初始化项目，由ModelHub XC社区提供模型

Model: shiyilin123/qwen3-4b-medical-finetuned
Source: Original Platform

.gitattributes

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+vocab.json filter=lfs diff=lfs merge=lfs -text

Modelfile

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+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

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+# Qwen3-4B Medical Finetuned（医疗微调版）
+
+> 🩺 基于 Qwen3-4B 在中文医疗语料上微调的语言模型，适用于医疗问答、病历摘要、医学知识咨询等场景。
+
+## 🔍 模型简介
+
+本模型在 [Qwen3-4B](https://modelscope.cn/models/qwen/Qwen3-4B) 基础上，使用高质量中文医疗对话、医学教材、临床指南等语料进行指令微调（SFT），显著提升在医疗垂直领域的问答准确性、专业性和安全性。
+
+## 🚀 使用方法
+
+### 安装依赖
+
+```python
+pip install modelscope transformers torch
+```
+
+
+### Python 加载与推理
+
+```python
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "shiyilin123/qwen3-4b-medical-finetuned"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
+
+# 输入医疗问题
+prompt = "患者主诉头痛、发热三天，可能是什么疾病？"
+
+messages = [
+    {"role": "user", "content": prompt}
+]
+
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+)
+
+response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
+print("模型回答：\n", response)
+```
+
+## 📚 训练数据
+
+- 中文医疗对话数据集（脱敏）
+- 《内科学》《外科学》等医学教材节选
+- 临床诊疗指南（如高血压、糖尿病等）
+- 医学科普问答对
+
+> 所有数据均已清洗、去隐私、合规处理。
+
+## ⚙️ 模型配置
+
+- 基座模型：Qwen3-4B
+- 微调方式：全参数微调（Full Fine-tuning）
+- 训练框架：ModelScope + Transformers
+- 硬件：NVIDIA A100 × 4
+- 序列长度：4096
+
+## 📈 性能表现（示例）
+
+| 问题                       | 模型输出（节选）                                             |
+| -------------------------- | ------------------------------------------------------------ |
+| “糖尿病患者饮食注意事项？” | 糖尿病患者应控制总热量摄入，选择低GI食物，如糙米、燕麦、绿叶蔬菜…… |
+| “阿司匹林的适应症？”       | 阿司匹林常用于解热镇痛、抗血小板聚集，预防心脑血管事件……     |
+
+> 实际效果请以推理结果为准。建议在专业医生指导下使用模型输出。
+
+## ⚠️ 使用声明
+
+- 本模型**不构成医疗建议**，仅供科研、学习、辅助参考。
+- 请勿用于临床诊断或替代专业医生判断。
+- 模型可能存在幻觉或错误，使用前请人工复核关键信息。
+
+## 📜 许可证
+
+Apache 2.0 —— 允许商用、修改、分发，请保留原作者信息。
+
+## 🙏 致谢
+
+- 感谢 [通义千问团队](https://qwenlm.github.io/) 开源 Qwen 系列模型
+- 感谢魔搭社区提供模型托管与算力支持
+
+## 📬 联系作者
+
+- 用户名：shiyilin123
+- 魔搭主页：https://modelscope.cn/profile/shiyilin123
+- 问题反馈：欢迎在模型页面“讨论”区留言
+## ✅ 使用说明
+
+1. 将上述内容 **复制粘贴** 到你的模型目录中，保存为 `README.md`
+2. 编码格式建议使用 **UTF-8 无 BOM**（记事本 → 另存为 → 编码选“UTF-8”）
+3. 上传后，魔搭会自动渲染为美观的模型主页！
+
+
+
+---
+
+## 💡 可选增强（进阶）
+
+如果你想支持 **在线体验**，可在 `README.md` 最后添加：
+
+## 🧪 在线体验
+
+
+点击页面上方 “**在Notebook中打开**” 按钮，可直接在线运行推理代码。
+
+或者添加 **模型卡片图**（上传 `thumbnail.png` 到模型目录）：
+
+
+
+---
+
+
+
+---
+

added_tokens.json

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+{
+  "</tool_call>": 151658,
+  "<tool_call>": 151657,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 70,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.55.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework":"Pytorch","task":"text-generation"}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
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+  ],
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+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.55.0"
+}

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modelscope.yaml

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+framework: pytorch
+task: text-generation
+license: apache-2.0
+tags:
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+  - text-generation
+pipeline:
+  type: text-generation

special_tokens_map.json

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tokenizer.json

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+}