Update (2025-06-10):
The model has been updated to the latest version with improved performance and stability.
KwaiCoder-AutoThink-preview
is the first public
AutoThink
LLM released by the
Kwaipilot
team at Kuaishou.
The model merges
thinking
and
non‑thinking
abilities into a single checkpoint and
dynamically adjusts its reasoning depth
based on the input’s difficulty.
✨ Key Highlights
Feature
What it means
Benefit
Auto Think
Diverse
pre‑think
data teaches the model to predict task difficulty
Better choice of when to think
Step‑SRPO
Token‑wise GRPO variant with process‑level rewards
More stable RL, higher “think” & “no‑think” accuracy
Agentic Data
Automated cot cold start data generation
Stronger inference models before reinforcement learning
KD + MTP
1 teacher → many‑token prediction distillation
<1⁄30 pre‑train cost
Evaluation Results
🔧 Quick Start
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Kwaipilot/KwaiCoder-AutoThink-preview"# 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=32768,
temperature=0.6,
top_p=0.9,
)
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>This is a definitional query seeking a basic explanation, which can be answered with straightforward factual recall or a concise summary. Requires think-off mode.</judge><think off>Large Language Models (LLMs) are advanced artificial intelligence systems designed to understand and generate human-like text. They are trained on vast amounts of data to learn grammar, facts, reasoning, and context. Key features include: - **Scale**: Billions (or even trillions) of parameters, enabling complex pattern recognition. - **Versatility**: Can perform tasks like answering questions, writing code, summarizing text, and more. - **Adaptability**: Fine-tuned for specific uses (e.g., customer support, creative writing). Examples include OpenAI's GPT, Google's Gemini, and Meta's Llama. While powerful, LLMs may occasionally hallucinate or rely on outdated information. They’re transforming industries by automating text-based tasks and enhancing human productivity. Would you like a deeper dive into any aspect?"""
🏗️ TODO
Technical reports will soon be released.
The released version of the model with improved performance will be comming soon.
🚦 Limitations & Notes
The preview checkpoint may occasionally over‑ or under‑think outside training distribution.
Use responsibly; verify factual outputs, especially when disabling thought traces.
📜 License
This repository is licensed under the
MIT License
. The use of KwaiCoder-AutoThink models is subject to the Model License. KwaiCoder-AutoThink models supports commercial use.
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