Update README.md

README.md

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----
-license: llama3
----
+---
+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