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
+## Overview
-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：
+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:
-- **推理速度提升**：输出token数减少60-80%（相比慢思考模型）
+- **Enhanced Reasoning Speed**: Reduces output tokens by 60-80% (compared to slow-thinking models)
-- **资源消耗降低**：适合边缘计算部署
+- **Reduced Resource Consumption**: Suitable for edge computing deployment
-- **认知偏差消除**：独创的轨迹对齐技术
+- **Elimination of Cognitive Bias**: Proprietary trajectory alignment technology
 
-## 核心创新
+## Core Innovations
-### 1. 快思考蒸馏框架
+### 1. Fast-Thinking Distillation Framework
-- **阶段一：快思考CoT数据收集**
+- **Stage 1: Fast-Thinking CoT Data Collection**
-  - **Long-to-Short改写**：从DeepSeek-R1提炼关键推理步骤
+  - **Long-to-Short Rewriting**: Extracts key reasoning steps from DeepSeek-R1
-  - **教师模型蒸馏**：提取DeepSeekV3-0324的快速推理轨迹
+  - **Teacher Model Distillation**: Captures the rapid reasoning trajectories of DeepSeekV3-0324
 
-- **阶段二：CoT轨迹认知对齐**
+- **Stage 2: CoT Trajectory Cognitive Alignment**
-  - **动态难度分级**（简单/中等/困难）
+  - **Dynamic Difficulty Grading** (Easy/Medium/Hard)
-    - LLM-as-a-Judge评估小模型可理解性
+    - LLM-as-a-Judge evaluates small model comprehensibility
-    - 简单链扩展 → 补充必要步骤
+    - Simple chain expansion → Adds necessary steps
-    - 困难链精简 → 移除高阶逻辑跳跃
+    - Hard chain simplification → Removes high-level logical leaps
-  - **验证机制**：迭代优化直至所有数据达"中等"评级
+  - **Validation Mechanism**: Iterative optimization until all data reaches "Medium" rating
 
-### 2. 性能突破
+### 2. Performance Breakthroughs
-- **32B模型**在GPQA Diamond基准接近10倍参数量的闭源模型
+- **32B Model** approaches the performance of closed-source models with 10x the parameters on the GPQA Diamond benchmark
-- **推理效率**显著提升（见下表对比）
+- **Significant Improvement in Reasoning Efficiency** (see comparison table below)
 
-| 模型                          | MMLU_PRO Tokens | AIME2024 Tokens | 速度增益 |
+| Model                          | MMLU_PRO Tokens | AIME2024 Tokens | Speed Gain |
-|--------------------------------|-----------------|-----------------|----------|
+|--------------------------------|-----------------|-----------------|------------|
-| DistilQwen2.5-R1-32B (慢思考)  | 4198            | 12178           | 1x       |
+| DistilQwen2.5-R1-32B (Slow-Thinking) | 4198            | 12178           | 1x         |
 | DistilQwen2.5-DS3-0324-32B     | 690             | 4177            | 5-8x       |
 
-## 技术优势
+## Technical Advantages
-- **双阶段蒸馏**：先压缩推理长度，再对齐认知轨迹
+- **Two-Stage Distillation**: First compresses reasoning length, then aligns cognitive trajectories
-- **动态数据优化**：自适应难度调整确保知识可迁移性
+- **Dynamic Data Optimization**: Adaptive difficulty adjustment ensures knowledge transferability
-- **开源兼容**：基于Qwen2.5基座模型微调
+- **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]
+
 ```

generation_config.json

@@ -1,14 +0,0 @@
-{
-  "bos_token_id": 151643,
-  "do_sample": true,
-  "eos_token_id": [
-    151645,
-    151643
-  ],
-  "pad_token_id": 151643,
-  "repetition_penalty": 1.05,
-  "temperature": 0.7,
-  "top_k": 20,
-  "top_p": 0.8,
-  "transformers_version": "4.46.1"
-}

model-00001-of-00004.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2936da2eb6666f60def42e354b4e36a9bad19ebdf5090cbc02c0834f92c27df5
-size 4877660776

model-00002-of-00004.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:11aab5fc3313e10f1aaa9438a2d74cfe5c59538dd8e6506ec216926887cc0820
-size 4932751008

model-00003-of-00004.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:2053eee73faf67773f7e8e042b2f3e9bf599edb6bca4e77a2cd0d83d375852de
-size 4330865200

model-00004-of-00004.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:30be9d72da5b630c956bcd0fa3725246d1c5891df7f9d0bec00f65ea7145114a
-size 1089994880