizer), base_model.model.embed_tokens.num_embeddings))
+
+# typical LoRA config with `trainable_token_indices` targeting embedding layer `embed_tokens`
+# and specifically our new tokens we just added
+lora_config = LoraConfig(
+    target_modules='all-linear',
+    trainable_token_indices={'embed_tokens': tokenizer.convert_tokens_to_ids(special_tokens)},
+)
+peft_model = get_peft_model(base_model, lora_config)
+
+# proceed to train the model like normal
+[...]
+```
+
+The token weights are saved as a part of the adapter state dict alongside the LoRA weights. Full fine-tuning and saving the embedding matrix would have stored a much bigger file.
+
+To give a bit of an indication how much VRAM can be saved, a rudimentary comparison of the above example was made between training the embedding matrix fully (`modules_to_save=["embed_tokens"]`), using a LoRA for the embedding matrix (`target_modules=[..., "embed_tokens"]`, rank 32) and trainable tokens (`trainable_token_indices=[...]`, 6 tokens):
+
+|           | Trainable Tokens |       LoRA | Full Fine-tuning |
+| --------: | :--------------: | :--------: | :--------------: |
+| VRAM      |        15,562 MB |   15,581MB |     ~16,500MB    |
+| Influence |         6 tokens | all tokens |       all tokens |
+
+### Weight tying
+
+Many causal LMs use **weight tying**, where two or more weights share the same underlying parameters. In the most common case, the input embedding weights (`embed_tokens`) and output projection weights (`lm_head`) share the same tensor. This is because it reduces parameters and usually preserves model quality.
+
+It's not always obvious how PEFT deals with these tied weights when they are targeted for fine-tuning. For LoRA, the `ensure_weight_tying` on the [`LoraConfig`] controls whether PEFT should explicitly keep adapter-side updates tied for those layers. In practice, this can affect `modules_to_save`, `target_modules`, and `trainable_token_indices`. Note that this logic partially relies on convention when it comes to naming the layers (`"embed_tokens"`, `"lm_head"`) and proper working cannot be guaranteed if those conventions are not used.
+
+The tables below summarize expected behavior.
+
+#### `modules_to_save`
+
+| Base model weights tied | `ensure_weight_tying` | `LoraConfig` shape                                  | Behavior                                                     |
+|-------------------------|-----------------------|-----------------------------------------------------|--------------------------------------------------------------|
+| No                      | `False`               | `modules_to_save=["embed_tokens"]` or `["lm_head"]` | Add `ModulesToSaveWrapper` on selected layer only            |
+| No                      | `True`                | `modules_to_save=["embed_tokens"]` or `["lm_head"]` | Warn, then add `ModulesToSaveWrapper` on selected layer only |
+| Yes                     | `False`               | `modules_to_save=["embed_tokens"]` or `["lm_head"]` | Treat as separate                                            |
+| Yes                     | `True`                | `modules_to_save=["embed_tokens"]` or `["lm_head"]` | Wrap tied layers and keep wrappers tied                      |
+| No                      | `False`               | `modules_to_save=["embed_tokens", "lm_head"]`       | Treat as separate                                            |
+| No                      | `True`                | `modules_to_save=["embed_tokens", "lm_head"]`       | Warn, then treat as separate                                 |
+| Yes                     | `False`               | `modules_to_save=["embed_tokens", "lm_head"]`       | Warn, then treat as separate                                            |
+| Yes                     | `True`                | `modules_to_save=["embed_tokens", "lm_head"]`       | Keep `ModulesToSaveWrapper`s tied                            |
+
+#### `target_modules`
+
+| Base model weights tied | `ensure_weight_tying` | `LoraConfig` shape                                 | Behavior                                   |
+|-------------------------|-----------------------|----------------------------------------------------|--------------------------------------------|
+| No                      | `False`               | `target_modules=["embed_tokens"]` or `["lm_head"]` | Add LoRA on selected layer only            |
+| No                      | `True`                | `target_modules=["embed_tokens"]` or `["lm_head"]` | Warn, then add LoRA on selected layer only |
+| Yes                     | `False`               | `target_modules=["embed_tokens"]` or `["lm_head"]` | Treat as separate                          |
+| Yes                     | `True`                | `target_modules=["embed_tokens"]` or `["lm_head"]` | Keep LoRA adapters tied                    |
+| No                      | `False`               | `target_modules=["embed_tokens", "lm_head"]`       | Treat as separate                          |
+| No                      | `True`                | `target_modules=["embed_tokens", "lm_head"]`       | Warn, then treat as separate               |
+| Yes                     | `False`               | `target_modules=["embed_tokens", "lm_head"]`       | Warn, then treat as separate                          |
+| Yes                     | `True`                | `target_modules=["embed_tokens", "lm_head"]`       | Keep LoRA adapters tied                    |
+
+#### `trainable_token_indices`
+
+For trainable tokens, we have the additional complication that even if the LM head and embeddings are tied, as a user I may want to fine-tune *different* tokens on them. In the example table below, we thus differentiate between fine-tuning the same and fine-tuning different tokens.
+
+| Base model weights tied | `ensure_weight_tying` | `LoraConfig` shape                                                    | Behavior                                       |
+|-------------------------|-----------------------|-----------------------------------------------------------------------|------------------------------------------------|
+| No                      | `False`               | `trainable_token_indices=[1, 2, 3]`                                   | Trainable tokens on embeddings only            |
+| No                      | `True`                | `trainable_token_indices=[1, 2, 3]`                                   | Warn, then trainable tokens on embeddings only |
+| Yes                     | `False`               | `trainable_token_indices=[1, 2, 3]`                                   | Tied trainable tokens                          |
+| Yes                     | `True`                | `trainable_token_indices=[1, 2, 3]`                                   | Tied trainable tokens                          |
+| No                      | `False`               | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [1, 2]}` | Treat as separate                              |
+| No                      | `True`                | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [1, 2]}` | Warn, then treat as separate                   |
+| Yes                     | `False`               | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [1, 2]}` | Tied trainable tokens                          |
+| Yes                     | `True`                | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [1, 2]}` | Tied trainable tokens                          |
+| No                      | `False`               | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [3, 4]}` | Treat as separate                              |
+| No                      | `True`                | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [3, 4]}` | Warn, then treat as separate                   |
+| Yes                     | `False`               | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [3, 4]}` | Treat as separate                              |
+| Yes                     | `True`                | `trainable_token_indices={"lm_head": [1, 2], "embed_tokens": [3, 4]}` | Error                                          |
+
+For users, this means:
+
+- In general, if you want to fine-tune weights that are tied and want to keep them tied, pass `ensure_weight_tying=True`.
+- If your base model's weights are untied, `ensure_weight_tying=True` cannot force tying and only warns.
+- For `trainable_token_indices`, tied layers must use the same token indices when `ensure_weight_tying=True`.
+
+## Optimizers
+
+LoRA training can optionally include special purpose optimizers. Currently PEFT supports LoRA-FA and LoRA+.
+
+### LoRA-FA Optimizer
+
+LoRA training can be more effective and efficient using LoRA-FA, as described in [LoRA-FA](https://huggingface.co/papers/2308.03303). LoRA-FA reduces activation memory consumption by fixing the matrix A and only tuning the matrix B. During training, the gradient of B is optimized to approximate the full parameter fine-tuning gradient. Moreover, the memory consumption of LoRA-FA is not sensitive to the rank (since it erases the activation of $A$), therefore it can improve performance by enlarging lora rank without increasing memory consumption.
+
+```py
+from peft import LoraConfig, get_peft_model
+from peft.optimizers import create_lorafa_optimizer
+from transformers import Trainer, get_cosine_schedule_with_warmup
+
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
+
+config = LoraConfig(...)
+model = get_peft_model(base_model, config)
+
+optimizer = create_lorafa_optimizer(
+    model=model,
+    r=128,
+    lora_alpha=32,
+    lr=7e-5,
+)
+
+scheduler = get_cosine_schedule_with_warmup(
+    optimizer,
+    num_warmup_steps=100,
+    num_training_steps=1000,
+)
+
+trainer = Trainer(
+    ...,
+    optimizers=(optimizer, scheduler),
+)
+```
+
+### LoRA+ optimized LoRA
+
+LoRA training can be optimized using [LoRA+](https://huggingface.co/papers/2402.12354), which uses different learning rates for the adapter matrices A and B, shown to increase finetuning speed by up to 2x and performance by 1-2%.
+
+```py
+from peft import LoraConfig, get_peft_model
+from peft.optimizers import create_loraplus_optimizer
+from transformers import Trainer
+import bitsandbytes as bnb
+
+base_model = ...
+config = LoraConfig(...)
+model = get_peft_model(base_model, config)
+
+optimizer = create_loraplus_optimizer(
+    model=model,
+    optimizer_cls=bnb.optim.Adam8bit,
+    lr=5e-5,
+    loraplus_lr_ratio=16,
+)
+scheduler = None
+
+...
+trainer = Trainer(
+    ...,
+    optimizers=(optimizer, scheduler),
+)
+```
+
+
+## Post-Training
+
+This section shows potential post-processing methods for trained adapters.
+
+
+### Merge LoRA weights into the base model
+
+While LoRA is significantly smaller and faster to train, you may encounter latency issues during inference due to separately loading the base model and the LoRA adapter. To eliminate latency, use the [`~LoraModel.merge_and_unload`] function to merge the adapter weights with the base model. This allows you to use the newly merged model as a standalone model. The [`~LoraModel.merge_and_unload`] function doesn't keep the adapter weights in memory.
+
+Below is a diagram that explains the intuition of LoRA adapter merging:
+
+<div class="flex justify-center">
+    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"/>
+</div>
+
+We show in the snippets below how to run that using PEFT.
+
+```py
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+model = model.merge_and_unload()
+```
+
+It is important to assign the returned model to a variable and use it, [`~LoraModel.merge_and_unload`] is not an in-place operation. If you need to keep a copy of the weights so you can unmerge the adapter later or delete and load different ones, you should use the [`~LoraModel.merge_adapter`] function instead. Now you have the option to use [`~LoraModel.unmerge_adapter`] to return the base model.
+
+```py
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+model.merge_adapter()
+
+# unmerge the LoRA layers from the base model
+model.unmerge_adapter()
+```
+
+The [`~LoraModel.add_weighted_adapter`] function is useful for merging multiple LoRAs into a new adapter based on a user provided weighting scheme in the `weights` parameter. Below is an end-to-end example.
+
+First load the base model:
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+import torch
+
+base_model = AutoModelForCausalLM.from_pretrained(
+    "mistralai/Mistral-7B-v0.1", dtype=torch.float16, device_map="auto"
+)
+```
+
+Then we load the first adapter:
+
+```python
+peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
+model = PeftModel.from_pretrained(base_model, peft_model_id, adapter_name="sft")
+```
+
+Then load a different adapter and merge it with the first one:
+
+```python
+weighted_adapter_name = "sft-dpo"
+model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
+model.add_weighted_adapter(
+    adapters=["sft", "dpo"],
+    weights=[0.7, 0.3],
+    adapter_name=weighted_adapter_name,
+    combination_type="linear"
+)
+model.set_adapter(weighted_adapter_name)
+```
+
+> [!TIP]
+> There are several supported methods for `combination_type`. Refer to the [documentation](../package_reference/lora#peft.LoraModel.add_weighted_adapter) for more details. Note that "svd" as the `combination_type` is not supported when using `torch.float16` or `torch.bfloat16` as the datatype.
+
+Now, perform inference:
+
+```python
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+
+tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1")
+
+prompt = "Hey, are you conscious? Can you talk to me?"
+inputs = tokenizer(prompt, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+
+with torch.no_grad():
+    generate_ids = model.generate(**inputs, max_length=30)
+outputs = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+print(outputs)
+```
+
+### Recovering base model performance via intruder dimension reduction
+
+The paper [LoRA vs Full Fine-tuning: An Illusion of Equivalence](https://huggingface.co/papers/2410.21228) argues
+that LoRA training introduces extra dimensions into the weights that have very little in common with the already
+learnt weights and lead to forgetting of already learned information. PEFT implements the suggested mitigation
+in [`peft.tuners.lora.intruders.reduce_intruder_dimension`].
+
+The mitigation will take a PEFT model with a loaded LoRA and create a new, modified adapter that is loaded alongside
+the existing adapter and now the active adapter.
+
+Example usage:
+
+```python
+from peft.tuners.lora.intruders import reduce_intruder_dimension
+
+peft_model = AutoPeftModelForCausalLM.from_pretrained('hubnemo/llama-3.2b-metamathqa-lora64')
+
+reduce_intruder_dimension(
+    peft_model,
+    mitigation_lambda=0.75,
+)
+
+peft_model.generate(...)
+```
+
+There are a few hyper-parameters that can be used for tuning the effectiveness of the mitigation but, as evidenced
+in Figure 8 of the paper, it will always be a trade-off between task accuracy learned by the adapter and forgetting
+of the base model's knowledge. The mitigation will remove information from the adapter to reduce the impact on
+forgetting previous knowledge but this also means that some information about the task learned by the adapter is
+lost as well.
+
+While the defaults are set to deliver a good trade-off between the two factors it is not guaranteed that the defaults
+will hold for your adapter, your model and your data, therefore it is wise to have a benchmark ready to measure
+the effect.
+
+## Load adapters
+
+Adapters can be loaded onto a pretrained model with [`~PeftModel.load_adapter`], which is useful for trying out different adapters whose weights aren't merged. Set the active adapter weights with the [`~LoraModel.set_adapter`] function.
+
+```py
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1")
+peft_model_id = "alignment-handbook/zephyr-7b-sft-lora"
+model = PeftModel.from_pretrained(base_model, peft_model_id)
+
+# load different adapter
+model.load_adapter("alignment-handbook/zephyr-7b-dpo-lora", adapter_name="dpo")
+
+# set adapter as active
+model.set_adapter("dpo")
+```
+
+To return the base model, you could use [`~LoraModel.unload`] to unload all of the LoRA modules or [`~LoraModel.delete_adapter`] to delete the adapter entirely. [`~LoraModel.unload`] is not an in-place operation, remember to assign the returned model to a variable and use it.
+
+```py
+# unload adapter
+model = model.unload()
+
+# delete adapter
+model.delete_adapter("dpo")
+```
+
+## Tensor Parallelism
+
+LoRA supports [Tensor Parallelism (TP)](https://huggingface.co/docs/transformers/main/en/perf_train_gpu_many#tensor-parallelism) as provided by Transformers. When a base model is loaded with a `tp_plan`, PEFT automatically detects the TP configuration of each target module and adds the appropriate hooks to the LoRA adapter weights so that they participate correctly in the tensor-parallel computation.
+
+> [!WARNING]
+> Tensor Parallelism support for LoRA requires `transformers >= 5.4.0`.
+
+Usage is identical to the standard LoRA workflow — simply load the base model with a `tp_plan` before wrapping it with PEFT:
+
+```py
+from transformers import AutoModelForCausalLM
+from peft import get_peft_model, LoraConfig
+
+model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B", tp_plan="auto")
+lora_config = LoraConfig(r=16, target_modules=["q_proj", "v_proj"])
+model = get_peft_model(model, lora_config)
+```
+
+Saving and loading work as usual via `save_pretrained` / `from_pretrained`. PEFT gathers the sharded adapter weights back to full tensors before saving, so checkpoints are portable and independent of the number of devices used during training.
+
+## Inference
+
+This section showcases what you can do during inference time with LoRA, such as uncoupling the adapter.
+
+### Activated LoRA (aLoRA)
+
+Activated LoRA (aLoRA) is a low rank adapter architecture for causal LMs that reuses the existing base model KV cache for more efficient inference. This approach is best suited for inference pipelines which rely on the base model for most tasks/generations, but use aLoRA adapter(s) to perform specialized task(s) within the chain. For example, checking or correcting generated outputs of the base model. In these settings, inference times can be sped up by an order of magnitude or more. For more information on aLoRA and many example use cases, see the aLoRA [paper](https://huggingface.co/papers/2504.12397).
+
+This technique scans for the last occurrence of an invocation sequence (`alora_invocation_tokens`) in each input (this can be as short as 1 token). It activates the adapter weights on tokens starting with the beginning of the invocation sequence. Any inputs after the invocation sequence are also adapted, and all generated tokens will use the adapted weights. Weights on prior tokens are left un-adapted, making the cache for those tokens interchangeable with base model cache due to the causal attention mask in causal LMs. Usage is very similar to standard LoRA. The key difference is that the invocation sequence must be specified when the adapter is created:
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(alora_invocation_tokens=alora_invocation_tokens, task_type="CAUSAL_LM", ...)
+```
+
+alora_invocation_tokens` is a list of integer token ids. Given a desired invocation string, this can be obtained as:
+```py
+invocation_string = "placeholder"
+alora_invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False).
+```
+The tokenizer is the base model's tokenizer. Use `add_special_tokens=False` to avoid adding `SOS`/`EOS` tokens in our search string (which will most likely cause the search to fail).
+
+**Notes**
+* aLoRA is only supported for `task_type=CAUSAL_LM` tasks due to its focus on cache reuse.
+* Since the weights are adapted on fewer tokens, often (not always) aLoRA requires higher rank (`r`) than LoRA. `r=32` can be a good starting point.
+* aLoRA weights cannot be merged into the base model by definition, since the adapter weights are selectively applied to a subset of tokens. Attempts to merge will throw errors.
+* Beam search is not yet supported.
+* It is generally not recommended to add new tokens to the tokenizer that are not present in the base model. This can complicate the target use case of both the base model and adapter model operating on overlapping context. You can workaround this by adding [trainable tokens](../package_reference/trainable_tokens) to the base model prior to training the adapter.
+
+#### Choice of invocation sequence and SFT design
+
+You must add the `alora_invocation_tokens` sequence because it is not added automatically. We recommend activating the adapter weights early (at the start of any adapter-specific prompting), but after any long inputs, to maximize model performance without compromising cache reuse. As with any model,
+formatting should be consistent between train and test.
+
+Consider the following example, where the base model has a chat template,
+and the goal is to train the adapter to generate a desired output.
+
+* Option 1: If there is no task-specific prompt, i.e. the input is a chat history with the `assistant` prompt, then the chat template's `assistant` prompt (e.g. `<|start_of_role|>assistant<|end_of_role|>`) is a natural choice for the invocation string. See the model's chat template to find the prompt for the model.
+* Option 2: If there is a task-specific prompt for the adapter that describes the task the adapter is learning, and that prompt is put as a `user` turn immediately prior to the generation, then the chat template's `user` prompt (e.g. `<|start_of_role|>user<|end_of_role|>`) is a natural choice for the invocation string.
+
+After deciding on an invocation string, get the model tokenizer and obtain `alora_invocation_tokens` as
+```py
+alora_invocation_tokens = tokenizer.encode(invocation_string, add_special_tokens=False).
+```
+
+An example inference setup is at [alora finetuning](https://github.com/huggingface/peft/blob/main/examples/alora_finetuning/alora_finetuning.py).
+
+> [!NOTE]
+> If using custom strings for the invocation string, make sure that the start and end of the string are special tokens to avoid issues with tokenization at the boundaries.
+
+To see why, imagine that 'a', 'b', 'c', and 'ab' are tokens in your tokenizer (numbers 1, 2, 3, 4 respectively). Suppose that your alora_invocation_tokens = [2, 3]. Now imagine your input string is "abc". Because "ab" is a token, this will get tokenized as [4,3]. So the alora_invocation_tokens will fail to be found, despite the string "bc" being in it. If the start and end of the invocation string are special tokens, however, this failure case will never happen since special tokens are never tokenized into the same token with other characters.
+
+#### Using (and reusing) cache for generation
+The main purpose of aLoRA is to make KV cache interchangeable between the base model and aLoRA adapter models **prior to the invocation sequence** since base and adapted KV values are not compatible. Specifically, keys and values stored during one model generation can be used in subsequent generations to avoid expensive prefill operations for context tokens. When sharing cache between the base model and aLoRA adapters, there are 2 main patterns:
+1. The base model has generated something, and an aLoRA adapter is then called to do a follow-up generation. For example, the base model answers a question, and an aLoRA trained to detect hallucinations checks the base model response.
+2. An aLoRA adapter has generated something, and the base model or a different aLoRA adapter is called to do a follow-up generation where there is partial context overlap with the original aLoRA. For example, the user provides a query, and an aLoRA rewrites the query to be more self-contained and improve retrieval in a RAG system. Then, documents are retrieved and loaded into context, aLoRA checks if these documents are relevant to the question, and then the base model generates an answer.
+
+
+To demonstrate the above behaviors when using caching, we're using [DynamicCache](https://huggingface.co/docs/transformers/en/kv_cache) from `transformers`. Take care to ensure that adapted cache values are not mixed with base cache values. In particular, an extra step is required for sharing the cache when there is partial context overlap (pattern 2).
+
+**Pattern 1: Base model followed by aLoRA** Here, the entire input and generation from the base model is input into the aLoRA adapter, along with the invocation sequence:
+```
+from transformers import DynamicCache
+...
+cache = DynamicCache()
+inputs_base = tokenizer(prompt_base, return_tensors="pt")
+# Generate from base model and save cache
+with model_alora.disable_adapter():
+    output = model_alora.generate(inputs_base["input_ids"].to(device),attention_mask=inputs_base["attention_mask"].to(device),past_key_values = cache,return_dict_in_generate=True)
+output_text_base = tokenizer.decode(output.sequences[0])
+cache = output.past_key_values
+
+# Generate with aLoRA adapter from cache
+prompt_alora = output_text + INVOCATION_STRING
+inputs_alora = tokenizer(prompt_alora, return_tensors="pt").to(device)
+output = model_alora.generate(**inputs_alora, past_key_values=cache)
+output_text_alora = tokenizer.decode(output[0])
+
+# Note: cache is now tainted with adapter values and cannot be used in base model from here on!
+```
+
+**Pattern 2: aLoRA generation followed by base model (or another aLoRA) with partial context overlap** Here, we prefill the shared context using the base model, and then generate.
+
+```
+from transformers import DynamicCache
+import copy
+...
+cache = DynamicCache()
+inputs_shared = tokenizer(prompt_shared, return_tensors="pt").to(device)
+
+# Prefill from base model and save cache
+with model_alora.disable_adapter():
+    with torch.no_grad():
+        model_alora(**inputs_shared, past_key_values=cache)
+cache_copy = copy.deepcopy(cache)
+
+# Generate from aLoRA using prefilled cache
+prompt_alora = prompt_shared + INVOCATION_STRING
+inputs_alora = tokenizer(prompt_alora, return_tensors="pt").to(device)
+output = model_alora.generate(**inputs_alora, past_key_values=cache)
+output_text_alora = tokenizer.decode(output[0])
+
+# Generate from base model using saved cache not tainted by aLoRA KV values
+prompt_base = prompt_shared
+inputs_base = tokenizer(prompt_base, return_tensors="pt").to(device)
+with model_alora.disable_adapter():
+    output = model_alora.generate(**inputs_base, past_key_values=cache_copy)
+output_text_base = tokenizer.decode(output[0])
+```
+
+
+### Inference with different LoRA adapters in the same batch
+
+Normally, each inference batch has to use the same adapter(s) in PEFT. This can sometimes be annoying, because we may have batches that contain samples intended to be used with different LoRA adapters. For example, we could have a base model that works well in English and two more LoRA adapters, one for French and one for German. Usually, we would have to split our batches such that each batch only contains samples of one of the languages, we cannot combine different languages in the same batch.
+
+Thankfully, it is possible to mix different LoRA adapters in the same batch using the `adapter_name` argument. Below, we show an example of how this works in practice. First, let's load the base model, English, and the two adapters, French and German, like this:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+model_id = ...
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+model = AutoModelForCausalLM.from_pretrained(model_id)
+# load the LoRA adapter for French
+peft_model = PeftModel.from_pretrained(model, <path>, adapter_name="adapter_fr")
+# next, load the LoRA adapter for German
+peft_model.load_adapter(<path>, adapter_name="adapter_de")
+```
+
+Now, we want to generate text on a sample that contains all three languages: The first three samples are in English, the next three are in French, and the last three are in German. We can use the `adapter_names` argument to specify which adapter to use for each sample. Since our base model is used for English, we use the special string `"__base__"` for these samples. For the next three samples, we indicate the adapter name of the French LoRA fine-tune, in this case `"adapter_fr"`. For the last three samples, we indicate the adapter name of the German LoRA fine-tune, in this case `"adapter_de"`. This way, we can use the base model and the two adapters in a single batch.
+
+```python
+inputs = tokenizer(
+    [
+        "Hello, my dog is cute",
+        "Hello, my cat is awesome",
+        "Hello, my fish is great",
+        "Salut, mon chien est mignon",
+        "Salut, mon chat est génial",
+        "Salut, mon poisson est super",
+        "Hallo, mein Hund ist süß",
+        "Hallo, meine Katze ist toll",
+        "Hallo, mein Fisch ist großartig",
+    ],
+    return_tensors="pt",
+    padding=True,
+)
+
+adapter_names = [
+    "__base__", "__base__", "__base__",
+    "adapter_fr", "adapter_fr", "adapter_fr",
+    "adapter_de", "adapter_de", "adapter_de",
+]
+output = peft_model.generate(**inputs, adapter_names=adapter_names, max_new_tokens=20)
+```
+
+Note that the order does not matter here, i.e. the samples in the batch don't need to be grouped by adapter as in the example above. We just need to ensure that the `adapter_names` argument is aligned correctly with the samples.
+
+Additionally, the same approach also works with the `modules_to_save` feature, which allows for saving and reusing specific neural network layers, such as custom heads for classification tasks, across different LoRA adapters.
+
+#### Caveats
+
+Using this feature has some drawbacks, namely:
+
+- It only works for inference, not for training.
+- Disabling adapters using the `with model.disable_adapter()` context takes precedence over `adapter_names`.
+- You cannot pass `adapter_names` when some adapter weights were merged with base weight using the `merge_adapter` method. Please unmerge all adapters first by calling `model.unmerge_adapter()`.
+- For obvious reasons, this cannot be used after calling `merge_and_unload()`, since all the LoRA adapters will be merged into the base weights in this case.
+- This feature does not currently work with DoRA, so set `use_dora=False` in your `LoraConfig` if you want to use it.
+- The `modules_to_save` feature is currently only supported for the layers of types `Linear`, `Embedding`, `Conv2d` and `Conv1d`.
+- There is an expected overhead for inference with `adapter_names`, especially if the amount of different adapters in the batch is high. This is because the batch size is effectively reduced to the number of samples per adapter. If runtime performance is your top priority, try the following:
+  - Increase the batch size.
+  - Try to avoid having a large number of different adapters in the same batch, prefer homogeneous batches. This can be achieved by buffering samples with the same adapter and only perform inference with a small handful of different adapters.
+  - Take a look at alternative implementations such as [LoRAX](https://github.com/predibase/lorax), [punica](https://github.com/punica-ai/punica), or [S-LoRA](https://github.com/S-LoRA/S-LoRA), which are specialized to work with a large number of different adapters.
+
+
+### Composing and Reusing LoRA Adapters
+#### Arrow
+[Arrow](https://huggingface.co/papers/2405.11157) is a modular routing algorithm designed to combine multiple pre-trained task-specific LoRA adapters to solve a given task. Rather than merging all adapters naively, Arrow introduces a **gradient-free, token-wise mixture-of-experts (MoE) routing mechanism**. At inference time, it first computes a _prototype_ for each LoRA by extracting the top right singular vector from its SVD decomposition. Each token representation is then compared to these prototypes via cosine similarity to obtain routing coefficients. Tokens are assigned to the top-k most relevant LoRA adapters, with the coefficients normalized through softmax, and their outputs linearly combined. This allows effective reuse of existing LoRA modules for new tasks and leads to stronger zero-shot generalization.
+
+In PEFT, Arrow is enabled through [`ArrowConfig]` and `create_arrow_model`. You can also configure parameters such as `top_k` (the number of LoRA adapters combined per token), `router_temperature` (the softmax temperature applied to the routing coefficients), and `rng_seed` (for reproducibility).
+
+```py
+from peft import create_arrow_model, ArrowConfig
+from transformers import AutoModelForCausalLM
+
+# Loading the model
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+# Creating the Arrow config
+arrow_config = ArrowConfig(
+    top_k=3,
+    router_temperature=1.0,
+    rng_seed=42,
+)
+
+# The LoRA adapters below were trained on a clustered FLAN dataset.
+# Task clustering was performed using the Model-Based Clustering (MBC) method,
+# as described in the Arrow paper.
+# While one could train a separate LoRA for each task and let Arrow route tokens among them,
+# training LoRAs on clusters of tasks instead provides an indirect optimization for
+# transfer across the multi-task dataset.
+task_specific_adapter_paths = [
+        f"TahaBa/phi3-mini-clustered-flan/ts_expert_{i}" for i in range(10)
+    ]
+
+# Creating the Arrow model
+model = create_arrow_model(
+        base_model=base_model,
+        task_specific_adapter_paths=task_specific_adapter_paths,
+        arrow_config=arrow_config,
+    )
+
+# Now the forward path could be called on this model, like a normal PeftModel.
+```
+
+Furthermore, you can add or remove adapters after calling ```create_arrow_model```—for example, to fine-tune a new adapter or discard an unnecessary one. Once the adapters are in place, you can activate the ```"arrow_router"``` for inference to use Arrow. Note that if you add a new LoRA adapter after ```create_arrow_model``` and want to fine-tune it, you must explicitly set the new adapter as active, since ```"arrow_router"``` is activated by default in ```create_arrow_model```.
+
+```py
+from trl import SFTTrainer, SFTConfig
+
+# Adding a new adapter and activating it
+model.add_adapter(adapter_name='new_adapter')
+model.set_adapter('new_adapter')
+
+# Now the model could be trained along the `new_adapter`.
+trainer = SFTTrainer(
+        model=model,
+        args=SFTConfig(...),
+        ...
+    )
+
+# Once the training is done, you can activate `arrow_router` and use it in inference
+model.set_adapter('arrow_router')    # Model is ready to be used at inference time now
+```
+
+#### GenKnowSub
+[GenKnowSub](https://aclanthology.org/2025.acl-short.54/) augments Arrow by purifying task-specific LoRA adapters before routing. The key idea is to subtract general knowledge encoded in LoRA space—based on the [forgetting-via-negation principle](https://huggingface.co/papers/2212.04089)—so that task adapters become more isolated and focused on task-relevant signals. Concretely, GenKnowSub estimates a low-dimensional “general” subspace from a set of general (non task-specific) LoRA adapters and removes this component from each task adapter’s LoRA update prior to Arrow’s token-wise routing. This typically improves compositionality and reduces interference when combining many task adapters.
+
+In PEFT, enable GenKnowSub by setting ```use_gks=True``` in ArrowConfig, and providing ```general_adapter_paths``` in ```create_arrow_model```:
+
+```py
+from peft import create_arrow_model, ArrowConfig
+from transformers import AutoModelForCausalLM
+
+# Loading the model
+base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
+
+# Creating the Arrow config
+arrow_config = ArrowConfig(
+    top_k=3,
+    router_temperature=1.0,
+    use_gks=True,
+    rng_seed=42,
+)
+
+# Path to task-specific, trained on flan clustered dataset (as we explained before.)
+task_specific_adapter_paths = [
+        f"TahaBa/phi3-mini-clustered-flan/ts_expert_{i}" for i in range(10)
+    ]
+# These general adapters are trained on English, German, and French Wikipedia dataset,
+# with causal language modelling objective, each pair like: (507 token tsentence, 5 token completion), and the loss computed on the completion
+general_adapter_paths = [
+        "TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langen/checkpoint-17",
+        "TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langfr/checkpoint-35",
+        "TahaBa/phi3-mini-general-adapters/cluster0_batch16_prop1.0_langger/checkpoint-17"
+    ]
+
+# Creating the Arrow model
+model = create_arrow_model(
+        base_model=base_model,
+        task_specific_adapter_paths=task_specific_adapter_paths,
+        general_adapter_paths=general_adapter_paths,
+        arrow_config=arrow_config,
+    )
+
+# Now the forward path could be called on this model, like a normal PeftModel.
+```
+To encode general knowledge, GenKnowSub subtracts the average of the provided general adapters from each task-specific adapter once, before routing begins. Furthermore, the ability to add or remove adapters after calling ```create_arrow_model``` (as described in the Arrow section) is still supported in this case.
+
+> [!TIP]
+> **Things to keep in mind when using Arrow + GenKnowSub:**
+>
+> - All LoRA adapters (task-specific and general) must share the same ```rank``` and ```target_modules```.
+>
+> - Any inconsistency in these settings will raise an error in ```create_arrow_model```.
+>
+> - Having different scaling factors (```lora_alpha```) across task adapters is supported — Arrow handles them automatically.
+>
+> - Merging the ```"arrow_router"``` is not supported, due to its dynamic routing behavior.
+>
+> - In create_arrow_model, task adapters are loaded as ```task_i``` and general adapters as ```gks_j``` (where ```i``` and ```j``` are indices). The function ensures consistency of ```target_modules```, ```rank```, and whether adapters are applied to ```Linear``` or ```Linear4bit``` layers. It then adds the ```"arrow_router"``` module and activates it. Any customization of this process requires overriding ```create_arrow_model```.
+>
+> - This implementation is compatible with 4-bit quantization (via bitsandbytes):
+>
+>     ```py
+>     from transformers import AutoModelForCausalLM, BitsAndBytesConfig
+>     import torch
+>
+>     # Quantisation config
+>     bnb_config = BitsAndBytesConfig(
+>             load_in_4bit=True,
+>             bnb_4bit_quant_type="nf4",
+>             bnb_4bit_compute_dtype=torch.bfloat16,
+>             bnb_4bit_use_double_quant=False,
+>         )
+>
+>     # Loading the model
+>     base_model = AutoModelForCausalLM.from_pretrained(
+>         "microsoft/Phi-3-mini-4k-instruct",
+>         dtype=torch.bfloat16,
+>         device_map="auto",
+>         quantization_config=bnb_config,
+>     )
+>
+>     # Now call create_arrow_model() as we explained before.
+>     ```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/low_level_api.md

