\u4f60\u662f\u4e0d\u662f\u4e5f\u60f3\u5728\u672c\u5730\u642d\u5efa\u4e00\u4e2a\u5c5e\u4e8e\u81ea\u5df1\u7684AI\u5bf9\u8bdd\u52a9\u624b，\u4f46\u53c8\u62c5\u5fc3\u6280\u672f\u95e8\u69db\u592a\u9ad8\u3001\u914d\u7f6e\u592a\u590d\u6742？\u4eca\u5929，\u6211\u5c31\u5e26\u4f60\u752810\u5206\u949f\u65f6\u95f4，\u5728\u963f\u91cc\u4e91ECS\u8f7b\u91cf\u670d\u52a1\u5668\u4e0a，\u628a\u963f\u91cc\u901a\u4e49\u5343\u95ee\u7684\u8f7b\u91cf\u7ea7\u5927\u6a21\u578bQwen2.5-1.5B\u8dd1\u8d77\u6765，\u5e76\u7ed9\u5b83\u914d\u4e0a\u4e00\u4e2a\u6f02\u4eae\u7684\u7f51\u9875\u804a\u5929\u754c\u9762\u3002<\/p>\n
\u8fd9\u4e2a\u9879\u76ee\u6700\u5927\u7684\u7279\u70b9\u5c31\u662f\u7b80\u5355\u3002\u4f60\u4e0d\u7528\u53bb\u6298\u817e\u590d\u6742\u7684\u6df1\u5ea6\u5b66\u4e60\u6846\u67b6，\u4e5f\u4e0d\u7528\u62c5\u5fc3\u663e\u5b58\u4e0d\u591f\u3002\u6211\u4eec\u57fa\u4e8e\u5b98\u65b9\u76841.5B\u5c0f\u6a21\u578b，\u7528Streamlit\u8fd9\u4e2a\u8f7b\u91cf\u7ea7\u5de5\u5177\u505a\u4e2a\u7f51\u9875，\u6240\u6709\u5bf9\u8bdd\u90fd\u5728\u4f60\u7684\u670d\u52a1\u5668\u672c\u5730\u5b8c\u6210，\u6570\u636e\u7edd\u5bf9\u5b89\u5168\u3002<\/p>\n
\u5b66\u5b8c\u8fd9\u7bc7\u6559\u7a0b，\u4f60\u5c06\u80fd：<\/p>\n
\u5728\u5f00\u59cb\u4e4b\u524d，\u8bf7\u786e\u4fdd\u4f60\u62e5\u6709\u4ee5\u4e0b\u8d44\u6e90：<\/p>\n
\u6211\u4eec\u7684\u90e8\u7f72\u811a\u672c\u4f1a\u628a\u6240\u6709\u4e8b\u60c5\u90fd\u5b89\u6392\u597d\u3002\u4e3b\u8981\u5305\u542b\u4ee5\u4e0b\u51e0\u4e2a\u90e8\u5206：<\/p>\n
\u6574\u4e2a\u9879\u76ee\u7684\u903b\u8f91\u5f88\u7b80\u5355：\u811a\u672c\u542f\u52a8\u4e00\u4e2aWeb\u670d\u52a1，\u4f60\u901a\u8fc7\u6d4f\u89c8\u5668\u8bbf\u95ee\u8fd9\u4e2a\u670d\u52a1，\u8f93\u5165\u95ee\u9898，\u811a\u672c\u8c03\u7528\u672c\u5730\u7684\u6a21\u578b\u751f\u6210\u56de\u7b54，\u518d\u628a\u7ed3\u679c\u5c55\u793a\u5728\u7f51\u9875\u4e0a\u3002<\/p>\n
\u9996\u5148，\u901a\u8fc7SSH\u8fde\u63a5\u5230\u4f60\u7684\u963f\u91cc\u4e91ECS\u670d\u52a1\u5668\u3002<\/p>\n
ssh root@\u4f60\u7684\u670d\u52a1\u5668\u516c\u7f51IP<\/p>\n
\u8fde\u63a5\u6210\u529f\u540e，\u6211\u4eec\u5148\u66f4\u65b0\u7cfb\u7edf\u5e76\u5b89\u88c5\u4e00\u4e9b\u5fc5\u8981\u7684\u5de5\u5177\u3002<\/p>\n
# \u66f4\u65b0\u8f6f\u4ef6\u5305\u5217\u8868
\napt-get update<\/p>\n
# \u5b89\u88c5Python3\u3001pip\u548c\u9879\u76ee\u9700\u8981\u7684\u7cfb\u7edf\u4f9d\u8d56
\napt-get install -y python3-pip python3-venv git<\/p>\n
# \u9a8c\u8bc1\u5b89\u88c5
\npython3 –version
\npip3 –version<\/p>\n
