|
--- |
|
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 |
