nanochat
Trains chatbot models and explores open-source LLM. Build AI chatbots from scratch with clear code.
Trains chatbot models and explores open-source LLM. Build AI chatbots from scratch with clear code.
nanochat lets you train your own ChatGPT-style model. This open-source project makes it easy to build AI chatbots from scratch. Train, fine-tune, and deploy your model with this complete LLM training pipeline.
Rust Tokenizer.
A lightning-fast custom tokenizer. It uses the Byte Pair Encoding (BPE) method for efficient text processing. With a 65,536-token vocabulary, the tokenizer achieves 4.8 characters per token compression. This increases the performance of the language model.
FineWeb-EDU Pretraining.
nanochat is pre-trained with the FineWeb-EDU dataset. This dataset contains high-quality education and web data. The language model gets a broad understanding of various topics. It also learns to generate coherent and relevant text.
Supervised Fine-Tuning (SFT).
The next step in training nanochat involves supervised fine-tuning (SFT). This process adjusts the base model to excel at specific tasks. The conversational data improves the model’s conversational capabilities. The inclusion of mathematical reasoning boosts analytical skills.
Reinforcement Learning (GRPO).
Optional reinforcement learning is available for maximizing model relevance. It uses a simplified version of Gradient Ratio Policy Optimization (GRPO) on tasks.
KV Cache Inference.
An inference engine with KV caching and a Python sandbox speeds up the model. This allows for faster generation speed with the help of the memory, allowing the user to get more information.
ChatGPT-Like Interface.
The platform includes command-line tools for quick execution. It also has a web interface to make chatting
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