Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+---
+---
+language:
+- multilingual
+license: other
+license_name: kwaipilot-license
+license_link: LICENSE
+library_name: transformers
+---
+<div align="center">
+  <img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" />
+</div>
+
+<hr>
+
+<div align="center" style="line-height: 1;">
+  <a href="https://huggingface.co/Kwaipilot/KAT-V1-40B" target="_blank">
+    <img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/>
+  </a>
+  
+  <a href="https://arxiv.org/pdf/2507.08297" target="_blank">
+    <img alt="arXiv" src="https://img.shields.io/badge/arXiv-2507.08297-b31b1b.svg?style=for-the-badge"/>
+  </a>
+</div>
+
+# News
+
+- We released the technical report of the **KAT-V1 model**, available at https://arxiv.org/pdf/2507.08297.
+- Kwaipilot-AutoThink ranks first among all open-source models on [LiveCodeBench Pro](https://livecodebenchpro.com/), a challenging benchmark explicitly designed to prevent data leakage, and even surpasses strong proprietary systems such as Seed and o3-mini.
+
+***
+
+# Introduction
+
+**KAT (Kwaipilot-AutoThink)** is an open-source large-language model that mitigates *over-thinking* by learning **when** to produce explicit chain-of-thought and **when** to answer directly. 
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/zdnsvBmv6hWIC2Qxxy1fD.png)
+
+Its development follows a concise two-stage training pipeline:
+
+<table>
+  <thead>
+    <tr>
+      <th style="text-align:left; width:18%;">Stage</th>
+      <th style="text-align:left;">Core Idea</th>
+      <th style="text-align:left;">Key Techniques</th>
+      <th style="text-align:left;">Outcome</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1. Pre-training</strong></td>
+      <td>Inject knowledge while separating “reasoning” from “direct answering”.</td>
+      <td>
+        <em>Dual-regime data</em><br>
+        • <strong>Think-off</strong> queries labeled via a custom tagging system.<br>
+        • <strong>Think-on</strong> queries generated by a multi-agent solver.<br><br>
+        <em>Knowledge Distillation&nbsp;+&nbsp;Multi-Token Prediction</em> for fine-grained utility.
+      </td>
+      <td>Base model attains strong factual and reasoning skills without full-scale pre-training costs.</td>
+    </tr>
+    <tr>
+      <td><strong>2. Post-training</strong></td>
+      <td>Make reasoning optional and efficient.</td>
+      <td>
+        <em>Cold-start AutoThink</em> — majority vote sets the initial thinking mode.<br>
+        <em>Step-SRPO</em> — intermediate supervision rewards correct <strong>mode selection</strong> and <strong>answer accuracy</strong> under that mode.
+      </td>
+      <td>Model triggers CoT only when beneficial, reducing token use and speeding inference.</td>
+    </tr>
+  </tbody>
+</table>
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/cwFAEh7Rl3f4FU46z8gBZ.png)
+
+
+***
+
+# Data Format
+
+
+KAT produces responses in a **structured template** that makes the reasoning path explicit and machine-parsable.  
+Two modes are supported:
+
+
+![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/61ee40a269351366e29972ad/H8iAvQMMT02nyvlYnI5q1.jpeg)
+
+
+## Special Tokens
+
+| Token | Description |
+|-------|-------------|
+| `<judge>` | Analyzes the input to decide whether explicit reasoning is needed. |
+| `<think_on>` / `<think_off>` | Indicates whether reasoning is **activated** (“on”) or **skipped** (“off”). |
+| `<think>` | Marks the start of the chain-of-thought segment when `think_on` is chosen. |
+| `<answer>` | Marks the start of the final user-facing answer. |
+
+
+***
+
+# 🔧 Quick Start
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "Kwaipilot/KAT-V1-40B"
+
+# load the tokenizer and the model
+tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+
+# prepare the model input
+prompt = "Give me a short introduction to large language model."
+messages = [
+    {"role": "user", "content": prompt}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# conduct text completion
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=65536,
+    temperature=0.6,
+    top_p=0.95,
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() 
+content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n")
+print("prompt:\n", prompt)
+print("content:\n", content)
+"""
+prompt:
+Give me a short introduction to large language model.
+content:
+<judge>
+The user's request is to provide a concise factual introduction to large language models, which involves retrieving and summarizing basic information. This task is straightforward as it only requires recalling and presenting well-known details without deeper analysis. No complex reasoning is needed here—just a simple explanation will suffice.
+</judge>
+
+<think_off>
+<answer>
+A **Large Language Model (LLM)** is an advanced AI system trained on vast amounts of text data to understand, generate, and process human-like language. Here’s a concise introduction:
+
+### Key Points:
+1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning.
+2. **Capabilities**: 
+   - Answer questions, generate text, summarize content, translate languages.
+   - Understand context, sentiment, and nuances in language.
+3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA).
+4. **Scale**: Billions of parameters, requiring massive computational resources.
+5. **Applications**: Chatbots, content creation, coding assistance, research, and more.
+
+### Examples:
+- **OpenAI’s GPT-4**: Powers ChatGPT.
+- **Google’s Gemini**: Used in Bard.
+- **Meta’s LLaMA**: Open-source alternative.
+
+### Challenges:
+- **Bias**: Can reflect biases in training data.
+- **Accuracy**: May hallucinate "facts" not grounded in reality.
+- **Ethics**: Raises concerns about misinformation and job displacement.
+
+LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖
+</answer>
+"""
+```
+
+***
+
+# Future Releases
+
+Looking ahead, we will publish a companion paper that fully documents the **AutoThink training framework**, covering:
+
+* Cold-start initialization procedures  
+* Reinforcement-learning (Step-SRPO) strategies  
+* Data curation and reward design details  
+
+At the same time, we will open-source:
+
+* **Training resources** – the curated dual-regime datasets and RL codebase  
+* **Model suite** – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating
+
+
+# Citation
+
+```
+@techreport{Zhan2025KATV1,
+  title={KAT-V1: Kwai-AutoThink Technical Report},
+  author={Zizheng, Zhan and Ken, Deng and Huaixi, Tang and Wen, Xiang and Kun, Wu and others},
+  year={2025},
+  institution={arXiv preprint arXiv:2507.08297},
+  number={arXiv:2507.08297},
+  url={https://arxiv.org/abs/2507.08297}
+}
+```