---
license: llama3
datasets:
- REILX/extracted_tagengo_gpt4
- TigerResearch/sft_zh
- alexl83/AlpacaDataCleaned
- LooksJuicy/ruozhiba
- silk-road/alpaca-data-gpt4-chinese
- databricks/databricks-dolly-15k
- microsoft/orca-math-word-problems-200k
- Sao10K/Claude-3-Opus-Instruct-5K
language:
- zh
- en
---

### 数据集
使用以下8个数据集
![image/png](https://cdn-uploads.huggingface.co/production/uploads/636f54b95d2050767e4a6317/OkuVQ1lWXRAKyel2Ef0Fz.png)
对Llama-3-8B-Instruct进行微调并测试，结果显示，微调后的模型在CEVAL和MMLU的评分上均有所提升。

### 基础模型：
- https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

### 训练工具
https://github.com/hiyouga/LLaMA-Factory

### 测评方式：
使用opencompass(https://github.com/open-compass/OpenCompass/ )， 测试工具基于CEval和MMLU对微调之后的模型和原始模型进行测试。</br>
测试模型分别为：
- Llama-3-8B
- Llama-3-8B-Instruct
- Llama-3-8B-Instruct-750Mb-lora, 使用8DataSets数据集对Llama-3-8B-Instruct模型进行sft方式lora微调

### 测试机器
8*A800

### 8DataSets数据集：
大约750Mb的微调数据集
- https://huggingface.co/datasets/REILX/extracted_tagengo_gpt4
- https://huggingface.co/datasets/TigerResearch/sft_zh
- https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese
- https://huggingface.co/datasets/LooksJuicy/ruozhiba
- https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k
- https://huggingface.co/datasets/alexl83/AlpacaDataCleaned
- https://huggingface.co/datasets/Sao10K/Claude-3-Opus-Instruct-5K