@@ -0,0 +1,148 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Adapter injection
+
+With PEFT, you can inject trainable adapters into any `torch` module which allows you to use adapter methods without relying on the modeling classes in PEFT. This works for all adapters except for those based on prompt learning (e.g. prefix tuning or p-tuning).
+
+Check the table below to see when you should inject adapters.
+
+| Pros | Cons |
+|---|---|
+| the model is modified inplace, keeping all the original attributes and methods | manually write the `from_pretrained` and `save_pretrained` utility functions from Hugging Face to save and load adapters |
+| works for any `torch` module and modality | doesn't work with any of the utility methods provided by `PeftModel` such as disabling and merging adapters |
+
+## Creating a new PEFT model
+
+To perform the adapter injection, use the [`inject_adapter_in_model`] method. This method takes 3 arguments, the PEFT config, the model, and an optional adapter name. You can also attach multiple adapters to the model if you call [`inject_adapter_in_model`] multiple times with different adapter names.
+
+For example, to inject LoRA adapters into the `linear` submodule of the `DummyModel` module:
+
+```python
+import torch
+from peft import inject_adapter_in_model, LoraConfig
+
+class DummyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.embedding = torch.nn.Embedding(10, 10)
+        self.linear = torch.nn.Linear(10, 10)
+        self.lm_head = torch.nn.Linear(10, 10)
+
+    def forward(self, input_ids):
+        x = self.embedding(input_ids)
+        x = self.linear(x)
+        x = self.lm_head(x)
+        return x
+
+
+lora_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.1,
+    r=64,
+    bias="none",
+    target_modules=["linear"],
+)
+
+model = DummyModel()
+model = inject_adapter_in_model(lora_config, model)
+
+dummy_inputs = torch.LongTensor([[0, 1, 2, 3, 4, 5, 6, 7]])
+dummy_outputs = model(dummy_inputs)
+```
+
+Print the model to see that the adapters have been correctly injected.
+
+```bash
+DummyModel(
+  (embedding): Embedding(10, 10)
+  (linear): Linear(
+    in_features=10, out_features=10, bias=True
+    (lora_dropout): ModuleDict(
+      (default): Dropout(p=0.1, inplace=False)
+    )
+    (lora_A): ModuleDict(
+      (default): Linear(in_features=10, out_features=64, bias=False)
+    )
+    (lora_B): ModuleDict(
+      (default): Linear(in_features=64, out_features=10, bias=False)
+    )
+    (lora_embedding_A): ParameterDict()
+    (lora_embedding_B): ParameterDict()
+  )
+  (lm_head): Linear(in_features=10, out_features=10, bias=True)
+)
+```
+
+### Injection based on a `state_dict`
+
+Sometimes, it is possible that there is a PEFT adapter checkpoint but the corresponding PEFT config is not known for whatever reason. To inject the PEFT layers for this checkpoint, you would usually have to reverse-engineer the corresponding PEFT config, most notably the `target_modules` argument, based on the `state_dict` from the checkpoint. This can be cumbersome and error prone. To avoid this, it is also possible to call [`inject_adapter_in_model`] and pass the loaded `state_dict` as an argument:
+
+```python
+from safetensors.torch import load_file
+
+model = ...
+state_dict = load_file(<path-to-safetensors-file>)
+lora_config = LoraConfig(...)
+model = inject_adapter_in_model(lora_config, model, state_dict=state_dict)
+```
+
+In this case, PEFT will use the `state_dict` as reference for which layers to target instead of using the PEFT config. As a user, you don't have to set the exact `target_modules` of the PEFT config for this to work. However, you should still pass a PEFT config of the right type, in this example `LoraConfig`, you can leave the `target_modules` as `None`.
+
+Be aware that this still only creates the uninitialized PEFT layers, the values from the `state_dict` are not used to populate the model weights. To populate the weights, proceed with calling [`set_peft_model_state_dict`] as described below.
+
+⚠️ Note that if there is a mismatch between what is configured in the PEFT config and what is found in the `state_dict`, PEFT will warn you about this. You can ignore the warning if you know that the PEFT config is not correctly specified.
+
+> [!WARNING]
+> If the original PEFT adapters was using `target_parameters` instead of `target_modules`, injecting from a `state_dict` will not work correctly. In this case, it is mandatory to use the correct PEFT config for injection.
+
+## Saving the model
+
+To only save the adapter, use the [`get_peft_model_state_dict`] function:
+
+```python
+from peft import get_peft_model_state_dict
+
+peft_state_dict = get_peft_model_state_dict(model)
+print(peft_state_dict)
+```
+
+Otherwise, `model.state_dict()` returns the full state dict of the model.
+
+## Loading the model
+
+After loading the saved `state_dict`, it can be applied using the [`set_peft_model_state_dict`] function:
+
+```python
+from peft import set_peft_model_state_dict
+
+model = DummyModel()
+model = inject_adapter_in_model(lora_config, model)
+outcome = set_peft_model_state_dict(model, peft_state_dict)
+# check that there were no wrong keys
+print(outcome.unexpected_keys)
+```
+
+If injecting the adapter is slow or you need to load a large number of adapters, you may use an optimization that allows to create an "empty" adapter on meta device and only fills the weights with real weights when the [`set_peft_model_state_dict`] is called. To do this, pass `low_cpu_mem_usage=True` to both [`inject_adapter_in_model`] and [`set_peft_model_state_dict`].
+
+```python
+model = DummyModel()
+model = inject_adapter_in_model(lora_config, model, low_cpu_mem_usage=True)
+
+print(model.linear.lora_A["default"].weight.device.type == "meta")  # should be True
+set_peft_model_state_dict(model, peft_state_dict, low_cpu_mem_usage=True)
+print(model.linear.lora_A["default"].weight.device.type == "cpu")  # should be True
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/mixed_models.md

