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Model: xinyuran/Qwen2.5-7B-RLRefine
Source: Original Platform

.gitattributes

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README.md

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+---
+language:
+- zh
+license: mit
+tags:
+- 7b
+- chinese
+- rlhf
+pipeline_tag: text-generation
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+---
+
+# Qwen2.5-7B-RLRefine
+
+**基于 SFT → DPO → GRPO 三阶段 RL 训练的中文电商评论关键词提取模型**
+
+该模型由 [RLRefine](https://github.com/xinyuran/RLRefine) 项目产出，以 Qwen2.5-7B-Instruct 为基座，经过完整的强化学习训练流水线微调，专注于从中文电商评论中提取结构化关键词。
+
+---
+
+## 模型简介
+
+| 项目 | 详情 |
+|------|------|
+| 基座模型 | Qwen/Qwen2.5-7B-Instruct |
+| 训练阶段 | SFT → DPO → GRPO |
+| 主要任务 | 中文电商评论关键词提取 |
+| 输出格式 | 结构化 JSON（含推理过程） |
+| 语言 | 中文 |
+| 许可证 | MIT |
+
+---
+
+## 训练方案
+
+### 三阶段训练流程
+
+```
+Qwen2.5-7B-Instruct（基座）
+    │
+    ▼ SFT  ──  3329 条示范数据，5 epochs，lr=1e-5，LoRA rank=16
+    │          训练时长 ~1h 9min，2× H100 80GB，token_acc=79%
+    │
+    ▼ DPO  ──  ~3697 条偏好对，1 epoch，lr=5e-7，LoRA rank=8
+    │          教会模型区分原子化正确提取 vs. 粗糙提取
+    │
+    ▼ GRPO ──  Schema 驱动奖励函数（F1 50% + 格式 20% + Schema 20% + 推理 10%）
+               924 steps，训练时长 ~4h 35min，最终 reward=0.9375，KL=0.00045
+```
+
+### 硬件环境
+
+- GPU：2× NVIDIA H100 80GB HBM3
+- 框架：ms-swift 3.11.2 + vLLM 0.13.0
+- 分布式：DeepSpeed Zero2（GRPO 阶段）
+
+---
+
+## 模型能力
+
+相比原始 Qwen2.5-7B-Instruct（Zero-shot），本模型的提升：
+
+| 维度 | 原始模型 | 本模型 |
+|------|---------|--------|
+| 推理结构 | 简单 Markdown 列表 | 系统化五步分析 |
+| 关键词原子化 | 常含多词短语 | 严格≤4字原子级 |
+| 置信度类型 | 字符串 `"0.95"`（类型错误） | 数字 `0.95`（正确） |
+| 覆盖率 | 易遗漏核心词 | 系统性覆盖 |
+| 幻觉率 | 较高 | 受奖励函数约束，显著降低 |
+
+---
+
+## 使用方法
+
+### 直接推理（vLLM）
+
+```bash
+vllm serve xinyuran/Qwen2.5-7B-RLRefine --port 8000
+```
+
+### 配合 RLRefine 框架使用
+
+```python
+from core.processor import RLRefineProcessor
+from prompts.prompt_builder import PromptBuilder
+from core.config import Config
+from core.schema import ExtractionTask
+
+# 使用项目内置关键词提取配置
+prompt_builder = PromptBuilder.create_keyword_extraction_builder()
+config = Config(...)
+processor = RLRefineProcessor(config=config, task=task, prompt_builder=prompt_builder)
+
+results = processor.process_batch([
+    {"id": "001", "describe": "电视非常好，画面清晰，系统很智能，更多功能还在摸索中"}
+])
+```
+
+### 输出格式示例
+
+```json
+{
+  "keywords": [
+    ["核心商品主体，评论围绕电视展开", "电视", 0.95],
+    ["电视的关键视觉属性，被明确评价为清晰", "画面", 0.85],
+    ["直接描述画面质量的正面评价词", "清晰", 0.80],
+    ["电视的操作系统，被描述为智能", "系统", 0.85],
+    ["直接描述系统特性的正面评价词", "智能", 0.80],
+    ["提及但未评价的具体属性", "功能", 0.70]
+  ]
+}
+```
+
+---
+
+## 提取规则
+
+模型经过训练后遵循以下关键词提取规范：
+
+1. **忠于原文**：关键词必须逐字对齐原文，不得改写或归纳
+2. **原子化**：每个关键词 ≤ 4 个汉字，对象词和描述词分别输出
+3. **先推理再提取**：输出包含完整推理过程后跟 JSON 结果
+4. **幻觉抑制**：关键词必须在原文中出现，不得编造
+5. **数量上限**：最多输出 15 个关键词，按重要性降序
+
+---
+
+## 局限性
+
+- 当前训练数据聚焦于**中文电商评论**领域，其他领域（新闻、医疗等）效果未经验证
+- 关键词提取任务存在一定主观性，不同场景下规则可能需要调整
+- GRPO 训练时 `frac_reward_zero_std=0.75`，存在一定程度的奖励饱和，详见 [reward_design.md](https://github.com/xinyuran/RLRefine/blob/main/docs/reward_design.md)
+
+---
+
+## 引用
+
+如果本模型对你的工作有帮助，欢迎 Star 项目仓库：
+
+- GitHub: [xinyuran/RLRefine](https://github.com/xinyuran/RLRefine)
+- HuggingFace: [xinyuran/Qwen2.5-7B-RLRefine](https://huggingface.co/xinyuran/Qwen2.5-7B-RLRefine)
+
+---
+
+## License
+
+MIT License — Copyright (c) 2026 xinyuran

added_tokens.json

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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,
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+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "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"
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+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
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+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

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+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.3"
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