\u8fd9\u662f\u6700\u5173\u952e\u7684\u4e00\u6b65\u3002\u4f60\u9700\u8981\u5c06Qwen2.5-1.5B-Instruct\u7684\u6a21\u578b\u6587\u4ef6\u4e0b\u8f7d\u5230\u670d\u52a1\u5668\u4e0a\u3002<\/p>\n
\u65b9\u6cd5\u4e00：\u76f4\u63a5\u4ece\u670d\u52a1\u5668\u4e0b\u8f7d（\u63a8\u8350） \u5728\u670d\u52a1\u5668\u4e0a，\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528git\u6765\u514b\u9686\u5305\u542b\u6a21\u578b\u6587\u4ef6\u7684\u4ed3\u5e93（\u5982\u679c\u6a21\u578b\u63d0\u4f9b\u65b9\u4f7f\u7528\u4e86Git LFS）\u3002\u6216\u8005，\u5982\u679c\u4f60\u6709\u6a21\u578b\u7684\u4e0b\u8f7d\u94fe\u63a5，\u53ef\u4ee5\u7528wget\u3002<\/p>\n
# \u521b\u5efa\u4e00\u4e2a\u76ee\u5f55\u6765\u5b58\u653e\u6a21\u578b，\u8fd9\u4e2a\u8def\u5f84\u5728\u540e\u7eed\u4ee3\u7801\u4e2d\u4f1a\u7528\u5230
\nmkdir -p \/root\/qwen1.5b
\ncd \/root\/qwen1.5b<\/p>\n
# \u5047\u8bbe\u4f60\u6709\u6a21\u578b\u7684\u4e0b\u8f7d\u94fe\u63a5，\u4f7f\u7528wget\u4e0b\u8f7d（\u8bf7\u66ff\u6362\u4e3a\u771f\u5b9e\u94fe\u63a5）
\n# wget https:\/\/example.com\/path\/to\/qwen2.5-1.5b-instruct.tar.gz
\n# tar -xzf qwen2.5-1.5b-instruct.tar.gz<\/p>\n
# \u66f4\u5e38\u89c1\u7684\u60c5\u51b5\u662f\u4eceHugging Face\u4e0b\u8f7d（\u9700\u8981\u5148\u5b89\u88c5git-lfs）
\n# apt-get install -y git-lfs
\n# git lfs install
\n# git clone https:\/\/huggingface.co\/Qwen\/Qwen2.5-1.5B-Instruct .<\/p>\n
\u65b9\u6cd5\u4e8c：\u4ece\u672c\u5730\u4e0a\u4f20 \u5982\u679c\u4f60\u5df2\u7ecf\u5728\u672c\u5730\u4e0b\u597d\u4e86\u6a21\u578b\u6587\u4ef6，\u53ef\u4ee5\u4f7f\u7528scp\u547d\u4ee4\u4e0a\u4f20\u5230\u670d\u52a1\u5668\u3002<\/p>\n
# \u5728\u4f60\u7684\u672c\u5730\u7535\u8111\u7ec8\u7aef\u6267\u884c
\nscp -r \/\u672c\u5730\/\u6a21\u578b\u6587\u4ef6\u5939\u8def\u5f84 root@\u4f60\u7684\u670d\u52a1\u5668\u516c\u7f51IP:\/root\/qwen1.5b<\/p>\n
\u65e0\u8bba\u7528\u54ea\u79cd\u65b9\u6cd5，\u6700\u7ec8\u8bf7\u786e\u4fdd\/root\/qwen1.5b\u76ee\u5f55\u4e0b\u5305\u542bconfig.json, model.safetensors (\u6216 .bin\u6587\u4ef6), tokenizer.json\u7b49\u6a21\u578b\u5fc5\u9700\u6587\u4ef6\u3002<\/p>\n
\u73b0\u5728，\u6211\u4eec\u6765\u521b\u5efa\u9879\u76ee\u76ee\u5f55\u548c\u6838\u5fc3\u6587\u4ef6\u3002<\/p>\n
# \u56de\u5230\u7528\u6237\u6839\u76ee\u5f55，\u521b\u5efa\u9879\u76ee\u6587\u4ef6\u5939
\ncd \/root
\nmkdir qwen_streamlit_app && cd qwen_streamlit_app<\/p>\n
\u9996\u5148，\u521b\u5efaPython\u4f9d\u8d56\u6587\u4ef6requirements.txt\u3002<\/p>\n
cat > requirements.txt << 'EOF'
\nstreamlit>=1.28.0
\ntorch>=2.0.0
\ntransformers>=4.35.0
\naccelerate>=0.24.0
\nsentencepiece
\ntiktoken
\nEOF<\/p>\n