@@ -0,0 +1,37 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+-->
+
+# Mixed adapter types
+
+Normally, it isn't possible to mix different adapter types in 🤗 PEFT. You can create a PEFT model with two different LoRA adapters (which can have different config options), but it is not possible to combine a LoRA and LoHa adapter. With [`PeftMixedModel`] however, this works as long as the adapter types are compatible. The main purpose of allowing mixed adapter types is to combine trained adapters for inference. While it is possible to train a mixed adapter model, this has not been tested and is not recommended.
+
+To load different adapter types into a PEFT model, use [`PeftMixedModel`] instead of [`PeftModel`]:
+
+```py
+from peft import PeftMixedModel
+
+base_model = ...  # load the base model, e.g. from transformers
+# load first adapter, which will be called "default"
+peft_model = PeftMixedModel.from_pretrained(base_model, <path_to_adapter1>)
+peft_model.load_adapter(<path_to_adapter2>, adapter_name="other")
+peft_model.set_adapter(["default", "other"])
+```
+
+The [`~PeftMixedModel.set_adapter`] method is necessary to activate both adapters, otherwise only the first adapter would be active. You can keep adding more adapters by calling [`~PeftModel.add_adapter`] repeatedly.
+
+[`PeftMixedModel`] does not support saving and loading mixed adapters. The adapters should already be trained, and loading the model requires a script to be run each time.
+
+## Tips
+
+- Not all adapter types can be combined. See [`peft.tuners.mixed.COMPATIBLE_TUNER_TYPES`](https://github.com/huggingface/peft/blob/1c1c7fdaa6e6abaa53939b865dee1eded82ad032/src/peft/tuners/mixed/model.py#L35) for a list of compatible types. An error will be raised if you try to combine incompatible adapter types.
+- It is possible to mix multiple adapters of the same type which can be useful for combining adapters with very different configs.
+- If you want to combine a lot of different adapters, the most performant way to do it is to consecutively add the same adapter types. For example, add LoRA1, LoRA2, LoHa1, LoHa2 in this order, instead of LoRA1, LoHa1, LoRA2, and LoHa2. While the order can affect the output, there is no inherently *best* order, so it is best to choose the fastest one.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/model_merging.md

@@ -0,0 +1,164 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Model merging
+
+Training a model for each task can be costly, take up storage space, and the models aren't able to learn new information to improve their performance. Multitask learning can overcome some of these limitations by training a model to learn several tasks, but it is expensive to train and designing a dataset for it is challenging. *Model merging* offers a solution to these challenges by combining multiple pretrained models into one model, giving it the combined abilities of each individual model without any additional training.
+
+PEFT provides several methods for merging models like a linear or SVD combination. This guide focuses on two methods that are more efficient for merging LoRA adapters by eliminating redundant parameters:
+
+* [TIES](https://hf.co/papers/2306.01708) - TrIm, Elect, and Merge (TIES) is a three-step method for merging models. First, redundant parameters are trimmed, then conflicting signs are resolved into an aggregated vector, and finally the parameters whose signs are the same as the aggregate sign are averaged. This method takes into account that some values (redundant and sign disagreement) can degrade performance in the merged model.
+* [DARE](https://hf.co/papers/2311.03099) - Drop And REscale is a method that can be used to prepare for other model merging methods like TIES. It works by randomly dropping parameters according to a drop rate and rescaling the remaining parameters. This helps to reduce the number of redundant and potentially interfering parameters among multiple models.
+
+Models are merged with the [`~LoraModel.add_weighted_adapter`] method, and the specific model merging method is specified in the `combination_type` parameter.
+
+## Merge method
+
+With TIES and DARE, merging is enabled by setting `combination_type` and `density` to a value of the weights to keep from the individual models. For example, let's merge three finetuned [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) models: [tinyllama_lora_nobots](https://huggingface.co/smangrul/tinyllama_lora_norobots), [tinyllama_lora_sql](https://huggingface.co/smangrul/tinyllama_lora_sql), and [tinyllama_lora_adcopy](https://huggingface.co/smangrul/tinyllama_lora_adcopy).
+
+<Tip warninig={true}>
+
+When you're attempting to merge fully trained models with TIES, you should be aware of any special tokens each model may have added to the embedding layer which are not a part of the original checkpoint's vocabulary. This may cause an issue because each model may have added a special token to the same embedding position. If this is the case, you should use the [`~transformers.PreTrainedModel.resize_token_embeddings`] method to avoid merging the special tokens at the same embedding index.
+
+<br>
+
+This shouldn't be an issue if you're only merging LoRA adapters trained from the same base model.
+
+</Tip>
+
+Load a base model and can use the [`~PeftModel.load_adapter`] method to load and assign each adapter a name:
+
+```py
+from peft import PeftConfig, PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+config = PeftConfig.from_pretrained("smangrul/tinyllama_lora_norobots")
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, load_in_4bit=True, device_map="auto").eval()
+tokenizer = AutoTokenizer.from_pretrained("smangrul/tinyllama_lora_norobots")
+
+model.config.vocab_size = 32005
+model.resize_token_embeddings(32005)
+
+model = PeftModel.from_pretrained(model, "smangrul/tinyllama_lora_norobots", adapter_name="norobots")
+_ = model.load_adapter("smangrul/tinyllama_lora_sql", adapter_name="sql")
+_ = model.load_adapter("smangrul/tinyllama_lora_adcopy", adapter_name="adcopy")
+```
+
+Set the adapters, weights, `adapter_name`, `combination_type`, and `density` with the [`~LoraModel.add_weighted_adapter`] method.
+
+<hfoptions id="merge-method">
+<hfoption id="TIES">
+
+Weight values greater than `1.0` typically produce better results because they preserve the correct scale. A good default starting value for the weights is to set all values to `1.0`.
+
+```py
+adapters = ["norobots", "adcopy", "sql"]
+weights = [2.0, 1.0, 1.0]
+adapter_name = "merge"
+density = 0.2
+model.add_weighted_adapter(adapters, weights, adapter_name, combination_type="ties", density=density)
+```
+
+</hfoption>
+<hfoption id="DARE">
+
+```py
+adapters = ["norobots", "adcopy", "sql"]
+weights = [2.0, 0.3, 0.7]
+adapter_name = "merge"
+density = 0.2
+model.add_weighted_adapter(adapters, weights, adapter_name, combination_type="dare_ties", density=density)
+```
+
+</hfoption>
+</hfoptions>
+
+Set the newly merged model as the active model with the [`~LoraModel.set_adapter`] method.
+
+```py
+model.set_adapter("merge")
+```
+
+Now you can use the merged model as an instruction-tuned model to write ad copy or SQL queries!
+
+<hfoptions id="ties">
+<hfoption id="instruct">
+
+```py
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+messages = [
+    {"role": "user", "content": "Write an essay about Generative AI."},
+]
+text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+outputs = model.generate(**inputs, max_new_tokens=256, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0]))
+```
+
+</hfoption>
+<hfoption id="ad copy">
+
+```py
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+messages = [
+    {"role": "system", "content": "Create a text ad given the following product and description."},
+    {"role": "user", "content": "Product: Sony PS5 PlayStation Console\nDescription: The PS5 console unleashes new gaming possibilities that you never anticipated."},
+]
+text = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(text, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+outputs = model.generate(**inputs, max_new_tokens=128, do_sample=True, top_p=0.95, temperature=0.2, repetition_penalty=1.2, eos_token_id=tokenizer.eos_token_id)
+print(tokenizer.decode(outputs[0]))
+```
+
+</hfoption>
+<hfoption id="SQL">
+
+```py
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+
+text = """Table: 2-11365528-2
+Columns: ['Team', 'Head Coach', 'President', 'Home Ground', 'Location']
+Natural Query: Who is the Head Coach of the team whose President is Mario Volarevic?
+SQL Query:"""
+
+inputs = tokenizer(text, return_tensors="pt")
+inputs = {k: v.to(device) for k, v in inputs.items()}
+outputs = model.generate(**inputs, max_new_tokens=64, repetition_penalty=1.1, eos_token_id=tokenizer("</s>").input_ids[-1])
+print(tokenizer.decode(outputs[0]))
+```
+
+</hfoption>
+</hfoptions>
+
+
+## Merging (IA)³ Models
+The (IA)³ models facilitate linear merging of adapters. To merge adapters in an (IA)³ model, utilize the `add_weighted_adapter` method from the `IA3Model` class. This method is analogous to the `add_weighted_adapter` method used in `LoraModel`, with the key difference being the absence of the `combination_type` parameter. For example, to merge three (IA)³ adapters into a PEFT model, you would proceed as follows:
+
+```py
+adapters = ["adapter1", "adapter2", "adapter3"]
+weights = [0.4, 0.3, 0.3]
+adapter_name = "merge"
+model.add_weighted_adapter(adapters, weights, adapter_name)
+```
+
+It is recommended that the weights sum to 1.0 to preserve the scale of the model. The merged model can then be set as the active model using the `set_adapter` method:
+
+```py
+model.set_adapter("merge")
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/quantization.md

