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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: chargoddard/internlm2-20b-llama |
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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 [chargoddard/internlm2-20b-llama](https://huggingface.co/chargoddard/internlm2-20b-llama) on the oncc_medqa_instruct dataset. |
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## Training procedure |
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``` |
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CUDA_VISIBLE_DEVICES=0 python src/train_bash.py --stage sft --do_train True --model_name_or_path /workspace/model --finetuning_type lora --quantization_bit 4 --flash_attn True --dataset_dir data --cutoff_len 1024 --learning_rate 0.0005 --num_train_epochs 1.0 --max_samples 10000 --lr_scheduler_type cosine --max_grad_norm 1.0 --logging_steps 10 --save_steps 100 --warmup_steps 20 --neftune_noise_alpha 0.5 --lora_rank 8 --lora_dropout 0.2 --output_dir /workspace/model-update --per_device_train_batch_size 4 --gradient_accumulation_steps 4 --lora_target q_proj,v_proj --template llama2 --dataset oncc_medqa_instruct |
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``` |
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Note: fix the bug in the tokenizer_config.json. i.e. "internlm/internlm2-20b--tokenization_internlm2.InternLM2Tokenizer" |
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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 |
