75 lines
2.7 KiB
Markdown
75 lines
2.7 KiB
Markdown
---
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license: apache-2.0
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---
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## 📖 Introduction
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# DistilQwen2.5-DS3-0324 系列快思考推理模型
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## 概述
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在平衡高效推理与思维能力的行业挑战下，DistilQwen2.5-DS3-0324系列创新性地将DeepSeekV3-0324的快思考能力迁移到轻量模型中。通过两阶段蒸馏框架，该系列在保持高性能的同时实现：
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- **推理速度提升**：输出token数减少60-80%（相比慢思考模型）
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- **资源消耗降低**：适合边缘计算部署
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- **认知偏差消除**：独创的轨迹对齐技术
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## 核心创新
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### 1. 快思考蒸馏框架
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- **阶段一：快思考CoT数据收集**
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- **Long-to-Short改写**：从DeepSeek-R1提炼关键推理步骤
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- **教师模型蒸馏**：提取DeepSeekV3-0324的快速推理轨迹
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- **阶段二：CoT轨迹认知对齐**
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- **动态难度分级**（简单/中等/困难）
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- LLM-as-a-Judge评估小模型可理解性
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- 简单链扩展 → 补充必要步骤
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- 困难链精简 → 移除高阶逻辑跳跃
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- **验证机制**：迭代优化直至所有数据达"中等"评级
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### 2. 性能突破
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- **32B模型**在GPQA Diamond基准接近10倍参数量的闭源模型
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- **推理效率**显著提升（见下表对比）
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| 模型 | MMLU_PRO Tokens | AIME2024 Tokens | 速度增益 |
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|--------------------------------|-----------------|-----------------|----------|
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| DistilQwen2.5-R1-32B (慢思考) | 4198 | 12178 | 1x |
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| DistilQwen2.5-DS3-0324-32B | 690 | 4177 | 5-8x |
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## 技术优势
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- **双阶段蒸馏**：先压缩推理长度，再对齐认知轨迹
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- **动态数据优化**：自适应难度调整确保知识可迁移性
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- **开源兼容**：基于Qwen2.5基座模型微调
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## 🚀 快速开始
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```python
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from modelscope import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"PAI/DistilQwen2.5-DS3-0324-7B",
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("PAI/DistilQwen2.5-DS3-0324-7B")
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prompt = "Give me a short introduction to large language model."
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messages=[
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{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You should think step-by-step."},
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{"role": "user", "content": prompt},
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=2048，
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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``` |