@@ -0,0 +1,356 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Quantization
+
+Quantization represents data with fewer bits, making it a useful technique for reducing memory-usage and accelerating inference especially when it comes to large language models (LLMs). There are several ways to quantize a model including:
+
+* optimizing which model weights are quantized with the [AWQ](https://hf.co/papers/2306.00978) algorithm
+* independently quantizing each row of a weight matrix with the [GPTQ](https://hf.co/papers/2210.17323) algorithm
+* quantizing to 8-bit and 4-bit precision with the [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) library
+* quantizing to as low as 2-bit precision with the [AQLM](https://huggingface.co/papers/2401.06118) algorithm
+
+However, after a model is quantized it isn't typically further trained for downstream tasks because training can be unstable due to the lower precision of the weights and activations. But since PEFT methods only add *extra* trainable parameters, this allows you to train a quantized model with a PEFT adapter on top! Combining quantization with PEFT can be a good strategy for training even the largest models on a single GPU. For example, [QLoRA](https://hf.co/papers/2305.14314) is a method that quantizes a model to 4-bits and then trains it with LoRA. This method allows you to finetune a 65B parameter model on a single 48GB GPU!
+
+In this guide, you'll see how to quantize a model to 4-bits and train it with LoRA.
+
+## Quantize a model
+
+[bitsandbytes](https://github.com/TimDettmers/bitsandbytes) is a quantization library with a Transformers integration. With this integration, you can quantize a model to 8 or 4-bits and enable many other options by configuring the [`~transformers.BitsAndBytesConfig`] class. For example, you can:
+
+* set `load_in_4bit=True` to quantize the model to 4-bits when you load it
+* set `bnb_4bit_quant_type="nf4"` to use a special 4-bit data type for weights initialized from a normal distribution
+* set `bnb_4bit_use_double_quant=True` to use a nested quantization scheme to quantize the already quantized weights
+* set `bnb_4bit_compute_dtype=torch.bfloat16` to use bfloat16 for faster computation
+
+```py
+import torch
+from transformers import BitsAndBytesConfig
+
+config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+```
+
+Pass the `config` to the [`~transformers.AutoModelForCausalLM.from_pretrained`] method.
+
+```py
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)
+```
+
+Next, you should call the [`~peft.utils.prepare_model_for_kbit_training`] function to preprocess the quantized model for training.
+
+```py
+from peft import prepare_model_for_kbit_training
+
+model = prepare_model_for_kbit_training(model)
+```
+
+Now that the quantized model is ready, let's set up a configuration.
+
+## LoraConfig
+
+Create a [`LoraConfig`] with the following parameters (or choose your own):
+
+```py
+from peft import LoraConfig
+
+config = LoraConfig(
+    r=16,
+    lora_alpha=8,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+```
+
+Then use the [`get_peft_model`] function to create a [`PeftModel`] from the quantized model and configuration.
+
+```py
+from peft import get_peft_model
+
+model = get_peft_model(model, config)
+```
+
+You're all set for training with whichever training method you prefer!
+
+### LoftQ initialization
+
+[LoftQ](https://hf.co/papers/2310.08659) initializes LoRA weights such that the quantization error is minimized, and it can improve performance when training quantized models. To get started, follow [these instructions](https://github.com/huggingface/peft/tree/main/examples/loftq_finetuning).
+
+In general, for LoftQ to work best, it is recommended to target as many layers with LoRA as possible, since those not targeted cannot have LoftQ applied. This means that passing `LoraConfig(..., target_modules="all-linear")` will most likely give the best results. Also, you should use `nf4` as quant type in your quantization config when using 4bit quantization, i.e. `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")`.
+
+In general, for LoftQ to work best, it is recommended to target as many layers with LoRA as possible, since those not targeted cannot have LoftQ applied. This means that passing `LoraConfig(..., target_modules="all-linear")` will most likely give the best results. Also, you should use `nf4` as quant type in your quantization config when using 4bit quantization, i.e. `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")`.
+
+There are currently two intended ways of applying LoftQ:
+
+1. load the non-quantized base model, apply LoftQ with your intended quantization level (e.g., `bits=4` for nf4) and
+   save the resulting adapter. Then quantize the base model using, for example,
+   `BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4")` and load the LoftQ-initialized adapter on top
+2. load the quantized base model, initialize a PEFT model with LoRA (no loftq) and use `replace_lora_weights_loftq` to
+   apply LoftQ initialization by streaming the reference weights of the non-quantized base model from the weights file
+   (explained further in the next section)
+
+#### Using `replace_lora_weights_loftq` for on-the-fly LoftQ application
+
+An easier but more limited way to apply LoftQ initialization is to use the convenience function `replace_lora_weights_loftq`. This takes the quantized PEFT model as input and replaces the LoRA weights in-place with their LoftQ-initialized counterparts.
+
+```python
+from peft import replace_lora_weights_loftq
+from transformers import BitsAndBytesConfig
+
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, ...)
+base_model = AutoModelForCausalLM.from_pretrained(..., quantization_config=bnb_config)
+# note: don't pass init_lora_weights="loftq" or loftq_config!
+lora_config = LoraConfig(task_type="CAUSAL_LM")
+peft_model = get_peft_model(base_model, lora_config)
+replace_lora_weights_loftq(peft_model)
+```
+
+`replace_lora_weights_loftq` also allows you to pass a `callback` argument to give you more control over which layers should be modified or not, which empirically can improve the results quite a lot. To see a more elaborate example of this, check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/loftq_finetuning/LoftQ_weight_replacement.ipynb).
+
+`replace_lora_weights_loftq` implements only one iteration step of LoftQ. This means that only the LoRA weights are updated, instead of iteratively updating LoRA weights and quantized base model weights. This may lead to lower performance but has the advantage that we can use the original quantized weights derived from the base model, instead of having to keep an extra copy of modified quantized weights. Whether this tradeoff is worthwhile depends on the use case.
+
+At the moment, `replace_lora_weights_loftq` has these additional limitations:
+
+- Model files must be stored as a `safetensors` file.
+- Only bitsandbytes 4bit quantization is supported.
+
+### QLoRA-style training
+
+QLoRA adds trainable weights to all the linear layers in the transformer architecture. Since the attribute names for these linear layers can vary across architectures, set `target_modules` to `"all-linear"` to add LoRA to all the linear layers:
+
+```py
+config = LoraConfig(target_modules="all-linear", ...)
+```
+
+## GPTQ quantization
+
+You can learn more about GPTQ-based `[2, 3, 4, 8]` bit quantization at [GPT-QModel](https://github.com/ModelCloud/GPTQModel) and in the Transformers [GPTQ](https://huggingface.co/docs/transformers/quantization/gptq) documentation. PEFT supports GPTQ post-training through GPT-QModel.
+
+```bash
+# GPT-QModel install
+pip install "gptqmodel>=7.0.0"
+```
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer, GPTQConfig
+
+model_id = "facebook/opt-125m"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+gptq_config = GPTQConfig(bits=4, group_size=128, dataset="wikitext2", tokenizer=tokenizer)
+
+quantized_model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", quantization_config=gptq_config)
+
+# save quantized model
+quantized_model.save_pretrained("./opt-125m-gptq")
+tokenizer.save_pretrained("./opt-125m-gptq")
+```
+
+Once quantized, you can post-train GPTQ models with PEFT APIs.
+
+## AQLM quantization
+
+Additive Quantization of Language Models ([AQLM](https://huggingface.co/papers/2401.06118)) is a Large Language Models compression method. It quantizes multiple weights together and takes advantage of interdependencies between them. AQLM represents groups of 8-16 weights as a sum of multiple vector codes. This allows it to compress models down to as low as 2-bit with considerably low accuracy losses.
+
+Since the AQLM quantization process is computationally expensive, the use of prequantized models is recommended. A partial list of available models can be found in the official aqlm [repository](https://github.com/Vahe1994/AQLM).
+
+The models support LoRA adapter tuning. To tune the quantized model you'll need to install the `aqlm` inference library: `pip install aqlm>=1.0.2`. Finetuned LoRA adapters shall be saved separately, as merging them with AQLM quantized weights is not possible.
+
+```py
+quantized_model = AutoModelForCausalLM.from_pretrained(
+    "BlackSamorez/Mixtral-8x7b-AQLM-2Bit-1x16-hf-test-dispatch",
+    dtype="auto", device_map="auto", low_cpu_mem_usage=True,
+)
+
+peft_config = LoraConfig(...)
+
+quantized_model = get_peft_model(quantized_model, peft_config)
+```
+
+You can refer to the [Google Colab](https://colab.research.google.com/drive/12GTp1FCj5_0SnnNQH18h_2XFh9vS_guX?usp=sharing) example for an overview of AQLM+LoRA finetuning.
+
+## EETQ quantization
+
+You can also perform LoRA fine-tuning on EETQ quantized models. [EETQ](https://github.com/NetEase-FuXi/EETQ) package offers simple and efficient way to perform 8-bit quantization, which is claimed to be faster than the `LLM.int8()` algorithm. First, make sure that you have a transformers version that is compatible with EETQ (e.g. by installing it from latest pypi or from source).
+
+```py
+import torch
+from transformers import EetqConfig
+
+config = EetqConfig("int8")
+```
+
+Pass the `config` to the [`~transformers.AutoModelForCausalLM.from_pretrained`] method.
+
+```py
+from transformers import AutoModelForCausalLM
+
+model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", quantization_config=config)
+```
+
+and create a `LoraConfig` and pass it to `get_peft_model`:
+
+```py
+from peft import LoraConfig, get_peft_model
+
+config = LoraConfig(
+    r=16,
+    lora_alpha=8,
+    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
+    lora_dropout=0.05,
+    bias="none",
+    task_type="CAUSAL_LM"
+)
+
+model = get_peft_model(model, config)
+```
+
+## HQQ quantization
+
+The models that are quantized using Half-Quadratic Quantization of Large Machine Learning Models ([HQQ](https://mobiusml.github.io/hqq_blog/)) support LoRA adapter tuning. To tune the quantized model, you'll need to install the `hqq` library with: `pip install hqq`.
+
+```python
+from hqq.engine.hf import HQQModelForCausalLM
+
+device = torch.accelerator.current_accelerator().type if hasattr(torch, "accelerator") else "cuda"
+
+quantized_model = HQQModelForCausalLM.from_quantized(save_dir_or_hfhub, device=device)
+peft_config = LoraConfig(...)
+quantized_model = get_peft_model(quantized_model, peft_config)
+```
+
+Or using transformers version that is compatible with HQQ (e.g. by installing it from latest pypi or from source).
+
+```python
+from transformers import HqqConfig, AutoModelForCausalLM
+
+quant_config = HqqConfig(nbits=4, group_size=64)
+quantized_model = AutoModelForCausalLM.from_pretrained(save_dir_or_hfhub, device_map=device_map, quantization_config=quant_config)
+peft_config = LoraConfig(...)
+quantized_model = get_peft_model(quantized_model, peft_config)
+```
+
+## torchao (PyTorch Architecture Optimization)
+
+PEFT supports models quantized with [torchao](https://github.com/pytorch/ao) ("ao") for int8 quantization.
+
+```python
+from peft import LoraConfig, get_peft_model
+from transformers import AutoModelForCausalLM, TorchAoConfig
+from torchao.quantization import Int8WeightOnlyConfig
+
+model_id = ...
+quantization_config = TorchAoConfig(quant_type=Int8WeightOnlyConfig())
+base_model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=quantization_config)
+peft_config = LoraConfig(...)
+model = get_peft_model(base_model, peft_config)
+```
+
+### Caveats:
+
+- Use the most recent versions of torchao (>= v0.4.0) and transformers (> 4.42).
+- Only linear layers are currently supported.
+- `quant_type = "int4_weight_only"` is currently not supported.
+- `NF4` is not implemented in transformers as of yet and is thus also not supported.
+- DoRA only works with `quant_type = "int8_weight_only"` at the moment.
+- There is explicit support for torchao when used with LoRA. However, when torchao quantizes a layer, its class does not change, only the type of the underlying tensor. For this reason, PEFT methods other than LoRA will generally also work with torchao, even if not explicitly supported. Be aware, however, that **merging only works correctly with LoRA and with `quant_type = "int8_weight_only"`**. If you use a different PEFT method or dtype, merging will likely result in an error, and even it doesn't, the results will still be incorrect.
+
+## INC quantization
+
+Intel Neural Compressor ([INC](https://github.com/intel/neural-compressor)) enables model quantization for various devices,
+including Intel Gaudi accelerators (also known as HPU devices). You can perform LoRA fine-tuning on models that have been
+quantized using INC. To use INC with PyTorch models, install the library with: `pip install neural-compressor[pt]`.
+Quantizing a model to FP8 precision for HPU devices can be done with the following single-step quantization workflow:
+
+```python
+import torch
+from neural_compressor.torch.quantization import FP8Config, convert, finalize_calibration, prepare
+quant_configs = {
+    ...
+}
+config = FP8Config(**quant_configs)
+```
+
+Pass the config to the `prepare` method, run inference to gather calibration stats, and call `finalize_calibration`
+and `convert` methods to quantize model to FP8 precision:
+
+```python
+model = prepare(model, config)
+# Run inference to collect calibration statistics
+...
+# Finalize calibration and convert the model to FP8 precision
+finalize_calibration(model)
+model = convert(model)
+# Load PEFT LoRA adapter as usual
+...
+```
+
+An example demonstrating how to load a PEFT LoRA adapter into an INC-quantized FLUX text-to-image model for HPU
+devices is provided [here](https://github.com/huggingface/peft/blob/main/examples/stable_diffusion/inc_flux_lora_hpu.py).
+
+
+### Caveats:
+
+- `merge()` and `unmerge()` methods are currently not supported for INC-quantized models.
+- Currently, only **Linear** INC-quantized layers are supported when loading PEFT adapters.
+
+## Other Supported PEFT Methods
+
+Besides LoRA, the following PEFT methods also support quantization:
+
+- **VeRA** (supports bitsandbytes quantization)
+- **AdaLoRA** (supports both bitsandbytes and GPTQ quantization)
+- **(IA)³** (supports bitsandbytes quantization)
+
+## Transformer Engine (TE) LoRA
+
+PEFT supports LoRA adapters on top of [NVIDIA Transformer Engine](https://docs.nvidia.com/deeplearning/transformer-engine/) layers (`te.pytorch.Linear`, `te.pytorch.LayerNormLinear`, and `te.pytorch.LayerNormMLP`). TE layers use FP8 and fused kernels to accelerate transformer training, and attaching LoRA adapters lets you parameter-efficiently fine-tune TE-accelerated models.
+
+```python
+from peft import LoraConfig, get_peft_model
+
+config = LoraConfig(
+    r=8,
+    lora_alpha=16,
+    target_modules=["q_proj", "v_proj"],
+    task_type="TOKEN_CLS",
+)
+model = get_peft_model(te_model, config)
+```
+
+When Transformer Engine is installed, PEFT automatically dispatches matching layers to the `TeLinear` adapter — no extra configuration is needed.
+
+### Caveats
+
+- `merge()` and `unmerge()` are not yet supported for TE layers.
+- DoRA is not supported with TE layers.
+
+A complete ESM2 token-classification example is available under `examples/lora_finetuning_transformer_engine/`.
+
+## Next steps
+
+If you're interested in learning more about quantization, the following may be helpful:
+
+* Learn more details about QLoRA and check out some benchmarks on its impact in the [Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA](https://huggingface.co/blog/4bit-transformers-bitsandbytes) blog post.
+* Read more about different quantization schemes in the Transformers [Quantization](https://hf.co/docs/transformers/main/quantization) guide.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/torch_compile.md

@@ -0,0 +1,70 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# torch.compile
+
+In PEFT, [torch.compile](https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html) works for some but not all features. The reason why it won't always work is because PEFT is highly dynamic in certain places (loading and switching between multiple adapters, for instance), which can cause trouble for `torch.compile`. In other places, `torch.compile` may work, but won't be as fast as expected because of graph breaks.
+
+If you don't see an error, it doesn't necessarily mean that `torch.compile` worked correctly. It might give you an output, but the output is incorrect. This guide describes what works with `torch.compile` and what doesn't. For your own testing, we recommend using the latest PyTorch version, as `torch.compile` is constantly being improved.
+
+> [!TIP]
+> Unless indicated otherwise, the default `torch.compile` settings were used.
+
+## Training and inference with `torch.compile`
+
+These features **work** with `torch.compile`. Everything listed below was tested with a causal LM:
+
+- Training with `Trainer` from 🤗 transformers
+- Training with a custom PyTorch loop
+- Inference
+- Generation
+
+The following adapters were tested successfully:
+
+- AdaLoRA
+- BOFT
+- IA³
+- Layer Norm Tuning
+- LoHa
+- LoKr
+- LoRA
+- LoRA + DoRA
+- LoRA applied to embedding layers
+- OFT
+- VeRA
+- HRA
+
+## Advanced PEFT features with `torch.compile`
+
+Below are some of the more advanced PEFT features that **work**. They were all tested with LoRA.
+
+- `modules_to_save` (i.e. `config = LoraConfig(..., modules_to_save=...)`)
+- Merging adapters (one or multiple)
+- Merging multiple adapters into one adapter (i.e. calling `model.add_weighted_adapter(...)`)
+- Using PEFT adapters with quantization (bitsandbytes)
+- Disabling adapters (i.e. using `with model.disable_adapter()`)
+- Unloading (i.e. calling `model.merge_and_unload()`)
+- Mixed adapter batches (i.e. calling `model(batch, adapter_names=["__base__", "default", "other", ...])`)
+- Inference with multiple adapters (i.e. using `model.add_adapter` or `model.load_adapter` to load more than 1 adapter); for this, only call `torch.compile` _after_ loading all adapters
+
+Generally, we can expect that if a feature works correctly with LoRA and is also supported by other adapter types, it should also work for that adapter type.
+
+## Test cases
+
+All the use cases listed above are tested inside of [`peft/tests/test_torch_compile.py`](https://github.com/huggingface/peft/blob/main/tests/test_torch_compile.py). If you want to check in more detail how we tested a certain feature, please go to that file and check the test that corresponds to your use case.
+
+> [!TIP]
+> If you have another use case where you know that `torch.compile` does or does not work with PEFT, please contribute by letting us know or by opening a PR to add this use case to the covered test cases.

