试用Qwen2做推理

参考：https://qwen.readthedocs.io/en/latest/getting_started/quickstart.html

from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto

# Now you do not need to add "trust_remote_code=True"
model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2-7B-Instruct",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")

# Instead of using model.chat(), we directly use model.generate()
# But you need to use tokenizer.apply_chat_template() to format your inputs as shown below
prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)

# Directly use generate() and tokenizer.decode() to get the output.
# Use `max_new_tokens` to control the maximum output length.
generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

执行此操作后模型会缓存到如下目录
/root/.cache/huggingface/hub/models–Qwen–Qwen2-7B-Instruct/

安装LLaMA-Factory

参考：https://github.com/hiyouga/LLaMA-Factory

git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
pip install -e ".[torch,metrics]"

使用自有数据集微调Qwen2

参考：https://qwen.readthedocs.io/en/latest/training/SFT/llama_factory.html

启动web ui 界面来进行微调

llamafactory-cli webui

数据集选择提前准备好的自有数据集train_data.json
编辑LLaMA-Factory/data路径下的dataset_info.json，把自有数据集加入进去

按照自己需求配置训练参数，所有的参数都配置好之后，点一下“预览命令”，确认命令没有问题之后，就可以点击“开始”进行训练了。训练的过程中可以看到 loss的变化曲线、训练耗时等。

参考：https://blog.csdn.net/u012505617/article/details/137864437

验证微调效果

可以直接点击webui界面中的“chat”选项卡，然后点击“加载模型”，然后就可以进行对话了，不过使用A100实测非常慢，一次会话花了四十多分钟。

所以建议在webui 界面训练好模型之后点击“Export”选项卡，然后，在“模型路径”中输入原始模型路径，然后在“检查点路径”中选择自己微调得到的 checkpoint路径，然后在“最大分块大小（GB）”中设置为4，同时设置一下导出目录，最后点击“开始导出”，就可以看到输出的模型了

本地部署模型，并做推理测试
参考：https://qwen.readthedocs.io/en/latest/run_locally/llama.cpp.html

这里使用的是vLLM
参考：https://qwen.readthedocs.io/en/latest/deployment/vllm.html

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

# Initialize the tokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-7B-Instruct")

# Pass the default decoding hyperparameters of Qwen2-7B-Instruct
# max_tokens is for the maximum length for generation.
sampling_params = SamplingParams(temperature=0.1, top_p=0.1, repetition_penalty=1.05, max_tokens=512) #这里想要推理的结果跟我们微调的预期严格一致，所以调低了前两个参数的值。

# Input the model name or path. Can be GPTQ or AWQ models.
llm = LLM(model="LLaMA-Factory/saves/Qwen2-7B-Chat/sft") #这里填写前面Export时填写的Path

# Prepare your prompts
prompt = "这里输入与微调数据集中类似的问题来验证"
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

# generate outputs
outputs = llm.generate([text], sampling_params)

# Print the outputs.
for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")