快手开源 Kwaipilot-AutoThink 思考模型，有效解决过度思考问题

新闻

• 在专为防止数据泄露设计的挑战性基准测试LiveCodeBench Pro中，Kwaipilot-AutoThink位列所有开源模型榜首，甚至超越了Seed和o3-mini等强大的专有系统。

介绍

KAT(Kwaipilot-AutoThink) 是一个开源大语言模型，通过学习何时生成显式思维
链及何时直接作答，有效缓解过度思考问题。

它的发展遵循简洁的两阶段训练流程：

阶段 核心思想 关键技术 成果 1. 预训练阶段 在分离\"推理\"与\"直接回答\"的同时注入知识 双机制数据
• 通过定制标签系统标注的非思考型查询
• 由多智能体求解器生成的思考型查询

知识蒸馏 + 多标记预测实现细粒度效用 基础模型在不承担完整预训练成本的情况下，获得强大的事实掌握和推理能力 2. 后训练阶段 使推理过程可选项且高效 冷启动自动思考 —— 多数表决设定初始思考模式
分步SRPO —— 中间监督机制奖励正确的模式选择及该模式下的答案准确性 模型仅在有益时触发思维链，减少标记使用并加速推理

数据格式

KAT生成的响应采用结构化模板，使推理路径清晰且机器可解析。
支持两种模式：

特殊标记

标记 描述 分析输入内容以判断是否需要显式推理。 / 表示推理功能是否启用（“on”）或跳过（“off”）。 当选择think_on时，标记思维链片段的开始。 标记最终面向用户答案的开始。

🔧 Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLMmodel_name = \"Kwaipilot/KAT-V1-40B\"# load the tokenizer and the modeltokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype=\"auto\", device_map=\"auto\")# prepare the model inputprompt = \"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 completiongenerated_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: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.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. 🌐🤖\"\"\"

无聊消遣方法