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---
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
tags:
- text-generation-inference
- llama
- chat
- sft
- lora
---
### 数据集
使用以下8个数据集
对Llama-3-8B-Instruct进行微调。
### 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
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 300
- num_epochs: 1.0 |