Update README.md

README.md

@@ -4,52 +4,53 @@ license: apache-2.0
 
 ## 📖 Introduction
 
-# DistilQwen2.5-DS3-0324 系列快思考推理模型
+# DistilQwen2.5-DS3-0324 Series: Fast-Thinking Reasoning Models
 
-## 概述
-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：
-- **推理速度提升**：输出token数减少60-80%（相比慢思考模型）
-- **资源消耗降低**：适合边缘计算部署
-- **认知偏差消除**：独创的轨迹对齐技术
+## 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
 
-## 核心创新
-### 1. 快思考蒸馏框架
-- **阶段一：快思考CoT数据收集**
-  - **Long-to-Short改写**：从DeepSeek-R1提炼关键推理步骤
-  - **教师模型蒸馏**：提取DeepSeekV3-0324的快速推理轨迹
+## 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
 
-- **阶段二：CoT轨迹认知对齐**
-  - **动态难度分级**（简单/中等/困难）
-    - LLM-as-a-Judge评估小模型可理解性
-    - 简单链扩展 → 补充必要步骤
-    - 困难链精简 → 移除高阶逻辑跳跃
-  - **验证机制**：迭代优化直至所有数据达"中等"评级
+- **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. 性能突破
-- **32B模型**在GPQA Diamond基准接近10倍参数量的闭源模型
-- **推理效率**显著提升（见下表对比）
+### 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)
 
-| 模型                          | MMLU_PRO Tokens | AIME2024 Tokens | 速度增益 |
-|--------------------------------|-----------------|-----------------|----------|
-| DistilQwen2.5-R1-32B (慢思考)  | 4198            | 12178           | 1x       |
-| DistilQwen2.5-DS3-0324-32B     | 690             | 4177            | 5-8x     |
+| 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       |
 
-## 技术优势
-- **双阶段蒸馏**：先压缩推理长度，再对齐认知轨迹
-- **动态数据优化**：自适应难度调整确保知识可迁移性
-- **开源兼容**：基于Qwen2.5基座模型微调
+## 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
 
-## 🚀 快速开始
+## 🚀 Quick Start
 
 ```python
-from modelscope import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer
+device = "cuda" # the device to load the model onto
 
 model = AutoModelForCausalLM.from_pretrained(
-    "PAI/DistilQwen2.5-DS3-0324-7B",
+    "alibaba-pai/DistilQwen2.5-DS3-0324-7B",
     torch_dtype="auto",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("PAI/DistilQwen2.5-DS3-0324-7B")
+tokenizer = AutoTokenizer.from_pretrained("alibaba-pai/DistilQwen2.5-DS3-0324-7B")
 
 prompt = "Give me a short introduction to large language model."
 messages=[
@@ -72,4 +73,5 @@ generated_ids = [
 ]
 
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+
 ```