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--- |
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license: llama3 |
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datasets: |
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- REILX/extracted_tagengo_gpt4 |
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- TigerResearch/sft_zh |
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- alexl83/AlpacaDataCleaned |
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- LooksJuicy/ruozhiba |
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- silk-road/alpaca-data-gpt4-chinese |
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- databricks/databricks-dolly-15k |
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- microsoft/orca-math-word-problems-200k |
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- Sao10K/Claude-3-Opus-Instruct-5K |
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language: |
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- zh |
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- en |
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--- |
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### 数据集 |
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使用以下8个数据集 |
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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。 |
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### 基础模型： |
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- https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct |
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### 训练工具 |
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https://github.com/hiyouga/LLaMA-Factory |
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### 测评方式： |
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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> |
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测试模型分别为： |
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- Llama-3-8B |
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- Llama-3-8B-Instruct |
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- 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 |
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### 测试机器 |
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8*A800 |
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### 8DataSets数据集： |
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大约750Mb的微调数据集 |
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- https://huggingface.co/datasets/REILX/extracted_tagengo_gpt4 |
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- https://huggingface.co/datasets/TigerResearch/sft_zh |
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- https://huggingface.co/datasets/silk-road/alpaca-data-gpt4-chinese |
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- https://huggingface.co/datasets/LooksJuicy/ruozhiba |
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- https://huggingface.co/datasets/microsoft/orca-math-word-problems-200k |
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- https://huggingface.co/datasets/alexl83/AlpacaDataCleaned |
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- https://huggingface.co/datasets/Sao10K/Claude-3-Opus-Instruct-5K |