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/developer_guides/troubleshooting.md

@@ -0,0 +1,458 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Troubleshooting
+
+If you encounter any issue when using PEFT, please check the following list of common issues and their solutions.
+
+## Examples don't work
+
+Examples often rely on the most recent package versions, so please ensure they're up-to-date. In particular, check the following package versions:
+
+- `peft`
+- `transformers`
+- `accelerate`
+- `torch`
+
+In general, you can update the package version by running this command inside your Python environment:
+
+```bash
+python -m pip install -U <package_name>
+```
+
+Installing PEFT from source is useful for keeping up with the latest developments:
+
+```bash
+python -m pip install git+https://github.com/huggingface/peft
+```
+
+## Dtype-related issues
+
+### ValueError: Attempting to unscale FP16 gradients
+
+This error probably occurred because the model was loaded with `dtype=torch.float16` and then used in an automatic mixed precision (AMP) context, e.g. by setting `fp16=True` in the [`~transformers.Trainer`] class from 🤗 Transformers. The reason is that when using AMP, trainable weights should never use fp16. To make this work without loading the whole model in fp32, add the following to your code:
+
+```python
+peft_model = get_peft_model(...)
+
+# add this:
+for param in model.parameters():
+    if param.requires_grad:
+        param.data = param.data.float()
+
+# proceed as usual
+trainer = Trainer(model=peft_model, fp16=True, ...)
+trainer.train()
+```
+
+Alternatively, you can use the [`~utils.cast_mixed_precision_params`] function to correctly cast the weights:
+
+```python
+from peft import cast_mixed_precision_params
+
+peft_model = get_peft_model(...)
+cast_mixed_precision_params(peft_model, dtype=torch.float16)
+
+# proceed as usual
+trainer = Trainer(model=peft_model, fp16=True, ...)
+trainer.train()
+```
+
+> [!TIP]
+> Starting from PEFT version v0.12.0, PEFT automatically promotes the dtype of adapter weights from `torch.float16` and `torch.bfloat16` to `torch.float32` where appropriate. To _prevent_ this behavior, you can pass `autocast_adapter_dtype=False` to [`~get_peft_model`], to [`~PeftModel.from_pretrained`], and to [`~PeftModel.load_adapter`].
+
+### Selecting the dtype of the adapter
+
+Most PEFT methods, like LoRA, work by adding trainable adapter weights. By default, those weights are stored in float32 dtype (fp32), i.e. at a relatively high precision. Therefore, even if the base model is loaded in float16 (fp16) or bfloat16 (bf16), the adapter weights are float32. When the adapter results are calculated during the forward pass, the input will typically be in the dtype of the base model, thus it will be upcast to float32 if necessary, then cast back to the original dtype.
+
+If you prefer to have the adapter weights in the lower precision of the base model, i.e. in float16 or bfloat16, you can pass `autocast_adapter_dtype=False` when creating the model ([`~get_peft_model`]) or loading the model ([`~PeftModel.from_pretrained`]). There are some advantages and disadvantages to this:
+
+Advantages of half precision adapter:
+- computation slightly faster
+- slightly less memory
+- smaller file size of checkpoint (half the size)
+
+Disadvantages of half precision adapter:
+- slightly worse loss
+- higher risk of overflow or underflow
+
+Note that for most use cases, overall runtime and memory cost will be determined by the size of the base model and by the dataset, while the dtype of the PEFT adapter will only have a small impact.
+
+## Bad results from a loaded PEFT model
+
+There can be several reasons for getting a poor result from a loaded PEFT model which are listed below. If you're still unable to troubleshoot the problem, see if anyone else had a similar [issue](https://github.com/huggingface/peft/issues) on GitHub, and if you can't find any, open a new issue.
+
+When opening an issue, it helps a lot if you provide a minimal code example that reproduces the issue. Also, please report if the loaded model performs at the same level as the model did before fine-tuning, if it performs at a random level, or if it is only slightly worse than expected. This information helps us identify the problem more quickly.
+
+### Random deviations
+
+If your model outputs are not exactly the same as previous runs, there could be an issue with random elements. For example:
+
+1. please ensure it is in `.eval()` mode, which is important, for instance, if the model uses dropout
+2. if you use [`~transformers.GenerationMixin.generate`] on a language model, there could be random sampling, so obtaining the same result requires setting a random seed
+3. if you used quantization and merged the weights, small deviations are expected due to rounding errors
+
+### Incorrectly loaded model
+
+Please ensure that you load the model correctly. A common error is trying to load a _trained_ model with [`get_peft_model`] which is incorrect. Instead, the loading code should look like this:
+
+```python
+from peft import PeftModel, PeftConfig
+
+base_model = ...  # to load the base model, use the same code as when you trained it
+config = PeftConfig.from_pretrained(peft_model_id)
+peft_model = PeftModel.from_pretrained(base_model, peft_model_id)
+```
+
+### Randomly initialized layers
+
+For some tasks, it is important to correctly configure `modules_to_save` in the config to account for randomly initialized layers. 
+
+As an example, this is necessary if you use LoRA to fine-tune a language model for sequence classification because 🤗 Transformers adds a randomly initialized classification head on top of the model. If you do not add this layer to `modules_to_save`, the classification head won't be saved. The next time you load the model, you'll get a _different_ randomly initialized classification head, resulting in completely different results.
+
+PEFT tries to correctly guess the `modules_to_save` if you provide the `task_type` argument in the config. This should work for transformers models that follow the standard naming scheme. It is always a good idea to double check though because we can't guarantee all models follow the naming scheme.
+
+When you load a transformers model that has randomly initialized layers, you should see a warning along the lines of:
+
+```
+Some weights of <MODEL> were not initialized from the model checkpoint at <ID> and are newly initialized: [<LAYER_NAMES>].
+You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+```
+
+The mentioned layers should be added to `modules_to_save` in the config to avoid the described problem.
+
+> [!TIP]
+> As an example, when loading a model that is using the DeBERTa architecture for sequence classification, you'll see a warning that the following weights are newly initialized: `['classifier.bias', 'classifier.weight', 'pooler.dense.bias', 'pooler.dense.weight']`. From this, it follows that the `classifier` and `pooler` layers should be added to: `modules_to_save=["classifier", "pooler"]`.
+
+### Extending the vocabulary
+
+For many language fine-tuning tasks, extending the model's vocabulary is necessary since new tokens are being introduced. This requires extending the embedding layer to account for the new tokens and, depending on the fine-tuning method, also storing the embedding layer in addition to the adapter weights when saving the adapter. There are a few ways of achieving this ordered by parameter effectiveness:
+
+- [trainable tokens](../package_reference/trainable_tokens), train only the specified tokens, optionally store only the updated values
+- training an adapter on the embedding matrix, optionally store only the updated values
+- full-finetuning of the embedding layer
+
+#### Using trainable tokens
+
+Let's start with trainable tokens, in this case its [LoRA integration](../developer_guides/lora#efficiently-train-tokens-alongside-lora).  If you're interested in only training the new embeddings and nothing else, refer to the [standalone documentation](../package_reference/trainable_tokens).
+
+To enable selective token training of the embedding layer, you'll need to supply the token ids of your newly added tokens via the `trainable_token_indices` parameter.  Optionally you can specify which layer to target if there is more than one embedding layer. For a Mistral model this could look like this:
+
+```python
+new_tokens = ['<think>', '</think>']
+tokenizer.add_tokens(new_tokens)
+base_model.resize_token_embeddings(len(tokenizer))
+
+lora_config = LoraConfig(
+    ...,
+    trainable_token_indices={'embed_tokens': tokenizer.convert_tokens_to_ids(new_tokens)},
+)
+```
+
+If your model uses tied weights (such as the `lm_head`), trainable tokens will try to resolve those and keep them updated as well, so in that case there should be no need for adding `modules_to_save=["lm_head"]`. This only works if the model uses the Transformers convention for tying weights.
+
+Saving the model with `model.save_pretrained` may save the full embedding matrix instead of
+only the difference as a precaution because the embedding matrix was resized. To save space you can disable this behavior by setting `save_embedding_layers=False` when calling `save_pretrained`. This is safe to do as long as you don't modify the embedding matrix through other means as well, as such changes will be not tracked by trainable tokens.
+
+#### Using an adapter, e.g. LoRA
+
+Prepare the embedding layer by adding it to the `target_modules` of your adapter config. For example, the Mistral config could look like this:
+
+```python
+config = LoraConfig(..., target_modules=["embed_tokens", "lm_head", "q_proj", "v_proj"])
+```
+
+Once added to `target_modules`, PEFT automatically stores the embedding layer when saving the adapter if the model has the [`~transformers.PreTrainedModel.get_input_embeddings`] and [`~transformers.PreTrainedModel.get_output_embeddings`]. This is generally the case for Transformers models.
+
+If the model's embedding layer doesn't follow the Transformer's naming scheme but nevertheless implements `get_input_embeddings`, you can still save it by manually passing `save_embedding_layers=True` when saving the adapter:
+
+```python
+model = get_peft_model(...)
+# train the model
+model.save_pretrained("my_adapter", save_embedding_layers=True)
+```
+
+For inference, load the base model first and resize it the same way you did before you trained the model. After you've resized the base model, you can load the PEFT checkpoint.
+
+For a complete example, please check out [this notebook](https://github.com/huggingface/peft/blob/main/examples/causal_language_modeling/peft_lora_clm_with_additional_tokens.ipynb).
+
+#### Full fine-tuning
+
+Full fine-tuning is more costly in terms of VRAM or storage space but if all else fails, you can fall back to this and see if it works for you. Achieve it by adding the name of the embedding layer to `modules_to_save`. Note that you need to add tied layers as well, e.g. `lm_head`. Example for a Mistral model with LoRA:
+
+```python
+config = LoraConfig(..., modules_to_save=["embed_tokens", "lm_head"], target_modules=["q_proj", "v_proj"])
+```
+
+### Getting a warning about "weights not being initialized from the model checkpoint"
+
+When you load your PEFT model which has been trained on a task (for example, classification), you may get a warning like:
+
+> Some weights of LlamaForSequenceClassification were not initialized from the model checkpoint at meta-llama/Llama-3.2-1B and are newly initialized: ['score.weight']. You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
+
+Although this looks scary, it is most likely nothing to worry about. This warning comes from Transformers, and it isn't a PEFT specific warning. It lets you know that a randomly initialized classification head (`score`) is attached to the base model, and the head must be trained to produce sensible predictions.
+
+When you get this warning _before_ training the model, PEFT automatically takes care of making the classification head trainable if you correctly passed the `task_type` argument to the PEFT config.
+
+```python
+from peft import LoraConfig, TaskType
+
+lora_config = LoraConfig(..., task_type=TaskType.SEQ_CLS)
+```
+
+If your classification head does not follow the usual naming conventions from Transformers (which is rare), you have to explicitly tell PEFT the name of the head in `modules_to_save`.
+
+```python
+lora_config = LoraConfig(..., modules_to_save=["name-of-classification-head"])
+```
+
+To check the name of the classification head, print the model and it should be the last module.
+
+If you get this warning from your inference code, i.e. _after_ training the model, when you load the PEFT model, you always have to load the Transformers model first. Since Transformers does not know that you will load PEFT weights afterwards, it still gives the warning.
+
+As always, it is best practice to ensure the model works correctly for inference by running some validation on it.
+
+### Check layer and model status
+
+Sometimes a PEFT model can end up in a bad state, especially when handling multiple adapters. There can be some confusion around what adapters exist, which one is active, which one is merged, etc. To help investigate this issue, call the [`~peft.PeftModel.get_layer_status`] and the [`~peft.PeftModel.get_model_status`] methods. 
+
+The [`~peft.PeftModel.get_layer_status`] method gives you a detailed overview of each targeted layer's active, merged, and available adapters.
+
+```python
+>>> from transformers import AutoModel
+>>> from peft import get_peft_model, LoraConfig
+
+>>> model_id = "google/flan-t5-small"
+>>> model = AutoModel.from_pretrained(model_id)
+>>> model = get_peft_model(model, LoraConfig())
+
+>>> model.get_layer_status()
+[TunerLayerStatus(name='model.encoder.block.0.layer.0.SelfAttention.q',
+                  module_type='lora.Linear',
+                  enabled=True,
+                  active_adapters=['default'],
+                  merged_adapters=[],
+                  requires_grad={'default': True},
+                  available_adapters=['default']),
+ TunerLayerStatus(name='model.encoder.block.0.layer.0.SelfAttention.v',
+                  module_type='lora.Linear',
+                  enabled=True,
+                  active_adapters=['default'],
+                  merged_adapters=[],
+                  requires_grad={'default': True},
+                  available_adapters=['default']),
+...]
+
+>>> model.get_model_status()
+TunerModelStatus(
+    base_model_type='T5Model',
+    adapter_model_type='LoraModel',
+    peft_types={'default': 'LORA'},
+    trainable_params=344064,
+    total_params=60855680,
+    num_adapter_layers=48,
+    enabled=True,
+    active_adapters=['default'],
+    merged_adapters=[],
+    requires_grad={'default': True},
+    available_adapters=['default'],
+)
+```
+
+In the model state output, you should look out for entries that say `"irregular"`. This means PEFT detected an inconsistent state in the model. For instance, if `merged_adapters="irregular"`, it means that for at least one adapter, it was merged on some target modules but not on others. The inference results will most likely be incorrect as a result.
+
+The best way to resolve this issue is to reload the whole model and adapter checkpoint(s). Ensure that you don't perform any incorrect operations on the model, e.g. manually merging adapters on some modules but not others.
+
+Convert the layer status into a pandas `DataFrame` for an easier visual inspection.
+
+```python
+from dataclasses import asdict
+import pandas as pd
+
+df = pd.DataFrame(asdict(layer) for layer in model.get_layer_status())
+```
+
+It is possible to get this information for non-PEFT models if they are using PEFT layers under the hood, but some information like the `base_model_type` or the `peft_types` cannot be determined in that case. As an example, you can call this on a [diffusers](https://huggingface.co/docs/diffusers/index) model like so:
+
+```python
+>>> import torch
+>>> from diffusers import StableDiffusionPipeline
+>>> from peft import get_model_status, get_layer_status
+
+>>> path = "runwayml/stable-diffusion-v1-5"
+>>> lora_id = "takuma104/lora-test-text-encoder-lora-target"
+>>> pipe = StableDiffusionPipeline.from_pretrained(path, dtype=torch.float16)
+>>> pipe.load_lora_weights(lora_id, adapter_name="adapter-1")
+>>> pipe.load_lora_weights(lora_id, adapter_name="adapter-2")
+>>> pipe.set_lora_device(["adapter-2"], "cuda")
+>>> get_layer_status(pipe.text_encoder)
+[TunerLayerStatus(name='text_model.encoder.layers.0.self_attn.k_proj',
+                  module_type='lora.Linear',
+                  enabled=True,
+                  active_adapters=['adapter-2'],
+                  merged_adapters=[],
+                  requires_grad={'adapter-1': False, 'adapter-2': True},
+                  available_adapters=['adapter-1', 'adapter-2'],
+                  devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']}),
+ TunerLayerStatus(name='text_model.encoder.layers.0.self_attn.v_proj',
+                  module_type='lora.Linear',
+                  enabled=True,
+                  active_adapters=['adapter-2'],
+                  merged_adapters=[],
+                  requires_grad={'adapter-1': False, 'adapter-2': True},
+                  devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']}),
+...]
+
+>>> get_model_status(pipe.unet)
+TunerModelStatus(
+    base_model_type='other',
+    adapter_model_type='None',
+    peft_types={},
+    trainable_params=797184,
+    total_params=861115332,
+    num_adapter_layers=128,
+    enabled=True,
+    active_adapters=['adapter-2'],
+    merged_adapters=[],
+    requires_grad={'adapter-1': False, 'adapter-2': True},
+    available_adapters=['adapter-1', 'adapter-2'],
+    devices={'adapter-1': ['cpu'], 'adapter-2': ['cuda']},
+)
+```
+
+## Speed
+
+### Loading adapter weights is slow
+
+Loading adapters like LoRA weights should generally be fast compared to loading the base model. However, there can be use cases where the adapter weights are quite large or where users need to load a large number of adapters -- the loading time can add up in this case. The reason for this is that the adapter weights are first initialized and then overridden by the loaded weights, which is wasteful. To speed up the loading time, you can pass the `low_cpu_mem_usage=True` argument to [`~PeftModel.from_pretrained`] and [`~PeftModel.load_adapter`].
+
+> [!TIP]
+> If this option works well across different use cases, it may become the default for adapter loading in the future.
+
+
+## Reproducibility
+
+### Models using batch norm
+
+When loading a trained PEFT model where the base model uses batch norm (e.g. `torch.nn.BatchNorm1d` or `torch.nn.BatchNorm2d`), you may find that you cannot reproduce the exact same outputs. This is because the batch norm layers keep track of running stats during training, but these stats are not part of the PEFT checkpoint. Therefore, when you load the PEFT model, the running stats of the base model will be used (i.e. from before training with PEFT).
+
+Depending on your use case, this may not be a big deal. If, however, you need your outputs to be 100% reproducible, you can achieve this by adding the batch norm layers to `modules_to_save`. Below is an example of this using resnet and LoRA. Notice that we set `modules_to_save=["classifier", "normalization"]`. We need the `"classifier"` argument because our task is image classification, and we add the `"normalization"` argument to ensure that the batch norm layers are saved in the PEFT checkpoint.
+
+```python
+from transformers import AutoModelForImageClassification
+from peft import LoraConfig, get_peft_model
+
+model_id = "microsoft/resnet-18"
+base_model = AutoModelForImageClassification.from_pretrained(self.model_id)
+config = LoraConfig(
+    target_modules=["convolution"],
+    modules_to_save=["classifier", "normalization"],
+),
+```
+
+Depending on the type of model you use, the batch norm layers could have different names than `"normalization"`, so please ensure that the name matches your model architecture.
+
+## Version mismatch
+
+### Error while loading the config because of an unexpected keyword argument
+
+When you encounter an error like the one shown below, it means the adapter you're trying to load was trained with a more recent version of PEFT than the version you have installed on your system.
+
+```
+TypeError: LoraConfig.__init__() got an unexpected keyword argument <argument-name>
+```
+
+The best way to resolve this issue is to install the latest PEFT version:
+
+```sh
+python -m pip install -U PEFT
+```
+
+If the adapter was trained from a source install of PEFT (an unreleased version of PEFT), then you also need to install PEFT from source.
+
+```sh
+python -m pip install -U git+https://github.com/huggingface/peft.git
+```
+
+If it is not possible for you to upgrade PEFT, there is a workaround you can try.
+
+Assume the error message says that the unknown keyword argument is named `foobar`. Search inside the `adapter_config.json` of this PEFT adapter for the `foobar` entry and delete it from the file. Then save the file and try loading the model again.
+
+This solution works most of the time. As long as it is the default value for `foobar`, it can be ignored. However, when it is set to some other value, you will get incorrect results. Upgrading PEFT is the recommended solution.
+
+## Adapter handling
+
+### Using multiple adapters at the same time
+
+PEFT allows you to create more than one adapter on the same model. This can be useful in many situations. For example, for inference, you may want to serve two fine-tuned models from the same base model instead of loading the base model once for each fine-tuned model, which would cost more memory. However, multiple adapters can be activated at the same time. This way, the model may leverage the learnings from all those adapters at the same time. As an example, if you have a diffusion model, you may want to use one LoRA adapter to change the style and a different one to change the subject.
+
+Activating multiple adapters at the same time is generally possible on all PEFT methods (LoRA, LoHa, IA³, etc.) except for prompt learning methods (p-tuning, prefix tuning, etc.). The following example illustrates how to achieve this:
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+
+model_id = ...
+base_model = AutoModelForCausalLM.from_pretrained(model_id)
+model = PeftModel.from_pretrained(base_model, lora_path_0)  # default adapter_name is 'default'
+model.load_adapter(lora_path_1, adapter_name="other")
+# the 'other' adapter was loaded but it's not active yet, so to activate both adapters:
+model.base_model.set_adapter(["default", "other"])
+```
+
+> [!TIP]
+> In the example above, you can see that we need to call `model.base_model.set_adapter(["default", "other"])`. Why can we not call `model.set_adapter(["default", "other"])`? This is unfortunately not possible because, as explained earlier, some PEFT methods don't support activating more than one adapter at a time.
+
+It is also possible to train two adapters at the same time, but you should be careful to ensure that the weights of both adapters are known to the optimizer. Otherwise, only one adapter will receive updates.
+
+```python
+from transformers import AutoModelForCausalLM
+from peft import LoraConfig, get_peft_model
+
+model_id = ...
+base_model = AutoModelForCausalLM.from_pretrained(model_id)
+lora_config_0 = LoraConfig(...)
+lora_config_1 = LoraConfig(...)
+model = get_peft_model(base_model, lora_config_0)
+model.add_adapter(adapter_name="other", peft_config=lora_config_1)
+```
+
+If we would now call:
+
+```python
+from transformers import Trainer
+
+trainer = Trainer(model=model,  ...)
+trainer.train()
+```
+
+or
+
+```python
+optimizer = torch.optim.AdamW([param for param in model.parameters() if param.requires_grad], ...)
+```
+
+then the second LoRA adapter (`"other"`) would not be trained. This is because it is inactive at this moment, which means the `requires_grad` attribute on its parameters is set to `False` and the optimizer will ignore it. Therefore, make sure to activate all adapters that should be trained _before_ initializing the optimizer:
+
+```python
+# activate all adapters
+model.base_model.set_adapter(["default", "other"])
+trainer = Trainer(model=model,  ...)
+trainer.train()
+```
+
+> [!TIP]
+> This section deals with using multiple adapters _of the same type_ on the same model, for example, using multiple LoRA adapters at the same time. It does not apply to using _different types_ of adapters on the same model, for example one LoRA adapter and one LoHa adapter. For this, please check [`PeftMixedModel`](https://huggingface.co/docs/peft/developer_guides/mixed_models).

