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README.md

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+# LLaMA Efficient Tuning
+
+1. Download the weights of the LLaMA models.
+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)
+
+```python
+python convert_llama_weights_to_hf.py \
+    --input_dir path_to_llama_weights --model_size 7B --output_dir llama_7b
+```
+
+3. Fine-tune the LLaMA models.
+
+```bash
+CUDA_VISIBLE_DEVICES=0 python src/train_sft.py \
+    --model_name_or_path llama_7b \
+    --do_train \
+    --dataset alpaca_gpt4_zh \
+    --finetuning_type lora \
+    --output_dir path_to_sft_checkpoint \
+    --overwrite_cache \
+    --per_device_train_batch_size 2 \
+    --gradient_accumulation_steps 2 \
+    --lr_scheduler_type cosine \
+    --logging_steps 10 \
+    --save_steps 100 \
+    --learning_rate 1e-5 \
+    --num_train_epochs 1.0 \
+    --fp16
+```