77 lines
3.3 KiB
Markdown
77 lines
3.3 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 Series: Fast-Thinking Reasoning Models
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## Overview
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In response to the industry challenge of balancing efficient reasoning with cognitive capabilities, the DistilQwen2.5-DS3-0324 series innovatively transfers the fast-thinking capabilities of DeepSeekV3-0324 to lightweight models. Through a two-stage distillation framework, this series achieves high performance while delivering:
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- **Enhanced Reasoning Speed**: Reduces output tokens by 60-80% (compared to slow-thinking models)
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- **Reduced Resource Consumption**: Suitable for edge computing deployment
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- **Elimination of Cognitive Bias**: Proprietary trajectory alignment technology
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## Core Innovations
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### 1. Fast-Thinking Distillation Framework
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- **Stage 1: Fast-Thinking CoT Data Collection**
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- **Long-to-Short Rewriting**: Extracts key reasoning steps from DeepSeek-R1
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- **Teacher Model Distillation**: Captures the rapid reasoning trajectories of DeepSeekV3-0324
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- **Stage 2: CoT Trajectory Cognitive Alignment**
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- **Dynamic Difficulty Grading** (Easy/Medium/Hard)
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- LLM-as-a-Judge evaluates small model comprehensibility
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- Simple chain expansion → Adds necessary steps
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- Hard chain simplification → Removes high-level logical leaps
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- **Validation Mechanism**: Iterative optimization until all data reaches "Medium" rating
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### 2. Performance Breakthroughs
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- **32B Model** approaches the performance of closed-source models with 10x the parameters on the GPQA Diamond benchmark
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- **Significant Improvement in Reasoning Efficiency** (see comparison table below)
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| Model | MMLU_PRO Tokens | AIME2024 Tokens | Speed Gain |
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|--------------------------------|-----------------|-----------------|------------|
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| DistilQwen2.5-R1-32B (Slow-Thinking) | 4198 | 12178 | 1x |
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| DistilQwen2.5-DS3-0324-32B | 690 | 4177 | 5-8x |
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## Technical Advantages
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- **Two-Stage Distillation**: First compresses reasoning length, then aligns cognitive trajectories
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- **Dynamic Data Optimization**: Adaptive difficulty adjustment ensures knowledge transferability
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- **Open-Source Compatibility**: Fine-tuned based on the Qwen2.5 base model
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## 🚀 Quick Start
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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device = "cuda" # the device to load the model onto
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model = AutoModelForCausalLM.from_pretrained(
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"alibaba-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("alibaba-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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``` |