Llama3-8B-Chinese-Chat，首个使用 ORPO 算法微调的中文 Llama3 模型，文章介绍：https://zhuanlan.zhihu.com/p/693905042（可点原文链接阅读）
Llama3-Chinese，首个使用 DoRA 和 LoRA+ 算法微调的中文 Llama3 模型，仓库地址：https://github.com/seanzhang-zhichen/llama3-chinese
下面我们一起来看看它的微调案例（可以直接在Colab上使用免费GPU运行），整个流程与其他模型微调基本一致：
from llmtuner import run_exp%cd /content/LLaMA-Factory/run_exp(dict(stage="sft",do_train=True,model_name_or_path="unsloth/llama-3-8b-Instruct-bnb-4bit",dataset="identity,alpaca_gpt4_en,alpaca_gpt4_zh",template="llama3",finetuning_type="lora",lora_target="all",output_dir="llama3_lora",per_device_train_batch_size=2,gradient_accumulation_steps=4,lr_scheduler_type="cosine",logging_steps=10,warmup_ratio=0.1,save_steps=1000,learning_rate=5e-5,num_train_epochs=3.0,max_samples=500,max_grad_norm=1.0,quantization_bit=4,loraplus_lr_ratio=16.0,use_unsloth=True,fp16=True,))
训练数据集：
[{"instruction": "hi","input": "","output": "Hello! I am Llama-3, an AI assistant developed by LLaMA Factory. How can I assist you today?"},{"instruction": "hello","input": "","output": "Hello! I am Llama-3, an AI assistant developed by LLaMA Factory. How can I assist you today?"},{"instruction": "Who are you?","input": "","output": "I am Llama-3, an AI assistant developed by LLaMA Factory. How can I assist you today?"},...更多llama3数据集（huggingface&魔搭）：
https://huggingface.co/datasets?sort=trending&search=llama3
值得一提的是，llamafactory的微调方案利用unsloth加速，而unsloth也在更早的时候发布了自己的微调方案，感兴趣的读者可以体验。
unsloth（https://github.com/unslothai/unsloth）是一个用于加速深度学习模型训练的开源工具。它可以实现5倍到30倍的训练速度提升,同时还能减少50%的内存占用。
