机器学习 -- 如何使用 Ollama 运行 Qwen 大模型

📎import requests
import json

def send_message_to_qwen(message, port=11434):
    url = f"http://127.0.0.1:{port}/api/chat"
    payload = {
        "model": "qwen",
        "messages": [{"role": "user", "content": message}]
    }
    response = requests.post(url, json=payload)
    if response.status_code == 200:
        response_content = ""
        for line in response.iter_lines():
            if line:
                response_content += json.loads(line)["message"]["content"]
        return response_content
    else:
        return f"Error: {response.status_code} - {response.text}"

if __name__ == "__main__":
    user_input = "介绍一下北京的旅游景点 ?"
    response = send_message_to_qwen(user_input)
    print("qwen's response:")
    print(response)

📎API_URL = "http://10.12.172.128:11434/v1/chat/completions"
# "model": "qwen2.5:14b",
REQ_SENTENCE = {
    "model": "qwen2.5:14b",
    "temperature": 0,
    "top_p": 1,
    "frequency_penalty": 1,
    "presence_penalty": 1,
    "keep_alive": "5m",
    "messages": [{
        "role": "user",
        "content": "You are a professional translator.\n\n\
Only reply the result and nothing else. \
Please translate to 简体中文:\n\n"
    }]
}
REQ_WORD = {
    "model": "qwen2.5:14b",
    "temperature": 0,
    "top_p": 1,
    "frequency_penalty": 1,
    "presence_penalty": 1,
    "keep_alive": "5m",
    "messages": [{
        "role": "user",
        "content": "你是一个翻译引擎，请翻译给出的文本，只需要翻译不需要解释。\
当且仅当文本只有一个单词时，请给出单词原始形态（如果有）、单词的语种、\
对应的音标或转写、所有含义（含词性）、双语示例，至少三条例句。\
如果你认为单词拼写错误，请提示我最可能的正确拼写，否则请严格按照下面格式给到翻译结果：\n\
    <单词>\n\
    [<语种>]· / <Pinyin>\n\
    [<词性缩写>] <中文含义>]\n\
    例句：\n\
    <序号><例句>(例句翻译)\n\
    词源：\n\
    <词源>\n\n\
Only reply the result and nothing else. \
好的，我明白了，请给我这个单词。:\n\n\
单词是："
    }]
}