REILX/Llama-3-8B-Instruct-ruozhiba-lora

基于ruozhiba对Llama-3-8B-Instruct进行微调。

模型：

• https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct

数据集：

• https://huggingface.co/datasets/LooksJuicy/ruozhiba

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。
测试模型分别为：

• Llama-3-8B
• Llama-3-8B-Instruct
• LLama3-Instruct-sft-ruozhiba,使用ruozhiba数据对Llama-3-8B-Instruct使用sft方式lora微调

测试机器

8*A800

结果

模型名称	CEVAL	MMLU
LLama3	49.91	66.62
LLama3-Instruct	50.55	67.15
LLama3-Instruct-sft-ruozhiba-3epoch	50.87	67.51

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 2
• total_train_batch_size: 16
• total_eval_batch_size: 8
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 20
• num_epochs: 3.0
• mixed_precision_training: Native AMP