| --- |
| license: apache-2.0 |
| library_name: peft |
| tags: |
| - trl |
| - sft |
| - generated_from_trainer |
| base_model: hfl/llama-3-chinese-8b-instruct |
| model-index: |
| - name: checkpoints |
| 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. --> |
| # Epipaca |
|  |
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|
| 1. This is the cross-languadge LLM adapter design for epilepsy-care instuction, with support both Mandarin and English. |
| 2. It is finetune by [Epilepsy_Synthetics](https://huggingface.co/datasets/CocoNutZENG/Epilepsy_Synthetics "Epilepsy_Synthetics") dataset. |
| <br> |
| ** Notice: Haven't validate yet. Use with care. ** |
|
|
| ## Training and evaluation data |
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| More information needed |
|
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| ## Training procedure |
|
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| ### Training hyperparameters |
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| The following hyperparameters were used during training: |
| - learning_rate: 0.0001 |
| - train_batch_size: 1 |
| - eval_batch_size: 8 |
| - seed: 42 |
| - gradient_accumulation_steps: 2 |
| - total_train_batch_size: 2 |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_ratio: 0.03 |
| - num_epochs: 2 |
|
|
| ### Training results |
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| ### Framework versions |
|
|
| - PEFT 0.11.1 |
| - Transformers 4.40.2 |
| - Pytorch 2.2.1 |
| - Datasets 2.19.1 |
| - Tokenizers 0.19.1 |