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/index.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# PEFT
+
+🤗 PEFT (Parameter-Efficient Fine-Tuning) is a library for efficiently adapting large pretrained models to various downstream applications without fine-tuning all of a model's parameters because it is prohibitively costly. PEFT methods only fine-tune a small number of (extra) model parameters - significantly decreasing computational and storage costs - while yielding performance comparable to a fully fine-tuned model. This makes it more accessible to train and store large language models (LLMs) on consumer hardware.
+
+PEFT is integrated with the Transformers, Diffusers, and Accelerate libraries to provide a faster and easier way to load, train, and use large models for inference.
+
+<div class="mt-10">
+  <div class="w-full flex flex-col space-y-4 md:space-y-0 md:grid md:grid-cols-2 md:gap-y-4 md:gap-x-5">
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="quicktour"
+      ><div class="w-full text-center bg-gradient-to-br from-blue-400 to-blue-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Quicktour</div>
+      <p class="text-gray-700">Start here if you're new to 🤗 PEFT to get an overview of the library's main features, and how to train a model with a PEFT method.</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./task_guides/prompt_based_methods"
+      ><div class="w-full text-center bg-gradient-to-br from-indigo-400 to-indigo-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">How-to guides</div>
+      <p class="text-gray-700">Practical guides demonstrating how to apply various PEFT methods across different types of tasks like image classification, causal language modeling, automatic speech recognition, and more. Learn how to use 🤗 PEFT with the DeepSpeed and Fully Sharded Data Parallel scripts.</p>
+    </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./conceptual_guides/adapter"
+      ><div class="w-full text-center bg-gradient-to-br from-pink-400 to-pink-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Conceptual guides</div>
+      <p class="text-gray-700">Get a better theoretical understanding of how LoRA and various soft prompting methods help reduce the number of trainable parameters to make training more efficient.</p>
+   </a>
+    <a class="!no-underline border dark:border-gray-700 p-5 rounded-lg shadow hover:shadow-lg" href="./package_reference/config"
+      ><div class="w-full text-center bg-gradient-to-br from-purple-400 to-purple-500 rounded-lg py-1.5 font-semibold mb-5 text-white text-lg leading-relaxed">Reference</div>
+      <p class="text-gray-700">Technical descriptions of how 🤗 PEFT classes and methods work.</p>
+    </a>
+  </div>
+</div>
+
+<iframe
+	src="https://stevhliu-peft-methods.hf.space"
+	frameborder="0"
+	width="850"
+	height="620"
+></iframe>

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/install.md

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+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Installation
+
+Before you start, you will need to setup your environment, install the appropriate packages, and configure 🤗 PEFT. 🤗 PEFT is tested on **Python 3.9+**.
+
+🤗 PEFT is available on PyPI, as well as GitHub:
+
+## PyPI
+
+To install 🤗 PEFT from PyPI:
+
+```bash
+pip install peft
+```
+
+## Source
+
+New features that haven't been released yet are added every day, which also means there may be some bugs. To try them out, install from the GitHub repository:
+
+```bash
+pip install git+https://github.com/huggingface/peft
+```
+
+If you're working on contributing to the library or wish to play with the source code and see live 
+results as you run the code, an editable version can be installed from a locally-cloned version of the 
+repository:
+
+```bash
+git clone https://github.com/huggingface/peft
+cd peft
+pip install -e .[test]
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/adalora.md

@@ -0,0 +1,31 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# AdaLoRA
+
+[AdaLoRA](https://hf.co/papers/2303.10512) is a method for optimizing the number of trainable parameters to assign to weight matrices and layers, unlike LoRA, which distributes parameters evenly across all modules. More parameters are budgeted for important weight matrices and layers while less important ones receive fewer parameters.
+
+The abstract from the paper is:
+
+*Fine-tuning large pre-trained language models on downstream tasks has become an important paradigm in NLP. However, common practice fine-tunes all of the parameters in a pre-trained model, which becomes prohibitive when a large number of downstream tasks are present. Therefore, many fine-tuning methods are proposed to learn incremental updates of pre-trained weights in a parameter efficient way, e.g., low-rank increments. These methods often evenly distribute the budget of incremental updates across all pre-trained weight matrices, and overlook the varying importance of different weight parameters. As a consequence, the fine-tuning performance is suboptimal. To bridge this gap, we propose AdaLoRA, which adaptively allocates the parameter budget among weight matrices according to their importance score. In particular, AdaLoRA parameterizes the incremental updates in the form of singular value decomposition. Such a novel approach allows us to effectively prune the singular values of unimportant updates, which is essentially to reduce their parameter budget but circumvent intensive exact SVD computations. We conduct extensive experiments with several pre-trained models on natural language processing, question answering, and natural language generation to validate the effectiveness of AdaLoRA. Results demonstrate that AdaLoRA manifests notable improvement over baselines, especially in the low budget settings. Our code is publicly available at https://github.com/QingruZhang/AdaLoRA*.
+
+## AdaLoraConfig
+
+[[autodoc]] tuners.adalora.config.AdaLoraConfig
+
+## AdaLoraModel
+
+[[autodoc]] tuners.adalora.model.AdaLoraModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/adamss.md

@@ -0,0 +1,38 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# AdaMSS
+
+[AdaMSS](https://openreview.net/forum?id=8ZdWmpYxT0) (AdaMSS: Adaptive Multi-Subspace Approach for Parameter-Efficient Fine-Tuning) is a parameter-efficient fine-tuning method that decomposes weight matrices using SVD and clusters the decomposed space into multiple trainable subspaces. Each subspace learns independent low-rank updates while the original weights remain frozen. AdaMSS also supports Adaptive Subspace Allocation (ASA), which dynamically prunes less important subspaces during training based on gradient information.
+
+The abstract from the paper is:
+
+> We propose AdaMSS, an adaptive multi-subspace approach for parameter-efficient fine-tuning of large models. Unlike traditional parameterefficient fine-tuning methods that operate within a large single subspace of the network weights, AdaMSS leverages subspace segmentation to obtain multiple smaller subspaces and adaptively reduces the number of trainable parameters during training, ultimately updating only those associated with a small subset of subspaces most relevant to the target downstream task. By using the lowest-rank representation, AdaMSS achieves more compact expressiveness and finer tuning of the model parameters. Theoretical analyses demonstrate that AdaMSS has better generalization guarantee than LoRA, PiSSA, and other single-subspace low-rankbased methods. Extensive experiments across image classification, natural language understanding, and natural language generation tasks show that AdaMSS achieves comparable performance to full fine-tuning and outperforms other parameterefficient fine-tuning methods in most cases, all while requiring fewer trainable parameters. Notably, on the ViT-Large model, AdaMSS achieves 4.7% higher average accuracy than LoRA across seven tasks, using just 15.4% of the trainable parameters. On RoBERTa-Large, AdaMSS outperforms PiSSA by 7% in average accuracy across six tasks while reducing the number of trainable parameters by approximately 94.4%. These results demonstrate the effectiveness of AdaMSS in parameter-efficient fine-tuning. The code for AdaMSS is available at https: //github.com/jzheng20/AdaMSS.
+
+
+AdaMSS currently has the following constraints:
+- Only `nn.Linear` layers are supported.
+- Requires scikit-learn for the KMeans clustering step.
+
+If these constraints don't work for your use case, consider other methods instead.
+
+## AdamssConfig
+
+[[autodoc]] tuners.adamss.config.AdamssConfig
+
+## AdamssModel
+
+[[autodoc]] tuners.adamss.model.AdamssModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/adapter_utils.md

@@ -0,0 +1,31 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# LyCORIS
+
+[LyCORIS](https://hf.co/papers/2309.14859) (Lora beYond Conventional methods, Other Rank adaptation Implementations for Stable diffusion) are LoRA-like matrix decomposition adapters that modify the cross-attention layer of the UNet. The [LoHa](loha) and [LoKr](lokr) methods inherit from the `Lycoris` classes here.
+
+## LycorisConfig
+
+[[autodoc]] tuners.lycoris_utils.LycorisConfig
+
+## LycorisLayer
+
+[[autodoc]] tuners.lycoris_utils.LycorisLayer
+
+## LycorisTuner
+
+[[autodoc]] tuners.lycoris_utils.LycorisTuner

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/auto_class.md

@@ -0,0 +1,48 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# AutoPeftModels
+
+The `AutoPeftModel` classes loads the appropriate PEFT model for the task type by automatically inferring it from the configuration file. They are designed to quickly and easily load a PEFT model in a single line of code without having to worry about which exact model class you need or manually loading a [`PeftConfig`].
+
+## AutoPeftModel
+
+[[autodoc]] auto.AutoPeftModel
+    - from_pretrained
+
+## AutoPeftModelForCausalLM
+
+[[autodoc]] auto.AutoPeftModelForCausalLM
+
+## AutoPeftModelForSeq2SeqLM
+
+[[autodoc]] auto.AutoPeftModelForSeq2SeqLM
+
+## AutoPeftModelForSequenceClassification
+
+[[autodoc]] auto.AutoPeftModelForSequenceClassification
+
+## AutoPeftModelForTokenClassification
+
+[[autodoc]] auto.AutoPeftModelForTokenClassification
+
+## AutoPeftModelForQuestionAnswering
+
+[[autodoc]] auto.AutoPeftModelForQuestionAnswering
+
+## AutoPeftModelForFeatureExtraction
+
+[[autodoc]] auto.AutoPeftModelForFeatureExtraction

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/beft.md

@@ -0,0 +1,41 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# BEFT: Bias-Efficient Fine-Tuning of Language Models in Low-Data Regimes
+
+[BEFT](https://arxiv.org/abs/2509.15974) is a parameter efficient fine-tuning algorithm (PEFT) that only fine-tunes the added bias terms of value projections from pretrained transformer models. BEFT demonstrates that fine-tuning the added bias terms of value projections from pretrained transformers generally leads to a higher downstream performance in low-data regimes than fine-tuning the added bias terms of query/key projections.
+
+BEFT currently has the following tradeoffs:
+
+Pros:
+- BEFT requires far fewer parameters than LoRA, while maintaining competitive or superior performance across tasks in low-data regimes. 
+
+Cons:
+- In high-data regimes, BEFT may show limited effectiveness compared to LoRA and full-parameters fine-tuning.
+
+If your use case belongs to the high-data regime, consider other PEFT methods such as LoRA.
+
+The abstract from the paper is:
+
+*Fine-tuning the bias terms of large language models (LLMs) has the potential to achieve unprecedented parameter efficiency while maintaining competitive performance, particularly in low-data regimes. However, the link between fine-tuning different bias terms (i.e., **b**<sub>q</sub>, **b**<sub>k</sub>, and **b**<sub>v</sub> in the query, key, or value projections) and downstream performance remains largely unclear to date. In this paper, we investigate the link between fine-tuning **b**<sub>q</sub>, **b**<sub>k</sub>, and **b**<sub>v</sub> with the performance of the downstream task. Our key finding is that directly fine-tuning **b**<sub>v</sub> generally leads to higher downstream performance in low-data regimes, in comparison to **b**<sub>q</sub> and **b**<sub>k</sub>. We extensively evaluate this unique property across a wide range of LLMs spanning encoder-only and decoder-only architectures up to 6.7B parameters (including bias-free LLMs). Our results provide strong evidence for the effectiveness of directly fine-tuning **b**<sub>v</sub> across various downstream tasks*.
+
+## BeftConfig
+
+[[autodoc]] tuners.beft.config.BeftConfig
+
+## BeftModel
+
+[[autodoc]] tuners.beft.model.BeftModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/boft.md

