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Model: PAI/DistilQwen2.5-DS3-0324-7B
Source: Original Platform

README.md

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+---
+license: apache-2.0
+---
+
+## 📖 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 → Adds necessary steps
+    - Hard chain simplification → 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
+
+## 🚀 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-DS3-0324-7B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/DistilQwen2.5-DS3-0324-7B")
+
+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，
+)
+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]
+
+```