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Open Source Model Fine-tuning

Prerequisites

Llama-recipes provides a pip distribution for easy install and usage in other projects. Alternatively, it can be installed from source.

Install with pip

pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 llama-recipes

Install from source

To install from source e.g. for development use this command. We're using hatchling as our build backend which requires an up-to-date pip as well as setuptools package.

git clone https://github.com/facebookresearch/llama-recipes
cd llama-recipes
pip install -U pip setuptools
pip install --extra-index-url https://download.pytorch.org/whl/test/cu118 -e .

Fine-tuning

By definition, we have three tasks in the paper.

  • Task 1: Given a task description + Code, generate a code snippet.
  • Task 2: Given a task description + Retrieval, generate a code snippet.
  • Task 3: Given a task description + Oracle, generate a code snippet.

You can use the following script to reproduce CodeLlama-7b's fine-tuning performance on this task：

torchrun --nproc_per_node 2 finetuning.py \
    --use_peft \
    --peft_method lora \
    --enable_fsdp \
    --model_name codellama/CodeLlama-7b-Instruct-hf \
    --context_length 8192 \
    --dataset mlbench_dataset \
    --output_dir OUTPUT_PATH \
    --task TASK \
    --data_path DATA_PATH \

You need to change parameter settings of OUTPUT_PATH, TASK and DATA_PATH correspondingly.

  • OUTPUT_DIR: The directory to save the model.
  • TASK: Choose from 1, 2 and 3.
  • DATA_PATH: The directory of the dataset.

Inference

You can use the following script to reproduce CodeLlama-7b's inference performance on this task：

python chat_completion.py \
    --model_name 'codellama/CodeLlama-7b-Instruct-hf' \
    --peft_model PEFT_MODEL \
    --prompt_file PROMPT_FILE \
    --task TASK \

You need to change parameter settings of PEFT_MODEL, PROMPT_FILE and TASK correspondingly.

  • PEFT_MODEL: The path of the PEFT model.
  • PROMPT_FILE: The path of the prompt file.
  • TASK: Choose from 1, 2 and 3.

You can also refer to vllm/generate.sh to optimize the throughput and memory usage of the model.