We provide two scripts to use the native Transformers for inference: a command-line interface and a web graphical interface.

Taking the loading of the Chinese-Alpaca-7B model as an example (for loading Plus models, refer to Loading Chinese-Alpaca-Plus below):

python scripts/inference/inference_hf.py \
    --base_model path_to_original_llama_hf_dir \
    --lora_model path_to_chinese_llama_or_alpaca_lora \
    --with_prompt \
    --interactive

If you have already merged the models with merge_llama_with_chinese_lora_to_hf.py , you don't need to specify --lora_model:

python scripts/inference/inference_hf.py \
    --base_model path_to_merged_llama_or_alpaca_hf_dir \
    --with_prompt \
    --interactive

Parameter description:

• --base_model {base_model}: Directory containing the LLaMA model weights and configuration files in HF format.
• --lora_model {lora_model}: Directory of the Chinese LLaMA/Alpaca LoRa files after decompression, or the 🤗Model Hub model name. If this parameter is not provided, only the model specified by --base_model will be loaded.
• --tokenizer_path {tokenizer_path}: Directory containing the corresponding tokenizer. If this parameter is not provided, its default value is the same as --lora_model; if the --lora_model parameter is not provided either, its default value is the same as --base_model.
• --with_prompt: Whether to merge the input with the prompt template. If you are loading an Alpaca model, be sure to enable this option!
• --interactive: Launch interactively for multiple single-round question-answer sessions (this is not the contextual dialogue in llama.cpp).
• --data_file {file_name}: In non-interactive mode, read the content of file_name line by line for prediction.
• --predictions_file {file_name}: In non-interactive mode, write the predicted results in JSON format to file_name.
• --use_cpu: Only use CPU for inference.
• --gpus {gpu_ids}: the GPU id(s) to use, default 0. You can specify multiple GPUs, for instance 0,1,2.

This method will start a web frontend page for interaction and support multi-turn conversations. In addition to Transformers, you need to install Gradio and mdtex2html:

pip install gradio
pip install mdtex2html

The launch command:

python scripts/inference/gradio_demo.py \
	--base_model path_to_original_llama_hf_dir \
	--lora_model path_to_chinese_alpaca_lora

If you have already merged the LoRA weights with merge_llama_with_chinese_lora_to_hf.py, you don't need to specify --lora_model:

python scripts/inference/gradio_demo.py --base_model path_to_merged_alpaca_hf_dir 

Parameter description:

• --base_model {base_model}: Directory containing the LLaMA model weights and configuration files in HF format.
• --lora_model {lora_model}: Directory of the Chinese LLaMA/Alpaca LoRa files after decompression, or the 🤗Model Hub model name. If this parameter is not provided, only the model specified by --base_model will be loaded.
• --tokenizer_path {tokenizer_path}: Directory containing the corresponding tokenizer. If this parameter is not provided, its default value is the same as --lora_model; if the --lora_model parameter is not provided either, its default value is the same as --base_model.
• --use_cpu: Only use CPU for inference.
• --gpus {gpu_ids}: the GPU id(s) to use, default 0. You can specify multiple GPUs, for instance 0,1,2.

Currently, neither of the scripts supports directly loading Chinese-Alpaca-Plus for inference from LoRA weights. If you want to perform inference using Chinese-Alpaca-Plus, please follow the steps below:

1. Using merge_llama_with_chinese_lora.py to obtaining a single model weight file in HF format

python scripts/merge_llama_with_chinese_lora.py \
    --base_model path_to_hf_llama \
    --lora_model path_to_chinese_llama_plus_lora,path_to_chinese_alpaca_plus_lora \
    --output_type huggingface \
    --output_dir path_to_merged_chinese_alpaca_plus

2. Loading the merged model with inference_hf.py or gradio_demo.py:

python scripts/inference/inference_hf.py \
    --base_model path_to_merged_chinese_alpaca_plus \
    --with_prompt --interactive

• Due to differences in decoding implementation details between different frameworks, this script cannot guarantee to reproduce the decoding effect of llama.cpp.
• This script is for convenient and quick experience only, and has not been optimized for fast inference.
• When running 7B model inference on a CPU, make sure you have 32GB of memory; when running 7B model inference on a single GPU, make sure you have 20GB VRAM.