\u63a5\u4e0b\u6765，\u521b\u5efa\u6838\u5fc3\u5e94\u7528\u811a\u672capp.py\u3002\u8fd9\u6bb5\u4ee3\u7801\u5b9e\u73b0\u4e86\u6a21\u578b\u52a0\u8f7d\u548c\u804a\u5929\u754c\u9762\u3002<\/p>\n
# app.py
\nimport streamlit as st
\nfrom transformers import AutoModelForCausalLM, AutoTokenizer
\nimport torch<\/p>\n
# \u8bbe\u7f6e\u9875\u9762\u6807\u9898\u548c\u56fe\u6807
\nst.set_page_config(page_title="Qwen2.5-1.5B \u672c\u5730\u52a9\u624b", page_icon="🤖")<\/p>\n
# \u5b9a\u4e49\u6a21\u578b\u8def\u5f84，\u5fc5\u987b\u4e0e\u4f60\u5b58\u653e\u6a21\u578b\u7684\u8def\u5f84\u4e00\u81f4
\nMODEL_PATH = "\/root\/qwen1.5b"<\/p>\n
@st.cache_resource
\ndef load_model_and_tokenizer():
\n """\u52a0\u8f7d\u6a21\u578b\u548c\u5206\u8bcd\u5668，\u5229\u7528Streamlit\u7f13\u5b58\u53ea\u52a0\u8f7d\u4e00\u6b21"""
\n st.info(f"🚀 \u6b63\u5728\u52a0\u8f7d\u6a21\u578b，\u8bf7\u7a0d\u5019… (\u8def\u5f84: {MODEL_PATH})")
\n tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)<\/p>\n
# \u81ea\u52a8\u9009\u62e9\u8fd0\u884c\u8bbe\u5907（GPU\/CPU）\u548c\u6570\u636e\u7c7b\u578b
\n model = AutoModelForCausalLM.from_pretrained(
\n MODEL_PATH,
\n torch_dtype="auto",
\n device_map="auto",
\n trust_remote_code=True
\n )
\n model.eval() # \u8bbe\u7f6e\u4e3a\u8bc4\u4f30\u6a21\u5f0f，\u8282\u7701\u8d44\u6e90
\n st.success("\u2705 \u6a21\u578b\u52a0\u8f7d\u6210\u529f！")
\n return model, tokenizer<\/p>\n
# \u4fa7\u8fb9\u680f\u914d\u7f6e
\nwith st.sidebar:
\n st.title("\u2699\ufe0f \u8bbe\u7f6e\u4e0e\u63a7\u5236")
\n st.markdown("—")
\n if st.button("🧹 \u6e05\u7a7a\u5bf9\u8bdd\u5386\u53f2", use_container_width=True):
\n st.session_state.messages = []
\n st.rerun() # \u6e05\u7a7a\u540e\u5237\u65b0\u754c\u9762
\n torch.cuda.empty_cache() if torch.cuda.is_available() else None
\n st.sidebar.success("\u5bf9\u8bdd\u5386\u53f2\u5df2\u6e05\u7a7a，\u663e\u5b58\u5df2\u91ca\u653e\u3002")<\/p>\n
st.markdown("—")
\n st.markdown("**\u5173\u4e8e**")
\n st.caption("\u57fa\u4e8e Qwen2.5-1.5B-Instruct \u6784\u5efa\u7684\u672c\u5730\u5bf9\u8bdd\u52a9\u624b\u3002\u6240\u6709\u6570\u636e\u5747\u5728\u672c\u5730\u5904\u7406\u3002")<\/p>\n
# \u521d\u59cb\u5316\u804a\u5929\u5386\u53f2
\nif "messages" not in st.session_state:
\n st.session_state.messages = []<\/p>\n
# \u52a0\u8f7d\u6a21\u578b（\u9996\u6b21\u8fd0\u884c\u4f1a\u89e6\u53d1\u52a0\u8f7d）
\nmodel, tokenizer = load_model_and_tokenizer()<\/p>\n
# \u4e3b\u754c\u9762\u6807\u9898
\nst.title("💬 Qwen2.5-1.5B \u672c\u5730\u667a\u80fd\u52a9\u624b")
\nst.caption("\u5b8c\u5168\u672c\u5730\u8fd0\u884c \u00b7 \u9690\u79c1\u5b89\u5168 \u00b7 \u5f00\u7bb1\u5373\u7528")<\/p>\n
