MiniCPM/finetune/README_en.md

MiniCPM Fine-tuning

(Part of the doc inside these demo code are automatically generated by RepoAgent)

中文版

This directory provides examples of fine-tuning the MiniCPM-2B model, including full model fine-tuning and PEFT. In terms of format, we offer examples for multi-turn dialogue fine-tuning and input-output format fine-tuning.

If you have downloaded the model to your local system, the OpenBMB/MiniCPM-2B field mentioned in this document and in the code should be replaced with the corresponding address to load the model from your local system.

Running the example requires python>=3.10. Besides the basic torch dependency, additional dependencies are needed to run the example code.

We have provided an example notebook to demonstrate how to process data and use the fine-tuning script with AdvertiseGen as an example.

pip install -r requirements.txt

Testing Hardware Standard

We only provide examples for single-node multi-GPU/multi-node multi-GPU setups, so you will need at least one machine with multiple GPUs. In the default configuration file in this repository, we have documented the memory usage:

• SFT full parameters fine-tuning: Evenly distributed across 4 GPUs, each GPU consumes 30245MiB of memory.
• LORA fine-tuning: One GPU, consuming 10619MiB of memory.。

Please note that these results are for reference only, and memory consumption may vary with different parameters. Please adjust according to your hardware situation.

Multi-Turn Dialogue Format

The multi-turn dialogue fine-tuning example adopts the ChatGLM3 dialogue format convention, adding different loss_mask for different roles, thus calculating loss for multiple replies in one computation.

For the data file, the example uses the following format

[
  {
    "messages": [
      {
        "role": "system",
        "content": "<system prompt text>"
      },
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant response text>"
      },
      // ... Muti Turn
      {
        "role": "user",
        "content": "<user prompt text>"
      },
      {
        "role": "assistant",
        "content": "<assistant response text>"
      }
    ]
  }
  // ...
]

Dataset Format Example

Here, taking the AdvertiseGen dataset as an example, you can download the AdvertiseGen dataset from Google Drive or Tsinghua Cloud . After extracting the AdvertiseGen directory, place it in the data directory and convert it into the following format dataset.

Please note, the fine-tuning code now includes a validation set, so for a complete set of fine-tuning datasets, it must contain training and validation datasets, while the test dataset is optional. Or, you can use the validation dataset in place of it.

{"messages": [{"role": "user", "content": "类型#裙*裙长#半身裙"}, {"role": "assistant", "content": "这款百搭时尚的仙女半身裙，整体设计非常的飘逸随性，穿上之后每个女孩子都能瞬间变成小仙女啦。料子非常的轻盈，透气性也很好，穿到夏天也很舒适。"}]}

Start Fine-tuning

Execute single-node multi-GPU/multi-node multi-GPU runs with the following code.

cd finetune
bash sft_finetune.sh

Execute single-node single-GPU runs with the following code.

cd finetune
bash lora_finetune.sh