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--- |
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license: apache-2.0 |
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library_name: peft |
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tags: |
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- trl |
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- sft |
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- generated_from_trainer |
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base_model: hfl/llama-3-chinese-8b-instruct |
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model-index: |
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- name: checkpoints |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Epipaca |
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1. This is the cross-languadge LLM adapter design for epilepsy-care instuction, with support both Mandarin and English. |
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2. It is finetune by [Epilepsy_Synthetics](https://huggingface.co/datasets/CocoNutZENG/Epilepsy_Synthetics "Epilepsy_Synthetics") dataset. |
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<br> |
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** Notice: Haven't validate yet. Use with care. ** |
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## 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: |
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- learning_rate: 0.0001 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 2 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.03 |
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- num_epochs: 2 |
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### Training results |
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### Framework versions |
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- PEFT 0.11.1 |
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- Transformers 4.40.2 |
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- Pytorch 2.2.1 |
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- Datasets 2.19.1 |
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- Tokenizers 0.19.1 |