@@ -0,0 +1,31 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# BOFT
+
+[Orthogonal Butterfly (BOFT)](https://hf.co/papers/2311.06243) is a generic method designed for finetuning foundation models. It improves the parameter efficiency of the finetuning paradigm -- Orthogonal Finetuning (OFT), by taking inspiration from Cooley-Tukey fast Fourier transform, showing favorable results across finetuning different foundation models, including large vision transformers, large language models and text-to-image diffusion models.
+
+The abstract from the paper is:
+
+*Large foundation models are becoming ubiquitous, but training them from scratch is prohibitively expensive. Thus, efficiently adapting these powerful models to downstream tasks is increasingly important. In this paper, we study a principled finetuning paradigm -- Orthogonal Finetuning (OFT) -- for downstream task adaptation. Despite demonstrating good generalizability, OFT still uses a fairly large number of trainable parameters due to the high dimensionality of orthogonal matrices. To address this, we start by examining OFT from an information transmission perspective, and then identify a few key desiderata that enable better parameter-efficiency. Inspired by how the Cooley-Tukey fast Fourier transform algorithm enables efficient information transmission, we propose an efficient orthogonal parameterization using butterfly structures. We apply this parameterization to OFT, creating a novel parameter-efficient finetuning method, called Orthogonal Butterfly (BOFT). By subsuming OFT as a special case, BOFT introduces a generalized orthogonal finetuning framework. Finally, we conduct an extensive empirical study of adapting large vision transformers, large language models, and text-to-image diffusion models to various downstream tasks in vision and language*.
+
+## BOFTConfig
+
+[[autodoc]] tuners.boft.config.BOFTConfig
+
+## BOFTModel
+
+[[autodoc]] tuners.boft.model.BOFTModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/c3a.md

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+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# C3A: Parameter-Efficient Fine-Tuning via Circular Convolution
+
+[C3A](https://huggingface.co/papers/2407.19342) is a parameter-efficient fine-tuning technique that leverages Circular Convolution to achieve high rank adaptation within reasonable resource limits.
+
+Note that you should use a much larger learning rate (LR) for C3A than for other methods. For example, a LR of 1e-1 for C3A is a good starting point. Besides, a much smaller weight decay should be used. You can refer to the `method_comparison` folder for more details.
+
+For the `block_size`, it affects tunable parameters and performance. To start with, you can choose a $\mathrm{gcd}(d_1,d_2)$ near $\frac{\sqrt{d_1 \times d_2}}{r}$, where $r$ is the rank for LoRA you would use for this task.
+
+C3A currently has the following constraints:
+
+- Only `nn.Linear` layers are supported.
+- Quantized layers are not supported.
+- The block size should be a common divisor of both the input and output sizes of target layers.
+
+If these constraints don't work for your use case, consider other methods instead.
+
+The abstract from the paper is:
+
+> Low-Rank Adaptation (LoRA) has gained popularity for fine-tuning large foundation models, leveraging low-rank matrices $\mathbf{A}$ and $\mathbf{B}$ to represent weight changes (i.e., $\Delta \mathbf{W} = \mathbf{B} \mathbf{A}$). This method reduces trainable parameters and mitigates heavy memory consumption associated with full delta matrices by sequentially multiplying $\mathbf{A}$ and $\mathbf{B}$ with the activation. Despite its success, the intrinsic low-rank characteristic may limit its performance. Although several variants have been proposed to address this issue, they often overlook the crucial computational and memory efficiency brought by LoRA. In this paper, we propose Circular Convolution Adaptation (C3A), which not only achieves high-rank adaptation with enhanced performance but also excels in both computational power and memory utilization. Extensive experiments demonstrate that C3A consistently outperforms LoRA and its variants across various fine-tuning tasks.
+
+## C3AConfig
+
+[[autodoc]] tuners.c3a.config.C3AConfig
+
+## C3AModel
+
+[[autodoc]] tuners.c3a.model.C3AModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/cartridges.md

@@ -0,0 +1,91 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Cartridges
+
+Cartridges are a prompt-learning method that stores a compressed long-context representation as a parameterized KV-cache
+prefix. The core idea comes from the paper
+[Cartridges: Lightweight and general-purpose long context representations via self-study](https://huggingface.co/papers/2506.06266).
+
+For a high-level overview and motivation, see the blog post
+[Cartridges: Storing long contexts in tiny caches with self-study](https://hazyresearch.stanford.edu/blog/2025-06-08-cartridges).
+
+## How Cartridges differ from Prefix Tuning
+
+Both Prefix Tuning and Cartridges are served by injecting `past_key_values` (a prefix KV cache) into the base model.
+
+- Prefix Tuning learns virtual token embeddings (and optionally an MLP projection) and produces a KV prefix.
+- Cartridges learn the KV prefix itself directly (the per-layer key/value vectors for `p` virtual tokens), and are
+  designed to be initialized from real prefill KV (for example, the first `p` tokens of a corpus/system prompt).
+
+The paper also recommends freezing the first token as an attention sink for stability (`num_frozen_tokens=1` is the
+default).
+
+## Usage (inference)
+
+Load a trained CARTRIDGE adapter and run generation:
+
+```py
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from peft import PeftModel
+
+model_id = "Qwen/Qwen2.5-0.5B-Instruct"
+adapter_path = "path/to/cartridge_adapter"
+
+base = AutoModelForCausalLM.from_pretrained(model_id)
+model = PeftModel.from_pretrained(base, adapter_path)
+
+tok = AutoTokenizer.from_pretrained(model_id)
+if tok.pad_token is None:
+    tok.pad_token = tok.eos_token
+
+out = model.generate(**tok("Question about the corpus:", return_tensors="pt"), max_new_tokens=64)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+If you need to create and initialize a cartridge before training, see the initialization options below.
+
+## Initialization options
+
+The paper discusses a few practical initialization strategies:
+
+- Random KV (default): create a `CartridgeConfig` and start training. This initializes the KV prefix randomly.
+- KV from the first tokens of a prompt/corpus: use `initialize_kv_prefix_from_text(model, tokenizer, text=...)`. This
+  runs a prefill on `text` and copies the resulting KV cache for the first `num_virtual_tokens` into the adapter.
+- KV from an existing cache: use `initialize_kv_prefix_from_past_key_values(model, past_key_values=...)` if you already
+  have a `past_key_values` object from a base-model prefill.
+
+## Training
+
+The Cartridges paper proposes a SELF-STUDY distillation objective (a frozen base model provides teacher logits; the
+CARTRIDGE adapter is trained so the student matches the teacher’s next-token distribution over the target segment).
+PEFT keeps training logic out of the core library; see
+`https://github.com/huggingface/peft/tree/main/examples/cartridge_self_study` for a reference workflow.
+The example scripts use the frozen base model as the teacher and the adapted model as the student, so both share the
+same underlying checkpoint.
+
+## Composition
+
+To concatenate independently trained cartridges into a single adapter, use `compose_cartridge_adapters(...)`.
+
+## CartridgeConfig
+
+[[autodoc]] tuners.cartridge.config.CartridgeConfig
+
+## CartridgeEncoder
+
+[[autodoc]] tuners.cartridge.model.CartridgeEncoder

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/config.md

@@ -0,0 +1,22 @@
+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Configuration
+
+[`PeftConfigMixin`] is the base configuration class for storing the adapter configuration of a [`PeftModel`], and [`PromptLearningConfig`] is the base configuration class for soft prompt methods (p-tuning, prefix tuning, and prompt tuning). These base classes contain methods for saving and loading model configurations from the Hub, specifying the PEFT method to use, type of task to perform, and model configurations like number of layers and number of attention heads.
+
+## PeftConfigMixin
+
+[[autodoc]] config.PeftConfigMixin
+    - all
+
+## PeftConfig
+
+[[autodoc]] PeftConfig
+    - all
+
+## PromptLearningConfig
+
+[[autodoc]] PromptLearningConfig
+    - all

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/cpt.md

@@ -0,0 +1,34 @@
+<!-- Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+
+⚠️ Note that this file is in Markdown but contains specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Context-aware Prompt Tuning: Advancing In-Context Learning with Adversarial Methods
+
+[CPT](https://huggingface.co/papers/2410.17222) combines In-Context Learning (ICL), Prompt Tuning (PT), and adversarial optimization to improve few-shot learning by refining context embeddings. CPT updates the context tokens by optimizing both the context and the training examples, encapsulating them into a novel loss design that minimizes overfitting, enables more effective optimization, and drives significant improvements in classification tasks.
+
+[//]: # ([CPT]&#40;https://huggingface.co/papers/2410.17222&#41; for the paper)
+
+The abstract from the paper is:
+
+> Large Language Models (LLMs) can perform few-shot learning using either optimization-based approaches or In-Context Learning (ICL). Optimization-based methods often suffer from overfitting, as they require updating a large number of parameters with limited data. In contrast, ICL avoids overfitting but typically underperforms compared to optimization-based methods and is highly sensitive to the selection, order, and format of demonstration examples. To overcome these challenges, we introduce Context-aware Prompt Tuning (CPT), a method inspired by ICL, Prompt Tuning (PT), and adversarial attacks. CPT builds on the ICL strategy of concatenating examples before the input, extending it by incorporating PT-like learning to refine the context embedding through iterative optimization, extracting deeper insights from the training examples. Our approach carefully modifies specific context tokens, considering the unique structure of the examples within the context. In addition to updating the context with PT-like optimization, CPT draws inspiration from adversarial attacks, adjusting the input based on the labels present in the context while preserving the inherent value of the user-provided data. To ensure robustness and stability during optimization, we employ a projected gradient descent algorithm, constraining token embeddings to remain close to their original values and safeguarding the quality of the context. Our method has demonstrated superior accuracy across multiple classification tasks using various LLM models, outperforming existing baselines and effectively addressing the overfitting challenge in few-shot learning.
+
+
+Take a look at [Example](https://github.com/huggingface/peft/blob/main/examples/cpt_finetuning/README.md) for a step-by-step guide on how to train a model with CPT.
+
+
+## CPTConfig
+
+[[autodoc]] tuners.cpt.config.CPTConfig
+
+## CPTEmbedding
+
+[[autodoc]] tuners.cpt.model.CPTEmbedding
+

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/delora.md

@@ -0,0 +1,40 @@
+<!--Copyright 2025 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# DeLoRA: Decoupled Low-rank Adaptation
+[DeLoRA](https://huggingface.co/papers/2503.18225) is a parameter-efficient fine-tuning technique that implicitly maintains a Frobenius boundary with respect to the pretrained weights by normalizing and scaling learnable low-rank matrices. This effectively decouples the learning of directions (BA term) and magnitude (boundary term) of the weight updates, avoiding catastrophic shifts in the adapted weights and enhancing robustness to hyperparameter choices.
+
+Note:
+- use a learning rate 10-100x larger than for standard LoRA variants (typical values from 1e-3/1e-2/..)
+- ensure the initial boundary parameter lambda is not too small (typical values around 10/15/..). Setting different lambdas to different layers is possible
+
+DeLoRA currently has the following constraints:
+- Only nn.Linear layers are supported.
+- Quantized layers are not supported.
+
+If these constraints don't work for your use case, consider other methods instead.
+
+The abstract from the paper is:
+
+> Parameter-Efficient FineTuning (PEFT) methods have recently gained significant popularity thanks to the widespread availability of large-scale pretrained models. These methods allow for quick adaptation to downstream tasks with minimal computational cost. However, popular finetuning methods such as LoRA exhibit limited robustness when it comes to hyperparameter choices or extended training regimes, preventing optimal out-of-the-box performance. In contrast, bounded approaches, such as ETHER, provide greater robustness but are limited to extremely low-rank adaptations and fixed-strength transformations, reducing their adaptation expressive power. In this work, we propose Decoupled Low-rank Adaptation (DeLoRA), a novel finetuning method that normalizes and scales learnable low-rank matrices. By bounding the distance of the transformation, DeLoRA effectively decouples the angular learning from the adaptation strength, enhancing robustness without compromising performance. Through evaluations on subject-driven image generation, natural language understanding, and instruction tuning, we show that DeLoRA matches or surpasses performance of competing PEFT methods, while exhibiting stronger robustness. 
+
+## DeloraConfig
+
+[[autodoc]] tuners.delora.config.DeloraConfig
+
+## DeloraModel
+
+[[autodoc]] tuners.delora.model.DeloraModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/fourierft.md

@@ -0,0 +1,38 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# FourierFT: Discrete Fourier Transformation Fine-Tuning
+
+[FourierFT](https://huggingface.co/papers/2405.03003) is a parameter-efficient fine-tuning technique that leverages Discrete Fourier Transform to compress the model's tunable weights. This method outperforms LoRA in the GLUE benchmark and common ViT classification tasks using much less parameters.
+
+FourierFT currently has the following constraints:
+
+- Only `nn.Linear` layers are supported.
+- Quantized layers are not supported.
+
+If these constraints don't work for your use case, consider other methods instead.
+
+The abstract from the paper is:
+
+> Low-rank adaptation (LoRA) has recently gained much interest in fine-tuning foundation models. It effectively reduces the number of trainable parameters by incorporating low-rank matrices A and B to represent the weight change, i.e., Delta W=BA. Despite LoRA's progress, it faces storage challenges when handling extensive customization adaptations or larger base models. In this work, we aim to further compress trainable parameters by enjoying the powerful expressiveness of the Fourier transform. Specifically, we introduce FourierFT, which treats Delta W as a matrix in the spatial domain and learns only a small fraction of its spectral coefficients. With the trained spectral coefficients, we implement the inverse discrete Fourier transform to recover Delta W. Empirically, our FourierFT method shows comparable or better performance with fewer parameters than LoRA on various tasks, including natural language understanding, natural language generation, instruction tuning, and image classification. For example, when performing instruction tuning on the LLaMA2-7B model, FourierFT surpasses LoRA with only 0.064M trainable parameters, compared to LoRA's 33.5M.
+
+## FourierFTConfig
+
+[[autodoc]] tuners.fourierft.config.FourierFTConfig
+
+## FourierFTModel
+
+[[autodoc]] tuners.fourierft.model.FourierFTModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/functional.md

@@ -0,0 +1,37 @@
+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Functions for PEFT integration
+
+A collection of functions that could be useful for non-PeftModel models, e.g. transformers or diffusers integration
+
+The functions provided here can be considered "public API" of PEFT and hence are safe to be used by packages that provide PEFT integrations.
+
+## Cast the adapter weight dtypes
+[[autodoc]] functional.cast_adapter_dtype
+    - all
+
+## Delete the PEFT adapter from model
+[[autodoc]] functional.delete_adapter
+    - all
+
+## Get the state dict of the PEFT adapter
+[[autodoc]] functional.get_peft_model_state_dict
+    - all
+
+## Inject a PEFT adapter into the model based on a PEFT config
+[[autodoc]] functional.inject_adapter_in_model
+    - all
+
+## Set the active PEFT adapter(s) of the model
+[[autodoc]] functional.set_adapter
+    - all
+
+## Set the `requires_grad` attribute of the specified adapters
+[[autodoc]] functional.set_requires_grad
+    - all
+
+## Load the weights of the PEFT state dict into the model
+[[autodoc]] functional.set_peft_model_state_dict
+    - all

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/gralora.md

@@ -0,0 +1,32 @@
+# GraLoRA
+
+[**Granular Low-Rank Adaptation (GraLoRA)**](https://huggingface.co/papers/2505.20355) is a PEFT method designed to enhance the **expressivity** of low-rank adaptation while improving **robustness to outlier** activations, based on insights from well-known issues in quantization.
+
+![GraLoRA Overview](https://github.com/SqueezeBits/GraLoRA/raw/main/figure/gralora_overview.png)
+
+Unlike standard LoRA, which applies a single low-rank adapter across the entire feature space, GraLoRA introduces a structured and fine-grained adaptation scheme. It divides the adaptation space into a grid of $𝑘^2$ smaller, independent adapter pairs, each responsible for a localized subset of the input and output dimensions. As a result, each adapter operates on a subspace that is $k$ times smaller in both dimensions than the original LoRA adapter.
+
+This granular decomposition enables spatially localized and context-aware updates, effectively increasing representational capacity without additional parameters or computational cost. By isolating the influence of extreme activations within smaller subspaces, GraLoRA mitigates gradient distortion and preserves inter-channel balance during adaptation.
+
+---
+
+The abstract from the paper is:
+
+*Low-Rank Adaptation (LoRA) is a popular method for parameter-efficient fine-
+tuning (PEFT) of generative models, valued for its simplicity and effectiveness.
+Despite recent enhancements, LoRA still suffers from a fundamental limitation:
+overfitting when the bottleneck is widened. It performs best at ranks 32–64, yet its
+accuracy stagnates or declines at higher ranks, still falling short of full fine-tuning
+(FFT) performance. We identify the root cause as LoRA’s structural bottleneck,
+which introduces gradient entanglement to the unrelated input channels and distorts
+gradient propagation. To address this, we introduce a novel structure, Granular
+Low-Rank Adaptation (GraLoRA) that partitions weight matrices into sub-blocks,
+each with its own low-rank adapter. With negligible computational or storage cost,
+GraLoRA overcomes LoRA’s limitations, effectively increases the representational
+capacity, and more closely approximates FFT behavior. Experiments on code
+generation, commonsense reasoning, mathematical reasoning, general language
+understanding, and image generation benchmarks show that GraLoRA consistently
+outperforms LoRA and other baselines, achieving up to +8.5% absolute gain in
+Pass@1 on HumanEval+. These improvements hold across model sizes and rank
+settings, making GraLoRA a scalable and robust solution for PEFT.*
+

