metadata

language:
  - multilingual
license: other
license_name: kwaipilot-license
license_link: LICENSE
library_name: transformers

News

Kwaipilot-AutoThink ranks first among all open-source models on LiveCodeBench Pro, 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.

Its development follows a concise two-stage training pipeline:

Stage	Core Idea	Key Techniques	Outcome
1. Pre-training	Inject knowledge while separating “reasoning” from “direct answering”.	Dual-regime data • Think-off queries labeled via a custom tagging system. • Think-on queries generated by a multi-agent solver. Knowledge Distillation + Multi-Token Prediction for fine-grained utility.	Base model attains strong factual and reasoning skills without full-scale pre-training costs.
2. Post-training	Make reasoning optional and efficient.	Cold-start AutoThink — majority vote sets the initial thinking mode. Step-SRPO — intermediate supervision rewards correct mode selection and answer accuracy under that mode.	Model triggers CoT only when beneficial, reducing token use and speeding inference.

Data Format

KAT produces responses in a structured template that makes the reasoning path explicit and machine-parsable.
Two modes are supported:

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

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