# \u663e\u793a\u5386\u53f2\u804a\u5929\u8bb0\u5f55
\nfor message in st.session_state.messages:
\n with st.chat_message(message["role"]):
\n st.markdown(message["content"])<\/p>\n
# \u804a\u5929\u8f93\u5165\u6846
\nif prompt := st.chat_input("\u4f60\u597d，\u6211\u662fQwen，\u6709\u4ec0\u4e48\u53ef\u4ee5\u5e2e\u4f60\u7684？"):
\n # \u5c06\u7528\u6237\u8f93\u5165\u6dfb\u52a0\u5230\u5386\u53f2\u5e76\u663e\u793a
\n st.session_state.messages.append({"role": "user", "content": prompt})
\n with st.chat_message("user"):
\n st.markdown(prompt)<\/p>\n
# \u751f\u6210AI\u56de\u590d
\n with st.chat_message("assistant"):
\n message_placeholder = st.empty() # \u521b\u5efa\u4e00\u4e2a\u5360\u4f4d\u7b26\u7528\u4e8e\u6d41\u5f0f\u8f93\u51fa
\n full_response = ""<\/p>\n
# \u51c6\u5907\u6a21\u578b\u8f93\u5165
\n # \u4f7f\u7528\u6a21\u578b\u7684\u5b98\u65b9\u804a\u5929\u6a21\u677f\u6765\u683c\u5f0f\u5316\u5bf9\u8bdd\u5386\u53f2
\n conversation = [{"role": msg["role"], "content": msg["content"]} for msg in st.session_state.messages]
\n text = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
\n inputs = tokenizer(text, return_tensors="pt").to(model.device)<\/p>\n
# \u751f\u6210\u56de\u590d
\n with torch.no_grad(): # \u7981\u7528\u68af\u5ea6\u8ba1\u7b97，\u5927\u5e45\u8282\u7701\u663e\u5b58
\n generated_ids = model.generate(
\n **inputs,
\n max_new_tokens=1024, # \u6700\u591a\u751f\u62101024\u4e2a\u65b0token
\n do_sample=True, # \u542f\u7528\u91c7\u6837，\u4f7f\u56de\u7b54\u66f4\u591a\u6837
\n temperature=0.7, # \u91c7\u6837\u6e29\u5ea6，\u63a7\u5236\u968f\u673a\u6027
\n top_p=0.9, # \u6838\u91c7\u6837\u53c2\u6570，\u63a7\u5236\u8bcd\u6c47\u9009\u62e9\u8303\u56f4
\n repetition_penalty=1.1 # \u91cd\u590d\u60e9\u7f5a，\u907f\u514d\u91cd\u590d\u7528\u8bcd
\n )<\/p>\n
# \u89e3\u7801\u5e76\u8f93\u51fa\u751f\u6210\u7684\u6587\u672c（\u8df3\u8fc7\u8f93\u5165\u90e8\u5206）
\n new_tokens = generated_ids[0, inputs['input_ids'].shape[1]:]
\n response = tokenizer.decode(new_tokens, skip_special_tokens=True)<\/p>\n
# \u6a21\u62df\u9010\u5b57\u8f93\u51fa\u6548\u679c，\u63d0\u5347\u4f53\u9a8c
\n for chunk in response:
\n full_response += chunk
\n message_placeholder.markdown(full_response + "\u258c")
\n message_placeholder.markdown(full_response)<\/p>\n
# \u5c06AI\u56de\u590d\u6dfb\u52a0\u5230\u5386\u53f2
\n st.session_state.messages.append({"role": "assistant", "content": full_response})<\/p>\n
\u4ee3\u7801\u5199\u597d\u4e86，\u73b0\u5728\u6765\u5b89\u88c5\u8fd0\u884c\u5b83\u9700\u8981\u7684Python\u5e93\u3002<\/p>\n
