Manual Conversion

Preparation

1. Make sure the machine has enough memory to load the complete model (e.g., 13-15G for the 7B model) for the model merging operation.

2. Before merging, make sure that the SHA256 of the base model and the LoRA model patch files are consistent with those in SHA256.md, otherwise, the merge operation cannot be performed.

   • The original LLaMA contains the following files: tokenizer.model, tokenizer_checklist.chk, consolidated.00.pth, params.json

   • The SHA256 of the weight file consolidated.00.pth: 700df0d3013b703a806d2ae7f1bfb8e59814e3d06ae78be0c66368a50059f33d

3. Dependencies:

   • torch（1.12.0）⚠️ the checksum differs for torch 1.13.0 (and after), use with caution
   • transformers（4.28.1）
   • sentencepiece（0.1.97）
   • peft（0.2.0）
   • python >= 3.9

pip install torch==1.12.0
pip install transformers
pip install sentencepiece
pip install peft

pip install git+https://github.com/huggingface/transformers
pip install sentencepiece
pip install peft

Step 1: Convert the original LLaMA model to HF format

Use the script convert_llama_weights_to_hf.py provided by the latest 🤗transformers to convert the original LLaMA model to HuggingFace format. This project is not responsible for the compliance and correctness of using third-party (non-Facebook official) weights, such as the decapoda-research/llama-7b-hf in the HuggingFace model library (use at your own risk).

⚠️ Please put the original LLaMA's tokenizer.model file in--input_dir, and the other files in ${input_dir}/${model_size}.

python src/transformers/models/llama/convert_llama_weights_to_hf.py \
    --input_dir path_to_original_llama_root_dir \
    --model_size 7B \
    --output_dir path_to_original_llama_hf_dir

Step 2: Merge LoRA weights to generate full model weights

This step will expand the Chinese vocabulary of the original LLaMA model (HF format), merge LoRA weights, and generate full model weights. There are two options available here:

• generate a pth model file for quantization and deployment
• generate a HuggingFace model file（bin file) for simple inference.

Note that the merging steps of different models are different. Please read the following guide and follow the steps strictly.

Single LoRA weight merging (applicable to Chinese-LLaMA, Chinese-LLaMA-Plus, and Chinese-Alpaca)

Execute the following command:

python scripts/merge_llama_with_chinese_lora.py \
    --base_model path_to_original_llama_hf_dir \
    --lora_model path_to_chinese_llama_or_alpaca_lora \
    --output_type [pth|huggingface]
    --output_dir path_to_output_dir 

where:

• --base_model: directory where the HF format LLaMA model weights and configuration files are saved (generated in Step 1)
• --lora_model: directory where the Chinese LLaMA/Alpaca LoRA model compressed file downloaded in the previous section is located, or the model name on Hugging Face Model Hub: ziqingyang/chinese-alpaca-lora-7b or ziqingyang/chinese-llama-lora-7b
• --output_type: the saving format, either pth or huggingface. Default: pth.
• --output_dir: directory to save the consolidated model weights (default: ./)
• (optional) --offload_dir: for low-RAM users, please specify a offload directory

Multiple LoRA weights merging (applicable to Chinese-Alpaca-Plus)

Merging Chinese-Alpaca-Plus requires two LoRA weights, namely Chinese-LLaMA-Plus-LoRA and Chinese-Alpaca-Plus-LoRA. To complete the merge, execute the following command:

python scripts/merge_llama_with_chinese_lora.py \
    --base_model path_to_original_llama_hf_dir \
    --lora_model path_to_chinese_llama_plus_lora,path_to_chinese_alpaca_plus_lora \
    --output_type [pth|huggingface]
    --output_dir path_to_output_dir 

Note that the --lora_model requires two LoRA models, separated by a comma.

⚠️The order of the two LoRA models is important and cannot be reversed.⚠️

The meaning of the each options is the same as those in Single LoRA weight merging.