Instructions to use lmong/Adapter3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use lmong/Adapter3 with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("microsoft/Phi-3-mini-4k-instruct") model = PeftModel.from_pretrained(base_model, "lmong/Adapter3") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lmong/Adapter3: direct link, hf CLI and curl.
- Browser
- Download file 1.26 kB
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https://huggingface.co/lmong/Adapter3/resolve/main/README.md
- Command line
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hf download hf://lmong/Adapter3/README.md
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curl -L -o README.md https://huggingface.co/lmong/Adapter3/resolve/main/README.md
1.26 kB
metadata
base_model: microsoft/Phi-3-mini-4k-instruct
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Adapter3
results: []
Adapter3
This model is a fine-tuned version of microsoft/Phi-3-mini-4k-instruct on an unknown dataset.
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: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
Training results
Framework versions
- PEFT 0.13.0
- Transformers 4.45.1
- Pytorch 2.2.0+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0