# \u5728\u9879\u76ee\u76ee\u5f55\u4e0b，\u5b89\u88c5\u4f9d\u8d56
\npip3 install -r requirements.txt<\/p>\n
\u5b89\u88c5\u5b8c\u6210\u540e，\u6211\u4eec\u53ef\u4ee5\u76f4\u63a5\u8fd0\u884cStreamlit\u5e94\u7528\u3002\u4f46\u4e3a\u4e86\u8ba9\u670d\u52a1\u5728\u540e\u53f0\u8fd0\u884c\u4e14\u4e0d\u53d7SSH\u4f1a\u8bdd\u5f71\u54cd，\u6211\u4eec\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u542f\u52a8\u811a\u672cstart.sh\u3002<\/p>\n
cat > start.sh << 'EOF'
\n#!\/bin\/bash
\necho "\u542f\u52a8 Qwen2.5-1.5B Streamlit \u670d\u52a1…"
\necho "\u670d\u52a1\u5c06\u5728\u540e\u53f0\u8fd0\u884c，\u8bbf\u95ee\u5730\u5740: http:\/\/<\u4f60\u7684\u670d\u52a1\u5668IP>:8501"
\n# \u57288501\u7aef\u53e3\u542f\u52a8Streamlit，\u5e76\u5141\u8bb8\u5916\u90e8\u7f51\u7edc\u8bbf\u95ee
\nnohup streamlit run app.py –server.port 8501 –server.address 0.0.0.0 > streamlit.log 2>&1 &
\necho "\u542f\u52a8\u547d\u4ee4\u5df2\u6267\u884c\u3002\u67e5\u770b\u65e5\u5fd7: tail -f streamlit.log"
\nEOF<\/p>\n
# \u7ed9\u542f\u52a8\u811a\u672c\u6dfb\u52a0\u6267\u884c\u6743\u9650
\nchmod +x start.sh<\/p>\n
\u73b0\u5728，\u6267\u884c\u542f\u52a8\u811a\u672c！<\/p>\n
.\/start.sh<\/p>\n
\u7b2c\u4e00\u6b21\u8fd0\u884c\u4f1a\u52a0\u8f7d\u6a21\u578b，\u53ef\u80fd\u9700\u898130\u79d2\u52301\u5206\u949f，\u5177\u4f53\u53d6\u51b3\u4e8e\u670d\u52a1\u5668\u6027\u80fd\u3002\u4f60\u4f1a\u770b\u5230\u7c7b\u4f3c\u4e0b\u9762\u7684\u65e5\u5fd7：<\/p>\n
\u542f\u52a8 Qwen2.5-1.5B Streamlit \u670d\u52a1…
\n\u670d\u52a1\u5c06\u5728\u540e\u53f0\u8fd0\u884c，\u8bbf\u95ee\u5730\u5740: http:\/\/<\u4f60\u7684\u670d\u52a1\u5668IP>:8501<\/p>\n
\u542f\u52a8\u6210\u529f\u540e，\u6253\u5f00\u4f60\u7684\u6d4f\u89c8\u5668，\u8f93\u5165\u4ee5\u4e0b\u5730\u5740：<\/p>\n
http:\/\/\u4f60\u7684\u670d\u52a1\u5668\u516c\u7f51IP:8501<\/p>\n
\u5982\u679c\u9875\u9762\u6210\u529f\u6253\u5f00，\u4f60\u4f1a\u770b\u5230\u4e00\u4e2a\u7b80\u6d01\u7684\u804a\u5929\u754c\u9762\u3002\u5728\u5e95\u90e8\u7684\u8f93\u5165\u6846\u91cc，\u5c1d\u8bd5\u95ee\u5b83\u4e00\u4e9b\u95ee\u9898，\u6bd4\u5982：<\/p>\n
\u7a0d\u7b49\u51e0\u79d2，\u5b83\u5c31\u4f1a\u5728\u672c\u5730\u8ba1\u7b97\u51fa\u7b54\u6848，\u5e76\u663e\u793a\u5728\u754c\u9762\u4e0a\u3002<\/p>\n
1. \u9875\u9762\u6253\u4e0d\u5f00（\u8fde\u63a5\u88ab\u62d2\u7edd）<\/p>\n
2. \u6a21\u578b\u52a0\u8f7d\u5931\u8d25<\/p>\n
3. \u56de\u7b54\u901f\u5ea6\u6162<\/p>\n
4. \u5982\u4f55\u5173\u95ed\u670d\u52a1 \u5728\u670d\u52a1\u5668\u7ec8\u7aef\u91cc，\u627e\u5230Streamlit\u7684\u8fdb\u7a0bID\u5e76\u7ed3\u675f\u5b83\u3002<\/p>\n
