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Model: shiyilin123/qwen3-4b-medical-finetuned Source: Original Platform
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# ollama modelfile auto-generated by llamafactory
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FROM .
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TEMPLATE """{{ if .System }}<|im_start|>system
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{{ .System }}<|im_end|>
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{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
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{{ .Content }}<|im_end|>
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<|im_start|>assistant
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{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
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{{ end }}{{ end }}"""
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SYSTEM """You are Qwen, created by Alibaba Cloud. You are a helpful assistant."""
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PARAMETER stop "<|im_end|>"
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PARAMETER num_ctx 4096
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123
README.md
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README.md
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# Qwen3-4B Medical Finetuned(医疗微调版)
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> 🩺 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。
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## 🔍 模型简介
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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。
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## 🚀 使用方法
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### 安装依赖
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```python
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pip install modelscope transformers torch
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```
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### Python 加载与推理
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```python
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from modelscope import AutoModelForCausalLM, AutoTokenizer
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model_id = "shiyilin123/qwen3-4b-medical-finetuned"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", trust_remote_code=True)
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# 输入医疗问题
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prompt = "患者主诉头痛、发热三天,可能是什么疾病?"
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messages = [
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{"role": "user", "content": prompt}
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]
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input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
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outputs = model.generate(
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input_ids,
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max_new_tokens=512,
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do_sample=True,
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temperature=0.7,
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top_p=0.9,
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)
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response = tokenizer.decode(outputs[0][input_ids.shape[1]:], skip_special_tokens=True)
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print("模型回答:\n", response)
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```
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## 📚 训练数据
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- 中文医疗对话数据集(脱敏)
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- 《内科学》《外科学》等医学教材节选
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- 临床诊疗指南(如高血压、糖尿病等)
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- 医学科普问答对
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> 所有数据均已清洗、去隐私、合规处理。
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## ⚙️ 模型配置
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- 基座模型:Qwen3-4B
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- 微调方式:全参数微调(Full Fine-tuning)
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- 训练框架:ModelScope + Transformers
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- 硬件:NVIDIA A100 × 4
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- 序列长度:4096
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## 📈 性能表现(示例)
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||||
| 问题 | 模型输出(节选) |
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| -------------------------- | ------------------------------------------------------------ |
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||||
| “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…… |
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||||
| “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…… |
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> 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。
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## ⚠️ 使用声明
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- 本模型**不构成医疗建议**,仅供科研、学习、辅助参考。
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- 请勿用于临床诊断或替代专业医生判断。
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- 模型可能存在幻觉或错误,使用前请人工复核关键信息。
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## 📜 许可证
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||||
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Apache 2.0 —— 允许商用、修改、分发,请保留原作者信息。
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## 🙏 致谢
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||||
- 感谢 [通义千问团队](https://qwenlm.github.io/) 开源 Qwen 系列模型
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||||
- 感谢魔搭社区提供模型托管与算力支持
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||||
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||||
## 📬 联系作者
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- 用户名:shiyilin123
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- 魔搭主页:https://modelscope.cn/profile/shiyilin123
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||||
- 问题反馈:欢迎在模型页面“讨论”区留言
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||||
## ✅ 使用说明
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||||
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||||
1. 将上述内容 **复制粘贴** 到你的模型目录中,保存为 `README.md`
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||||
2. 编码格式建议使用 **UTF-8 无 BOM**(记事本 → 另存为 → 编码选“UTF-8”)
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||||
3. 上传后,魔搭会自动渲染为美观的模型主页!
