Hi! Great question — here's a high-level roadmap for going from "I have a big text dataset" to "I have something I can chat with."

There are three main stages, and each teaches the model something different:

1. Pretraining (this repo, nanoGPT/build-nanogpt)

This is what you get from following Karpathy's tutorials directly: train a decoder-only Transformer on raw text to predict the next token. The result is a base model — it can complete text plausibly, but it won't reliably follow instructions or hold a conversation. Ask it "What is the capital of France?" and it might just continue with more trivia questions instead of answering yours.

Your huge datasource is exactly what's needed here, but keep in mind: quality and diversity matter a lot. GPT-2/GPT-3 scale models were trained on hundreds of billions of tokens of diverse, deduplicated web text. If your dataset is narrow or small, you'll get a model that's fluent but not very capable.

2. Supervised Fine-Tuning (SFT)

To make it chat-like, you fine-tune the base model on a smaller dataset of (prompt, ideal response) pairs — basically examples of the behavior you want ("answer questions helpfully," "follow instructions," etc). This is what turns a text-completion engine into something that responds to you instead of just continuing text.

3. RLHF / preference-based fine-tuning (optional but what makes it feel like ChatGPT)

This further aligns the model with human preferences — training a reward model on ranked outputs, then optimizing the base+SFT model against it (PPO, DPO, etc). This is what gives you the "polished assistant" feel, though it's the most involved step and not strictly necessary to get something usable.

Practical suggestions

If you're learning: Karpathy's nanoGPT covers pretraining well. He also has a more complete project, nanochat, which walks through pretraining → SFT → a chat-capable model end-to-end — that's probably the most direct answer to "how do I get something I can ask questions to."
Realistically evaluate your compute: pretraining a capable model from scratch needs serious GPU resources (multiple A100/H100-class GPUs for days-to-weeks, depending on scale). If that's not available, a common path is to fine-tune an existing open base model (e.g. Llama, Mistral, Qwen) on your dataset instead of pretraining from zero — much cheaper and often gets you further, faster.
For inference/serving once you have a fine-tuned model, tools like llama.cpp, vllm, or Hugging Face's transformers + a simple chat loop/web UI make it easy to actually "talk" to your model.

Hope that helps orient you — happy to go deeper on any of these stages if useful!

how to build a model and interact with it like chatgpt? #131

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