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/helpers.md

@@ -0,0 +1,41 @@
+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Helper methods
+
+A collection of helper functions for PEFT.
+
+## Checking if a model is a PEFT model
+
+[[autodoc]] helpers.check_if_peft_model
+    - all
+
+## Temporarily Rescaling Adapter Scale in LoraLayer Modules
+
+[[autodoc]] helpers.rescale_adapter_scale
+    - all
+
+## Context manager to disable input dtype casting in the `forward` method of LoRA layers
+
+[[autodoc]] helpers.disable_input_dtype_casting
+    - all
+
+## Context manager to enable DoRA caching (faster at inference time but requires more memory)
+
+[[autodoc]] helpers.DoraCaching
+    - all
+
+## KappaTune target selection
+
+`KappaTuneSelector` and `find_kappa_target_modules` implement a general target selection process from the [KappaTune paper](https://arxiv.org/abs/2506.16289). 
+
+The method identifies modules with higher flexibility (higher output differential entropy) and lower specialization (lower sensitivity to specific input directions).
+
+These properties make the selected modules good candidates for mitigating catastrophic forgetting in any adaptation method that adds trainable parameters, including LoRA, DoRA, LoHa, AdaLoRA, and even direct fine-tuning of the original weights.
+
+[[autodoc]] helpers.KappaTuneSelector
+    - all
+
+[[autodoc]] helpers.find_kappa_target_modules
+    - all

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/hira.md

@@ -0,0 +1,75 @@
+# HiRA
+
+High-Rank Adaptation ([HiRA](https://openreview.net/pdf?id=TwJrTz9cRS)) is a PEFT method that extends the LoRA approach by applying an element-wise modulation on the original weight matrix. Instead of adding a low-rank update directly, HiRA computes:
+
+$$
+W' = W_0 + W_0 \odot (B A)
+$$
+
+where $W_0$ is the base weight, and $A, B$ are low-rank factors with rank $r \ll \min(	\text{in_features}, \text{out_features})$. This formulation allows HiRA to adapt existing weights with a multiplicative, input-dependent modulation, often improving fine-tuning efficiency on downstream tasks.
+
+The abstract from the HiRA paper is:
+
+> *We propose Hadamard High-Rank Adaptation (HiRA), a parameter-efficient fine-tuning (PEFT) method that enhances the adaptability of Large Language Models (LLMs). While Low-rank Adaptation (LoRA) is widely used to reduce resource demands, its low-rank updates may limit its expressiveness for new tasks. HiRA addresses this by using a Hadamard product to retain high-rank update parameters, improving the model capacity. Empirically, HiRA outperforms LoRA and its variants on several tasks, with extensive ablation studies validating its effectiveness.*
+
+
+## Examples
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import get_peft_model
+from peft.tuners.hira import HiraConfig
+
+# Example 1: HiRA on opt-125m for causal language modeling
+model_id = "facebook/opt-125m"
+base_model = AutoModelForCausalLM.from_pretrained(model_id)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Define HiRA configuration: apply to the MLP dense layers in each transformer block
+hira_config = HiraConfig(
+    r=32,
+    target_modules=["k_proj", "q_proj", "v_proj", "fc1", "fc2"],
+    hira_dropout=0.0,
+    init_weights=True,
+)
+peft_model = get_peft_model(base_model, hira_config)
+
+peft_model.print_trainable_parameters()
+# trainable params: 4,718,592 || all params: 129,957,888 || trainable%: 3.6309
+```
+
+## HiraConfig
+
+[[autodoc]] tuners.hira.config.HiraConfig
+
+## Core Layers
+
+### HiraLayer
+
+[[autodoc]] tuners.hira.layer.HiraLayer
+
+### Linear Adapter
+
+[[autodoc]] tuners.hira.layer.Linear
+
+### Embedding Adapter
+
+[[autodoc]] tuners.hira.layer.Embedding
+
+### Convolutional Adapters
+
+[[autodoc]] tuners.hira.layer.Conv1d [[autodoc]] tuners.hira.layer.Conv2d [[autodoc]] tuners.hira.layer.ConvNd
+
+
+## Citation:
+If you found HiRA is useful, please cite HiRA as:
+```
+@inproceedings{
+huang2025hira,
+title={Hi{RA}: Parameter-Efficient Hadamard High-Rank Adaptation for Large Language Models},
+author={Qiushi Huang and Tom Ko and Zhan Zhuang and Lilian Tang and Yu Zhang},
+booktitle={The Thirteenth International Conference on Learning Representations},
+year={2025},
+url={https://openreview.net/forum?id=TwJrTz9cRS}
+}
+```

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/hotswap.md

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+<!--⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+-->
+
+# Hotswapping adapters
+
+The idea of hotswapping an adapter is the following: We can already load multiple adapters, e.g. two LoRAs, at the same time. But sometimes, we want to load one LoRA and then replace its weights in-place with the LoRA weights of another adapter. This is now possible the `hotswap_adapter` function.
+
+In general, this should be faster than deleting one adapter and loading the adapter in its place, which would be the how to achieve the same final outcome without hotswapping. Another advantage of hotswapping is that it prevents re-compilation in case the PEFT model is already compiled using `torch.compile`. This can save quite a lot of time.
+
+## Example without `torch.compile`
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+from peft.utils.hotswap import hotswap_adapter
+
+model_id = ...
+inputs = ...
+device = ...
+model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+# load lora 0
+model = PeftModel.from_pretrained(model, <path-adapter-0>)
+with torch.inference_mode():
+    output_adapter_0 = model(inputs)
+
+# replace the "default" lora adapter with the new one
+hotswap_adapter(model, <path-adapter-1>, adapter_name="default", torch_device=device)
+with torch.inference_mode():
+    output_adapter_1 = model(inputs).logits
+```
+
+## Example with `torch.compile`
+
+```python
+import torch
+from transformers import AutoModelForCausalLM
+from peft import PeftModel
+from peft.utils.hotswap import hotswap_adapter, prepare_model_for_compiled_hotswap
+
+model_id = ...
+inputs = ...
+device = ...
+max_rank = ...  # maximum rank among all LoRA adapters that will be used
+model = AutoModelForCausalLM.from_pretrained(model_id).to(device)
+
+# load lora 0
+model = PeftModel.from_pretrained(model, <path-adapter-0>)
+# Prepare the model to allow hotswapping even if ranks/scalings of 2nd adapter differ.
+# You can skip this step if all ranks and scalings are identical.
+prepare_model_for_compiled_hotswap(model, target_rank=max_rank)
+model = torch.compile(model)
+with torch.inference_mode():
+    output_adapter_0 = model(inputs)
+
+# replace the "default" lora adapter with the new one
+hotswap_adapter(model, <path-adapter-1>, adapter_name="default", torch_device=device)
+with torch.inference_mode():
+    output_adapter_1 = model(inputs).logits
+```
+
+## Caveats
+
+Hotswapping works with transformers models and diffusers models. However, there are some caveats:
+
+- Right now, only LoRA is properly supported.
+- It only works for the same PEFT method, so no swapping LoRA and LoHa, for example.
+- The adapter that is being swapped in must target the same layers as the previous adapter or a subset of those layers. It cannot target new layers. Therefore, if possible, start with the adapter that targets most layers.
+
+## API
+
+[[autodoc]] utils.hotswap.hotswap_adapter
+    - all
+
+[[autodoc]] utils.hotswap.hotswap_adapter_from_state_dict
+    - all

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/hra.md

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+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Bridging The Gap between Low-rank and Orthogonal Adaptation via Householder Reflection Adaptation (HRA)
+
+[HRA](https://huggingface.co/papers/2405.17484) is a simple but effective adapter-based fine-tuning method by leveraging Householder reflections. This method harnesses the advantages of both strategies, reducing parameters and computation costs while penalizing the loss of pre-training knowledge. It consistently achieves better performance with fewer trainable parameters and outperforms state-of-the-art adapters across different models, including large language models (LLMs) and conditional image generators.
+
+
+The abstract from the paper is:
+
+> While following different technical routes, both low-rank and orthogonal adaptation techniques can efficiently adapt large-scale pre-training models in specific tasks or domains based on a small piece of trainable parameters. In this study, we bridge the gap between these two techniques, proposing a simple but effective adaptation method based on Householder reflections. Given a pre-trained model, our method fine-tunes its layers by multiplying each frozen weight matrix with an orthogonal matrix constructed by a chain of learnable Householder reflections (HRs). This HR-based orthogonal fine-tuning is equivalent to an adaptive low-rank adaptation. Moreover, we show that the orthogonality of the reflection planes corresponding to the HRs impacts the model capacity and regularity. The analysis motivates us to regularize the orthogonality of the HRs, leading to different implementations of the proposed Householder reflection adaptation (HRA) method. Compared with state-of-the-art methods, HRA achieves superior performance with fewer learnable parameters when adapting large language models and conditional image generators. The code is available at [peft](https://github.com/huggingface/peft/tree/main/src/peft/tuners/hra) and [HRA](https://github.com/DaShenZi721/HRA).
+
+## HRAConfig
+
+[[autodoc]] tuners.hra.config.HRAConfig
+
+## HRAModel
+
+[[autodoc]] tuners.hra.model.HRAModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/ia3.md

@@ -0,0 +1,31 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# IA3
+
+Infused Adapter by Inhibiting and Amplifying Inner Activations, or [IA3](https://hf.co/papers/2205.05638), is a method that adds three learned vectors to rescale the keys and values of the self-attention and encoder-decoder attention layers, and the intermediate activation of the position-wise feed-forward network.
+
+The abstract from the paper is:
+
+*Few-shot in-context learning (ICL) enables pre-trained language models to perform a previously-unseen task without any gradient-based training by feeding a small number of training examples as part of the input. ICL incurs substantial computational, memory, and storage costs because it involves processing all of the training examples every time a prediction is made. Parameter-efficient fine-tuning (PEFT) (e.g. adapter modules, prompt tuning, sparse update methods, etc.) offers an alternative paradigm where a small set of parameters are trained to enable a model to perform the new task. In this paper, we rigorously compare few-shot ICL and PEFT and demonstrate that the latter offers better accuracy as well as dramatically lower computational costs. Along the way, we introduce a new PEFT method called (IA)^3 that scales activations by learned vectors, attaining stronger performance while only introducing a relatively tiny amount of new parameters. We also propose a simple recipe based on the T0 model called T-Few that can be applied to new tasks without task-specific tuning or modifications. We validate the effectiveness of T-Few on completely unseen tasks by applying it to the RAFT benchmark, attaining super-human performance for the first time and outperforming the state-of-the-art by 6% absolute. All of the code used in our experiments is publicly available*.
+
+## IA3Config
+
+[[autodoc]] tuners.ia3.config.IA3Config
+
+## IA3Model
+
+[[autodoc]] tuners.ia3.model.IA3Model

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/layernorm_tuning.md

@@ -0,0 +1,34 @@
+<!--Copyright 2024 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# LayerNorm Tuning
+
+LayerNorm Tuning ([LN Tuning](https://huggingface.co/papers/2312.11420)) is a PEFT method that only fine-tunes the parameters of the LayerNorm layers in a model.
+The paper has tested the performance of this method on large language models and has shown that it can achieve strong performance with a significant reduction in the number of trainable parameters and GPU memory usage.
+However, the method is not limited to language models and can be applied to any model that uses LayerNorm layers.
+In this implementation, the default is that all layernorm layers inside a model is finetuned, but it could be used to target other layer types such as `MLP` or `Attention` layers, this can be done by specifying the `target_modules` in the `LNTuningConfig`.
+
+The abstract from the paper is:
+
+*This paper introduces an efficient strategy to transform Large Language Models (LLMs) into Multi-Modal Large Language Models (MLLMs). By conceptualizing this transformation as a domain adaptation process, i.e., transitioning from text understanding to embracing multiple modalities, we intriguingly note that, within each attention block, tuning LayerNorm suffices to yield strong performance. Moreover, when benchmarked against other tuning approaches like full parameter finetuning or LoRA, its benefits on efficiency are substantial. For example, when compared to LoRA on a 13B model scale, performance can be enhanced by an average of over 20% across five multi-modal tasks, and meanwhile, results in a significant reduction of trainable parameters by 41.9% and a decrease in GPU memory usage by 17.6%. On top of this LayerNorm strategy, we showcase that selectively tuning only with conversational data can improve efficiency further. Beyond these empirical outcomes, we provide a comprehensive analysis to explore the role of LayerNorm in adapting LLMs to the multi-modal domain and improving the expressive power of the model.*
+
+## LNTuningConfig
+
+[[autodoc]] tuners.ln_tuning.config.LNTuningConfig
+
+## LNTuningModel
+
+[[autodoc]] tuners.ln_tuning.model.LNTuningModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/lily.md

@@ -0,0 +1,41 @@
+<!--Copyright 2026 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Lily: Low-Rank Interconnected Adaptation across Layers
+
+[Lily](https://huggingface.co/papers/2407.09946) is a parameter-efficient fine-tuning technique that introduces cross-layer weight sharing for adapter matrices. Instead of learning an independent AB pair per layer as in LoRA, Lily uses **locally shared A adapters** (each A is shared across a block of `stride_A` consecutive layers) and **globally shared B experts** (a small pool of `num_B` B adapters is shared across all layers). At each forward pass, a lightweight data-dependent router computes a softmax-weighted combination of the B experts to produce the effective B for that layer and input.
+
+This sharing can reduce the total number of adapter matrices from `2N` (standard LoRA) to `N / stride_A + num_B`, freeing up the parameter budget to use a **much larger rank `r`** — typically `2×`–`4×` what you would use in LoRA. Higher rank and better interconnectivity increase the effective rank of the weight update `ΔW = A × combined_B`, leading to better adaptation performance.
+
+Because the B combination is **data-dependent** (the router weights depend on the input activations at runtime), `merge` and `unmerge` are **not supported**. If weight merging is required for your deployment, consider other methods such as LoRA instead.
+
+Lily currently has the following additional constraints:
+- Only `nn.Linear` layers are supported.
+- Quantized layers are not supported.
+
+If these constraints don't work for your use case, consider other methods instead.
+
+The abstract from the paper is:
+
+> Low-rank adaptation (LoRA) is a widely used parameter-efficient fine-tuning (PEFT) method that learns weight updates ΔW = AB for pretrained weights W through low-rank adapters A and B. While LoRA ensures hardware efficiency, its low-rank weight updates limit adaptation performance. In this paper, we propose low-rank interconnected adaptation across layers (Lily), a novel PEFT method that introduces an interconnected framework with locally shared A and globally shared B experts. This structure eliminates redundant per-layer AB pairs, enabling higher-rank ΔW with equal or fewer parameters. To enhance expressiveness, we use data-dependent routers to determine A-B interconnections, preventing B experts from converging to the same behavior and improving representational power across domains. Experiments across modalities, architectures, and model sizes demonstrate Lily's superior performance and efficiency.
+
+## LilyConfig
+
+[[autodoc]] tuners.lily.config.LilyConfig
+
+## LilyModel
+
+[[autodoc]] tuners.lily.model.LilyModel

tasks/tasksmith-749ff8d440e1/environment/source/docs/source/package_reference/llama_adapter.md

@@ -0,0 +1,31 @@
+<!--Copyright 2023 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
+the License. You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
+rendered properly in your Markdown viewer.
+
+-->
+
+# Llama-Adapter
+
+[Llama-Adapter](https://hf.co/papers/2303.16199) is a PEFT method specifically designed for turning Llama into an instruction-following model. The Llama model is frozen and only a set of adaptation prompts prefixed to the input instruction tokens are learned. Since randomly initialized modules inserted into the model can cause the model to lose some of its existing knowledge, Llama-Adapter uses zero-initialized attention with zero gating to progressively add the instructional prompts to the model.
+
+The abstract from the paper is:
+
+*We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the input text tokens at higher transformer layers. Then, a zero-init attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With efficient training, LLaMA-Adapter generates high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Furthermore, our approach can be simply extended to multi-modal input, e.g., images, for image-conditioned LLaMA, which achieves superior reasoning capacity on ScienceQA. We release our code at https://github.com/ZrrSkywalker/LLaMA-Adapter*.
+
+## AdaptionPromptConfig
+
+[[autodoc]] tuners.adaption_prompt.config.AdaptionPromptConfig
+
+## AdaptionPromptModel
+
+[[autodoc]] tuners.adaption_prompt.model.AdaptionPromptModel