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90 lines
3.3 KiB
Markdown
90 lines
3.3 KiB
Markdown
# MiniCPM Fine-tuning
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(Part of the doc inside these demo code is automatically generated by [RepoAgent](https://github.com/OpenBMB/RepoAgent))
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[中文版](https://github.com/OpenBMB/MiniCPM/blob/main/finetune/README.md)
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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.
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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.
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Running the example requires `python>=3.10`. Besides the basic `torch` dependency, additional dependencies are needed to run the example code.
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**We have provided an [example notebook](lora_finetune.ipynb) to demonstrate how to process data and use the fine-tuning script with AdvertiseGen as an example.**
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```bash
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pip install -r requirements.txt
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```
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## Testing Hardware Standard
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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:
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+ SFT full parameters fine-tuning: Evenly distributed across 4 GPUs, each GPU consumes `30245MiB` of memory.
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+ LORA fine-tuning: One GPU, consuming `10619MiB` of memory.。
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> Please note that these results are for reference only, and memory consumption may vary with different parameters. Please adjust according to your hardware situation.
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## Multi-Turn Dialogue Format
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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.
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For the data file, the example uses the following format
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```json
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[
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{
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"messages": [
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{
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"role": "system",
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"content": "<system prompt text>"
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},
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{
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"role": "user",
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"content": "<user prompt text>"
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},
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{
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"role": "assistant",
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"content": "<assistant response text>"
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},
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// ... Muti Turn
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{
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"role": "user",
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"content": "<user prompt text>"
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},
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{
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"role": "assistant",
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"content": "<assistant response text>"
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}
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]
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}
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// ...
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]
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```
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## Dataset Format Example
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> 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.
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```
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{"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。"}]}
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```
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## Start Fine-tuning
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Execute **single-node multi-GPU/multi-node multi-GPU** runs with the following code.
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```bash
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cd finetune
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bash sft_finetune.sh
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```
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Execute **single-node single-GPU** runs with the following code.
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```angular2html
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cd finetune
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bash lora_finetune.sh
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```
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