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---
license: apache-2.0
---
---
license: apache-2.0
---
## ๐ Introduction
# DistilQwen2.5-DS3-0324 Series: Fast-Thinking Reasoning Models
## Overview
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:
- **Enhanced Reasoning Speed**: Reduces output tokens by 60-80% (compared to slow-thinking models)
- **Reduced Resource Consumption**: Suitable for edge computing deployment
- **Elimination of Cognitive Bias**: Proprietary trajectory alignment technology
## Core Innovations
### 1. Fast-Thinking Distillation Framework
- **Stage 1: Fast-Thinking CoT Data Collection**
- **Long-to-Short Rewriting**: Extracts key reasoning steps from DeepSeek-R1
- **Teacher Model Distillation**: Captures the rapid reasoning trajectories of DeepSeekV3-0324
- **Stage 2: CoT Trajectory Cognitive Alignment**
- **Dynamic Difficulty Grading** (Easy/Medium/Hard)
- LLM-as-a-Judge evaluates small model comprehensibility
- Simple chain expansion โ Adds necessary steps
- Hard chain simplification โ Removes high-level logical leaps
- **Validation Mechanism**: Iterative optimization until all data reaches "Medium" rating
### 2. Performance Breakthroughs
- **32B Model** approaches the performance of closed-source models with 10x the parameters on the GPQA Diamond benchmark
- **Significant Improvement in Reasoning Efficiency** (see comparison table below)
| Model | MMLU_PRO Tokens | AIME2024 Tokens | Speed Gain |
|--------------------------------|-----------------|-----------------|------------|
| DistilQwen2.5-R1-32B (Slow-Thinking) | 4198 | 12178 | 1x |
| DistilQwen2.5-DS3-0324-32B | 690 | 4177 | 5-8x |
## Technical Advantages
- **Two-Stage Distillation**: First compresses reasoning length, then aligns cognitive trajectories
- **Dynamic Data Optimization**: Adaptive difficulty adjustment ensures knowledge transferability
- **Open-Source Compatibility**: Fine-tuned based on the Qwen2.5 base model
## ๐ Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"alibaba-pai/DistilQwen2.5-DS3-0324-32B",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/DistilQwen2.5-DS3-0324-32B")
prompt = "Give me a short introduction to large language model."
messages=[
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant. You should think step-by-step."},
{"role": "user", "content": prompt},
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=2048๏ผ
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
``` |