卡帕西 8000 行代码手搓 ChatGPT，成本仅100美元，训练 12 小时 CORE 表现超越GPT-2

Core Insights - The article discusses the launch of "nanochat," a simplified version of ChatGPT created by Andrej Karpathy, which can be built with minimal resources and code [1][2][4]. - The project aims to provide an accessible framework for training language models, emphasizing ease of use and modification [11][13]. Project Overview - "Nanochat" is a full-stack training and inference pipeline that allows users to create a basic ChatGPT-like model with approximately 8000 lines of code [2][4]. - The total cost to train this model is around $100, using a cloud GPU server for about 4 hours [4][16]. - The model is built using Rust and includes a custom tokenizer, with training conducted on the FineWeb dataset [5][19]. Performance Metrics - After approximately 12 hours of training, the model's performance on the CORE metric surpasses that of GPT-2 [8]. - Specific performance metrics include: - CORE: 0.2219 - ARC-Easy: 0.3876 - GSM8K: 0.0758 - HumanEval: 0.0854 - MMLU: 0.3151 [7][56]. Training Process - The training process involves several stages: pre-training, mid-training, supervised fine-tuning (SFT), and reinforcement learning (RL) [45][50]. - The pre-training phase utilizes a large dataset to teach the model about the world, while mid-training focuses on adapting the model for conversational tasks [28][45]. - The SFT phase further refines the model using high-quality dialogue data [48]. Community Engagement - The project has gained significant attention, with over 4.8k stars on GitHub shortly after its release, indicating strong community interest [14]. - The framework is designed to be easily modifiable, allowing users to experiment with different parameters and configurations [59]. Future Potential - Karpathy envisions "nanochat" evolving into a research tool or benchmark framework, similar to previous projects like nanoGPT [13]. - The project is still in its early stages, with potential for further optimization and enhancement [13][50].