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--- |
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language: |
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- multilingual |
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license: other |
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license_name: kwaipilot-license |
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license_link: LICENSE |
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library_name: transformers |
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--- |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/Anditty/OASIS/refs/heads/main/Group.svg" width="60%" alt="Kwaipilot" /> |
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</div> |
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<hr> |
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<div align="center" style="line-height: 1;"> |
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<a href="https://huggingface.co/Kwaipilot/KAT-V1-40B" target="_blank"> |
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<img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-fcd022?style=for-the-badge&logo=huggingface&logoColor=000&labelColor"/> |
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</a> |
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<a href="https://arxiv.org/pdf/2507.08297" target="_blank"> |
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-2507.08297-b31b1b.svg?style=for-the-badge"/> |
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</a> |
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</div> |
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# News |
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- 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. |
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*** |
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# Introduction |
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**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. |
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Its development follows a concise two-stage training pipeline: |
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<table> |
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<thead> |
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<tr> |
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<th style="text-align:left; width:18%;">Stage</th> |
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<th style="text-align:left;">Core Idea</th> |
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<th style="text-align:left;">Key Techniques</th> |
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<th style="text-align:left;">Outcome</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td><strong>1. Pre-training</strong></td> |
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<td>Inject knowledge while separating “reasoning” from “direct answering”.</td> |
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<td> |
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<em>Dual-regime data</em><br> |
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• <strong>Think-off</strong> queries labeled via a custom tagging system.<br> |
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• <strong>Think-on</strong> queries generated by a multi-agent solver.<br><br> |
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<em>Knowledge Distillation + Multi-Token Prediction</em> for fine-grained utility. |
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</td> |
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<td>Base model attains strong factual and reasoning skills without full-scale pre-training costs.</td> |
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</tr> |
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<tr> |
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<td><strong>2. Post-training</strong></td> |
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<td>Make reasoning optional and efficient.</td> |
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<td> |
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<em>Cold-start AutoThink</em> — majority vote sets the initial thinking mode.<br> |
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<em>Step-SRPO</em> — intermediate supervision rewards correct <strong>mode selection</strong> and <strong>answer accuracy</strong> under that mode. |
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</td> |
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<td>Model triggers CoT only when beneficial, reducing token use and speeding inference.</td> |
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</tr> |
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</tbody> |
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</table> |
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*** |
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# Data Format |
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KAT produces responses in a **structured template** that makes the reasoning path explicit and machine-parsable. |
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Two modes are supported: |
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## Special Tokens |
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| Token | Description | |
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|-------|-------------| |
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| `<judge>` | Analyzes the input to decide whether explicit reasoning is needed. | |
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| `<think_on>` / `<think_off>` | Indicates whether reasoning is **activated** (“on”) or **skipped** (“off”). | |
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| `<think>` | Marks the start of the chain-of-thought segment when `think_on` is chosen. | |
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| `<answer>` | Marks the start of the final user-facing answer. | |
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*** |
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# 🔧 Quick Start |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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model_name = "Kwaipilot/KAT-V1-40B" |
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# load the tokenizer and the model |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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# prepare the model input |
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prompt = "Give me a short introduction to large language model." |
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messages = [ |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=65536, |
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temperature=0.6, |
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top_p=0.95, |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") |
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print("prompt:\n", prompt) |
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print("content:\n", content) |
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""" |
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prompt: |
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Give me a short introduction to large language model. |
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content: |
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<judge> |
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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. |
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</judge> |
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<think_off> |
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<answer> |
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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: |
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### Key Points: |
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1. **Training**: Trained on diverse text sources (books, websites, etc.) using deep learning. |
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2. **Capabilities**: |
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- Answer questions, generate text, summarize content, translate languages. |
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- Understand context, sentiment, and nuances in language. |
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3. **Architecture**: Often based on **transformer models** (e.g., BERT, GPT, LLaMA). |
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4. **Scale**: Billions of parameters, requiring massive computational resources. |
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5. **Applications**: Chatbots, content creation, coding assistance, research, and more. |
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### Examples: |
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- **OpenAI’s GPT-4**: Powers ChatGPT. |
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- **Google’s Gemini**: Used in Bard. |
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- **Meta’s LLaMA**: Open-source alternative. |
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### Challenges: |
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- **Bias**: Can reflect biases in training data. |
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- **Accuracy**: May hallucinate "facts" not grounded in reality. |
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- **Ethics**: Raises concerns about misinformation and job displacement. |
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LLMs represent a leap forward in natural language processing, enabling machines to interact with humans in increasingly sophisticated ways. 🌐🤖 |
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</answer> |
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""" |
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``` |
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*** |
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# Future Releases |
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Looking ahead, we will publish a companion paper that fully documents the **AutoThink training framework**, covering: |
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* Cold-start initialization procedures |
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* Reinforcement-learning (Step-SRPO) strategies |
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* Data curation and reward design details |
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At the same time, we will open-source: |
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* **Training resources** – the curated dual-regime datasets and RL codebase |
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* **Model suite** – checkpoints at 1.5B, 7B, and 13B parameters, all trained with AutoThink gating |
