diff --git a/README.md b/README.md
index 154df82..baeb4d5 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,75 @@
 ---
 license: apache-2.0
 ---
+
+## 📖 Introduction
+
+# DistilQwen2.5-DS3-0324 系列快思考推理模型
+
+## 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
+
+## 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
+
+- **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
+
+### 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）
+
+| 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     |
+
+## 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
+
+## 🚀 快速开始
+
+```python
+from modelscope import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "PAI/DistilQwen2.5-DS3-0324-32B",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("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，
+)
+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]
+```
\ No newline at end of file