Update README.md

README.md

@@ -25,178 +25,31 @@ library_name: transformers
 
 # 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}
-}
-```