QWen2.5学习

配置环境

pip install transformers

记得更新一下：typing_extensions

pip install --upgrade typing_extensions

安装modelscope

modelscope/modelscope: ModelScope: bring the notion of Model-as-a-Service to life.

下载这个仓库的代码上传到服务器解压

推理

新建QWen2_5.py：

from modelscope import AutoModelForCausalLM, AutoTokenizer

model_name = "qwen/Qwen2.5-7B-Instruct"

model = AutoModelForCausalLM.from_pretrained(
    model_name,    
    torch_dtype="auto",    
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Give me a short introduction to large language model."
messages = [
    {"role": "system", "content": "You are Qwen, created by Alibaba Cloud. 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(model.device)

generated_ids = model.generate(
    **model_inputs,    
    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]

运行：

 需要再安装一些东西：

pip install accelerate

pip install jinja2

另外如果无法自动下载，可以在下面的网站上手动下载：

https://huggingface.co/Qwen/Qwen2.5-7B-Instruct/tree/main

再运行：

python /root/modelscope-master/QWen2_5.py

2080ti有点慢，大概需要等6分钟：

输出：

输出的结果：

A large language model (LLM) is a type of artificial intelligence (AI) model designed to understand and generate human-like text based on the input it receives. These models are typically trained on vast amounts of text data from the internet, books, articles, and other sources, which allows them to learn patterns, semantics, and nuances in language.

Key characteristics of LLMs include:

1. **Scale**:**: They are usually very large, containing billions or even trillions of parameters, which allows them to capture complex relationships within text.
 2. **Generative Capabilities**:**: LLMs can generate text, answer questions, translate languages, summarize texts, and perform various other natural language processing tasks.
 3. **Context Understanding**.: These models can maintain context over long sequences of text, allowing for more coherent and meaningful responses.
 4. **Fine-Tuning**.: Many LLMs can be fine-tuned on specific tasks or domains to improve their performance on particular applications generation or understanding tasks.

Popular examples of large language models include models like GPT-3, BERT, and T5, which have been used in various applications applications applications scenarios, from customer service chatbots to creative writing assistance.