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||||
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||||
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||||
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||||
---
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||||
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||||
## 💡 可选增强(进阶)
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||||
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||||
如果你想支持 **在线体验**,可在 `README.md` 最后添加:
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||||
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||||
## 🧪 在线体验
|
||||
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||||
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||||
点击页面上方 “**在Notebook中打开**” 按钮,可直接在线运行推理代码。
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||||
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||||
或者添加 **模型卡片图**(上传 `thumbnail.png` 到模型目录):
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||||
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||||
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||||
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||||
---
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||||
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||||
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||||
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||||
---
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||||
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<tool_call>": 151657,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
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"<|image_pad|>": 151655,
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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chat_template.jinja
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chat_template.jinja
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{%- if tools %}
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{{- '<|im_start|>system\n' }}
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{%- if messages[0]['role'] == 'system' %}
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{{- messages[0]['content'] }}
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{%- else %}
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{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
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{%- endif %}
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{{- "\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>" }}
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{%- for tool in tools %}
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{{- "\n" }}
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{{- tool | tojson }}
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{%- endfor %}
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{{- "\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" }}
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{%- else %}
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||||
{%- if messages[0]['role'] == 'system' %}
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||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
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{%- else %}
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{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
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||||
{%- endif %}
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{%- endif %}
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{%- for message in messages %}
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
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{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
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{{- '<|im_start|>' + message.role }}
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{%- if message.content %}
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{{- '\n' + message.content }}
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{%- endif %}
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{%- for tool_call in message.tool_calls %}
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{%- if tool_call.function is defined %}
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{%- set tool_call = tool_call.function %}
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{%- endif %}
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||||
{{- '\n<tool_call>\n{"name": "' }}
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{{- tool_call.name }}
|
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{{- '", "arguments": ' }}
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{{- tool_call.arguments | tojson }}
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{{- '}\n</tool_call>' }}
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{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
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||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
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||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
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||||
{{- '\n</tool_response>' }}
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||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{{- '<|im_end|>\n' }}
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||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
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||||
{%- endif %}
|
||||
66
config.json
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config.json
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{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
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],
|
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"attention_dropout": 0.0,
|
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
|
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"hidden_size": 2048,
|
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"initializer_range": 0.02,
|
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"intermediate_size": 11008,
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"layer_types": [
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
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"full_attention",
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"full_attention",
|
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"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,
|
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"rope_scaling": null,
|
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"rope_theta": 1000000.0,
|
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"sliding_window": null,
|
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"tie_word_embeddings": true,
|
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"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.55.0",
|
||||
"use_cache": true,
|
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"use_sliding_window": false,
|
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"vocab_size": 151936
|
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}
|
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1
configuration.json
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configuration.json
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{"framework":"Pytorch","task":"text-generation"}
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generation_config.json
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
|
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"pad_token_id": 151643,
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"repetition_penalty": 1.05,
|
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"temperature": 0.7,
|
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"top_k": 20,
|
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"top_p": 0.8,
|
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"transformers_version": "4.55.0"
|
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}
|
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47
gitattributes
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gitattributes
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||||
*.7z filter=lfs diff=lfs merge=lfs -text
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||||
*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.bin.* filter=lfs diff=lfs merge=lfs -text
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||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
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||||
*.ftz filter=lfs diff=lfs merge=lfs -text
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||||
*.gz filter=lfs diff=lfs merge=lfs -text
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||||
*.h5 filter=lfs diff=lfs merge=lfs -text
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||||
*.joblib filter=lfs diff=lfs merge=lfs -text
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||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
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||||
*.model filter=lfs diff=lfs merge=lfs -text
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||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
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||||
*.onnx filter=lfs diff=lfs merge=lfs -text
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||||
*.ot filter=lfs diff=lfs merge=lfs -text
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||||
*.parquet filter=lfs diff=lfs merge=lfs -text
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||||
*.pb filter=lfs diff=lfs merge=lfs -text
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||||
*.pt filter=lfs diff=lfs merge=lfs -text
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||||
*.pth filter=lfs diff=lfs merge=lfs -text
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||||
*.rar filter=lfs diff=lfs merge=lfs -text
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9
modelscope.yaml
Normal file
9
modelscope.yaml
Normal file
@@ -0,0 +1,9 @@
|
||||
# modelscope.yaml
|
||||
framework: pytorch
|
||||
task: text-generation
|
||||
license: apache-2.0
|
||||
tags:
|
||||
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|
||||
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|
||||
pipeline:
|
||||
type: text-generation
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
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|
||||
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|
||||
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|
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||||
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|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
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size 11421896
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208
tokenizer_config.json
Normal file
208
tokenizer_config.json
Normal file
@@ -0,0 +1,208 @@
|
||||
{
|
||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
BIN
vocab.json
(Stored with Git LFS)
Normal file
BIN
vocab.json
(Stored with Git LFS)
Normal file
Binary file not shown.
Reference in New Issue
Block a user