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30 lines
864 B
30 lines
864 B
2 years ago
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# LLaMA Efficient Tuning
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1. Download the weights of the LLaMA models.
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2. Convert them to HF format using this [script](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
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```python
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python convert_llama_weights_to_hf.py \
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--input_dir path_to_llama_weights --model_size 7B --output_dir llama_7b
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```
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3. Fine-tune the LLaMA models.
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```bash
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CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
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--model_name_or_path llama_7b \
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--do_train \
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--dataset alpaca_gpt4_zh \
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--finetuning_type lora \
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--output_dir path_to_sft_checkpoint \
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--overwrite_cache \
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--per_device_train_batch_size 2 \
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--gradient_accumulation_steps 2 \
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--lr_scheduler_type cosine \
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--logging_steps 10 \
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--save_steps 100 \
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--learning_rate 1e-5 \
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--num_train_epochs 1.0 \
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--fp16
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```
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