From bd7ef939cef272e7eba31cd99714630d3dee331f Mon Sep 17 00:00:00 2001
From: ModelHub XC <noreply@modelhub.org.cn>
Date: Wed, 17 Jun 2026 19:15:12 +0800
Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?=
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Model: zjydiary/Medical-Qwen3-14B-1218-GGUF
Source: Original Platform
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+---
+base_model_relation: quantized
+license: Apache License 2.0
+language:
+- zh
+- en
+tags:
+- medical
+- qwen
+- sft
+- lora
+- reinforcement-learning
+base_model: zjydiary/Medical-Qwen3-14B-1218
+pipeline_tag: text-generation
+library_name: peft
+datasets:
+  - zjydiary/Medical
+---
+
+# Medical-Qwen3-14B-1218
+
+## 模型简介 (Model Description)
+
+**Medical-Qwen3-14B-1218** 是基于 [Qwen3-14B](https://modelscope.cn/Qwen/Qwen3-14B) 的医疗领域专用大语言模型。该模型通过两阶段微调（Pre-training + SFT）并采用加权合并策略（Weighted Merging）构建，旨在提升医疗问答的准确性、术语规范性及指令遵循能力。
+
+本版本为 **2025-12-18** 发布的稳定版，融合了 GPT-OSS 风格的训练参数与 Qwen3 的架构优势，在医疗长文本理解与生成上表现出优异的鲁棒性。
+
+## 训练细节 (Training Details)
+
+### 训练流程
+1.  **基座模型**: Qwen/Qwen3-14B (bf16)
+2.  **阶段一：医疗增量预训练 (LoRA PT)**
+    -   数据：混合医疗语料 (Medical Corpus)
+    -   策略：Qwen3 No-Think 模板，全参 LoRA (Target All)
+    -   目标：注入领域知识，适应医疗文风
+3.  **阶段二：指令微调 (LoRA SFT)**
+    -   数据：高质量医疗问答对 (Medical QA Pairs)
+    -   参数：Rank 8, Alpha 16, Dropout 0.07
+    -   策略：Cosine 调度, 强正则 (Weight Decay 0.03), 梯度累积优化
+4.  **模型合并 (Weighted Merging)**
+    -   方法：线性加权合并 (Linear Weighted Merging)
+    -   权重：`SFT (0.7) + PT (0.3)`
+    -   目的：平衡指令遵循能力（SFT）与领域知识广度（PT）
+
+### 训练环境
+-   **Framework**: LLaMA-Factory
+-   **Hardware**: NVIDIA GPU Cluster (FlashAttention-2 Enabled)
+-   **Quantization**: BNB 4-bit NF4 (Training), BFloat16 (Merge/Inference)
+
+## 评估结果 (Evaluation Results)
+
+评估时间：2025-12-18 12:24:46
+评估集：Medical Validation Set (F5/F6)
+解码参数：`temperature=0.6`, `top_p=0.8` (折中推荐配置)
+
+| Metric | Score | Description |
+| :--- | :--- | :--- |
+| **BLEU-4** | **16.38** | 高分表明生成的短语与专家回答高度一致 |
+| **ROUGE-1** | **20.42** | 优秀的词汇覆盖率，关键信息点召回准确 |
+| **ROUGE-2** | **4.60** | 相比早期版本 (+0.1)，二元术语搭配更加精准 |
+| **ROUGE-L** | **11.54** | 良好的长句结构相似度，逻辑连贯性强 |
+
+*注：相比纯 SFT 版本，合并模型在保持 ROUGE 分数的同时，显著降低了过拟合风险，提升了对不同解码参数的鲁棒性。*
+
+## 使用方法 (Usage)
+
+### 推荐推理参数
+为了获得最佳的医疗问答效果，建议使用以下参数：
+-   `temperature`: 0.5 - 0.7 (平衡准确性与流畅度)
+-   `top_p`: 0.8 - 0.9
+-   `repetition_penalty`: 1.05 - 1.1
+-   `max_new_tokens`: 2048 (支持长文回答)
+
+### 代码示例 (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_path = "/workspace/model/zjydiary/Medical-Qwen3-14B-1218"
+
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True
+)
+
+prompt = "患者，男，45岁，主诉持续性上腹痛3天，伴恶心呕吐。请给出初步诊断建议及检查方案。"
+messages = [
+    {"role": "system", "content": "你是一名专业的医疗助手，请用严谨、客观的语气回答。"},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **inputs,
+    max_new_tokens=2048,
+    temperature=0.6,
+    top_p=0.9
+)
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+## 声明 (Disclaimer)
+
+本模型仅供学术研究与技术验证使用。生成的医疗建议仅供参考，不能替代执业医师的诊断。在实际临床应用前，请务必进行严格的专业评估与人工审核。