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It serves as both an educational resource and a practical starting point for training small language models with modern techniques.\n\n## What nanochat Does\n- Trains a capable chatbot model from scratch on consumer hardware\n- Implements efficient training techniques that minimize compute requirements\n- Provides a complete pipeline from data preparation to interactive chat inference\n- Includes instruction tuning and RLHF-style alignment on a budget\n- Offers a reference implementation for understanding LLM training internals\n\n## Architecture Overview\nnanochat implements a transformer-based language model with a streamlined training pipeline. It uses a custom data loading system optimized for small-scale training, mixed-precision training with gradient accumulation, and a multi-stage pipeline covering pretraining, supervised fine-tuning, and preference optimization. The codebase is intentionally minimal to serve as a readable reference.\n\n## Self-Hosting & Configuration\n- Requires Python 3.10+ with PyTorch and a CUDA-capable GPU (RTX 3090 or better recommended)\n- Training configs are YAML files specifying model size, data paths, and hyperparameters\n- Pretrained checkpoints are available for skipping the pretraining phase\n- Inference runs on consumer GPUs or CPUs (slower) for interactive chat\n- No cloud dependencies; the entire pipeline runs on a single machine\n\n## Key Features\n- Complete LLM training pipeline in a minimal, readable codebase\n- Budget-friendly: full training from scratch costs under $100 in GPU compute\n- Multi-stage training covering pretraining, SFT, and preference optimization\n- Educational code with clear documentation explaining each component\n- Checkpoint compatibility with common inference frameworks for deployment\n\n## Comparison with Similar Tools\n- **minimind** — similar educational LLM trainer; nanochat includes alignment and chat-specific training stages\n- **nanoGPT** — Karpathy's earlier project for pretraining only; nanochat extends to full chat model training\n- **llama.cpp** — inference-focused; nanochat covers the training side of the pipeline\n- **Axolotl** — fine-tuning toolkit; nanochat provides the full training stack from scratch\n\n## FAQ\n**Q: What GPU is needed for training?**\nA: An RTX 3090 or 4090 is sufficient for the default model configuration.\n\n**Q: Can I use my own training data?**\nA: Yes. The data pipeline accepts JSONL formatted conversation data.\n\n**Q: How does the output quality compare to commercial models?**\nA: nanochat produces a capable conversational model, though it does not match frontier models trained on much larger budgets.\n\n**Q: Is this suitable for production deployment?**\nA: nanochat is primarily educational. 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