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--- |
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license: other |
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library_name: peft |
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tags: |
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- llama-factory |
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- lora |
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- generated_from_trainer |
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base_model: CausalLM/34b-beta |
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model-index: |
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- name: model-update |
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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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# model-update |
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This model is a fine-tuned version of [CausalLM/34b-beta](https://huggingface.co/CausalLM/34b-beta) on the oncc_medqa_instruct dataset. |
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## Training procedure |
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``` |
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git clone https://github.com/chenhaodev/LLaMA-Factory; cd LLaMA-Factory; pip install -r requirements.txt; |
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python create_pods.py 'CausalLM/34b-beta' 'NVIDIA A100 80GB PCIe' 1 xxx xxx xxx "--per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lora_target q_proj,v_proj --template llama2 --dataset oncc_medqa_instruct" 902i850eaw |
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``` |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0005 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 16 |
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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_steps: 20 |
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- num_epochs: 1.0 |
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### Training results |
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### Framework versions |
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- PEFT 0.8.2 |
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- Transformers 4.37.2 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.2 |
