Instructions to use Volko76/test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Volko76/test with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("openlm-research/open_llama_3b_v2") model = PeftModel.from_pretrained(base_model, "Volko76/test") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| library_name: peft | |
| tags: | |
| - generated_from_trainer | |
| base_model: openlm-research/open_llama_3b_v2 | |
| model-index: | |
| - name: qlora-out | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) | |
| <details><summary>See axolotl config</summary> | |
| axolotl version: `0.4.0` | |
| ```yaml | |
| base_model: openlm-research/open_llama_3b_v2 | |
| model_type: LlamaForCausalLM | |
| tokenizer_type: LlamaTokenizer | |
| load_in_8bit: false | |
| load_in_4bit: true | |
| strict: false | |
| push_dataset_to_hub: | |
| datasets: | |
| - path: mhenrichsen/alpaca_2k_test | |
| type: alpaca | |
| dataset_prepared_path: | |
| val_set_size: 0.05 | |
| adapter: qlora | |
| lora_model_dir: | |
| sequence_len: 1024 | |
| sample_packing: true | |
| lora_r: 8 | |
| lora_alpha: 32 | |
| lora_dropout: 0.05 | |
| lora_target_modules: | |
| lora_target_linear: true | |
| lora_fan_in_fan_out: | |
| wandb_project: | |
| wandb_entity: | |
| wandb_watch: | |
| wandb_name: | |
| wandb_log_model: | |
| output_dir: ./qlora-out | |
| gradient_accumulation_steps: 1 | |
| micro_batch_size: 1 | |
| num_epochs: 1 | |
| optimizer: paged_adamw_32bit | |
| torchdistx_path: | |
| lr_scheduler: cosine | |
| learning_rate: 0.0002 | |
| train_on_inputs: false | |
| group_by_length: false | |
| bf16: false | |
| fp16: true | |
| tf32: false | |
| gradient_checkpointing: true | |
| early_stopping_patience: | |
| resume_from_checkpoint: | |
| local_rank: | |
| logging_steps: 1 | |
| xformers_attention: | |
| flash_attention: true | |
| gptq_groupsize: | |
| gptq_model_v1: | |
| warmup_steps: 20 | |
| evals_per_epoch: 4 | |
| saves_per_epoch: 1 | |
| debug: | |
| deepspeed: | |
| weight_decay: 0.1 | |
| fsdp: | |
| fsdp_config: | |
| special_tokens: | |
| bos_token: "<s>" | |
| eos_token: "</s>" | |
| unk_token: "<unk>" | |
| ``` | |
| </details><br> | |
| # qlora-out | |
| This model is a fine-tuned version of [openlm-research/open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) on the None dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 1.1110 | |
| ## Model description | |
| More information needed | |
| ## Intended uses & limitations | |
| More information needed | |
| ## Training and evaluation data | |
| More information needed | |
| ## Training procedure | |
| ### Training hyperparameters | |
| The following hyperparameters were used during training: | |
| - learning_rate: 0.0002 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 1 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: cosine | |
| - lr_scheduler_warmup_steps: 20 | |
| - num_epochs: 1 | |
| - mixed_precision_training: Native AMP | |
| ### Training results | |
| | Training Loss | Epoch | Step | Validation Loss | | |
| |:-------------:|:-----:|:----:|:---------------:| | |
| | 1.4577 | 0.0 | 1 | 1.3470 | | |
| | 1.3192 | 0.25 | 107 | 1.1376 | | |
| | 1.3095 | 0.5 | 214 | 1.1225 | | |
| | 1.3178 | 0.75 | 321 | 1.1110 | | |
| ### Framework versions | |
| - PEFT 0.10.0 | |
| - Transformers 4.40.0.dev0 | |
| - Pytorch 2.1.2+cu118 | |
| - Datasets 2.18.0 | |
| - Tokenizers 0.15.0 |