# \u627e\u5230\u8fdb\u7a0bID
\nps aux | grep streamlit
\n# \u7ed3\u675f\u8fdb\u7a0b (\u5047\u8bbePID\u662f12345)
\nkill 12345<\/p>\n
\u901a\u8fc7\u4e0a\u9762\u8fd9\u51e0\u4e2a\u6b65\u9aa4，\u4f60\u5df2\u7ecf\u6210\u529f\u5728\u963f\u91cc\u4e91ECS\u4e0a\u90e8\u7f72\u4e86\u4e00\u4e2a\u5b8c\u5168\u672c\u5730\u5316\u7684\u3001\u5e26\u6709Web\u754c\u9762\u7684Qwen2.5-1.5B\u667a\u80fd\u5bf9\u8bdd\u52a9\u624b\u3002\u6211\u4eec\u6765\u56de\u987e\u4e00\u4e0b\u5173\u952e\u70b9：<\/p>\n
\u8fd9\u4e2a\u9879\u76ee\u5c31\u50cf\u4e00\u4e2a\u79cd\u5b50，\u4f60\u5b8c\u5168\u53ef\u4ee5\u57fa\u4e8e\u5b83\u505a\u66f4\u591a\u4e8b\u60c5：<\/p>\n
\u5e0c\u671b\u8fd9\u4e2a\u6559\u7a0b\u80fd\u5e2e\u4f60\u8f7b\u677e\u8e0f\u51fa\u5927\u6a21\u578b\u672c\u5730\u90e8\u7f72\u7684\u7b2c\u4e00\u6b65\u3002\u52a8\u624b\u8bd5\u8bd5\u5427，\u611f\u53d7\u4e00\u4e0b\u5728\u4f60\u81ea\u5df1\u670d\u52a1\u5668\u4e0a\u8fd0\u884cAI\u7684\u4e50\u8da3\u3002<\/p>\n
\u83b7\u53d6\u66f4\u591aAI\u955c\u50cf<\/p>\n
\u60f3\u63a2\u7d22\u66f4\u591aAI\u955c\u50cf\u548c\u5e94\u7528\u573a\u666f？\u8bbf\u95ee CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a，\u63d0\u4f9b\u4e30\u5bcc\u7684\u9884\u7f6e\u955c\u50cf，\u8986\u76d6\u5927\u6a21\u578b\u63a8\u7406\u3001\u56fe\u50cf\u751f\u6210\u3001\u89c6\u9891\u751f\u6210\u3001\u6a21\u578b\u5fae\u8c03\u7b49\u591a\u4e2a\u9886\u57df，\u652f\u6301\u4e00\u952e\u90e8\u7f72\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
Qwen2.5-1.5B Streamlit\u90e8\u7f72\u6559\u7a0b：\u963f\u91cc\u4e91ECS\u8f7b\u91cf\u670d\u52a1\u5668\u4e00\u952e\u90e8\u7f72\u811a\u672c\u5206\u4eab
\n1. \u5b66\u4e60\u76ee\u6807\u4e0e\u9879\u76ee\u4ef7\u503c
\n\u4f60\u662f\u4e0d\u662f\u4e5f\u60f3\u5728\u672c\u5730\u642d\u5efa\u4e00\u4e2a\u5c5e\u4e8e\u81ea\u5df1\u7684AI\u5bf9\u8bdd\u52a9\u624b，\u4f46\u53c8\u62c5\u5fc3\u6280\u672f\u95e8\u69db\u592a\u9ad8\u3001\u914d\u7f6e\u592a\u590d\u6742？\u4eca\u5929，\u6211\u5c31\u5e26\u4f60\u752810\u5206\u949f\u65f6\u95f4，\u5728\u963f\u91cc\u4e91ECS\u8f7b\u91cf\u670d\u52a1\u5668\u4e0a，\u628a\u963f\u91cc\u901a\u4e49\u5343\u95ee\u7684\u8f7b\u91cf\u7ea7\u5927\u6a21\u578bQwen2.5-1.5B\u8dd1\u8d77\u6765，\u5e76\u7ed9\u5b83\u914d\u4e0a\u4e00\u4e2a\u6f02\u4eae\u7684\u7f51\u9875\u804a\u5929\u754c\u9762\u3002
\n\u8fd9\u4e2a\u9879\u76ee\u6700\u5927\u7684\u7279\u70b9<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[9987,956,603],"topic":[],"class_list":["post-88129","post","type-post","status-publish","format-standard","hentry","category-server","tag-ai","tag-956","tag-603"],"yoast_head":"\n