nanochat 0.1.2
the minimal full-stack ChatGPT clone
Requires Python: >=3.9

# nanochat

![nanochat logo](dev/nanochat.png)

> The best ChatGPT that $100 can buy.

This repo is a full-stack implementation of an LLM like ChatGPT in a single, clean, minimal, hackable, dependency-lite codebase. nanochat is designed to run on a single 8XH100 node via scripts like [speedrun.sh](speedrun.sh), that run the entire pipeline start to end. This includes tokenization, pretraining, finetuning, evaluation, inference, and web serving over a simple UI so that you can talk to your own LLM just like ChatGPT. nanochat will become the capstone project of the course LLM101n being developed by Eureka Labs.

## Talk to it

To get a sense of the endpoint of this repo, you can currently find [nanochat d32](https://github.com/karpathy/nanochat/discussions/8) hosted on [nanochat.karpathy.ai](https://nanochat.karpathy.ai/). "d32" means that this model has 32 layers in the Transformer neural network. This model has 1.9 billion parameters, it was trained on 38 billion tokens by simply running the single script [run1000.sh](run1000.sh), and the total cost of training was ~$800 (about 33 hours training time on 8XH100 GPU node). While today this is enough to outperform GPT-2 of 2019, it falls dramatically short of moden Large Language Models like GPT-5. When talking to these micro models, you'll see that they make a lot of mistakes, they are a little bit naive and silly and they hallucinate a ton, a bit like children. It's kind of amusing. But what makes nanochat unique is that it is fully yours - fully configurable, tweakable, hackable, and trained by you from start to end. To train and talk to your own, we turn to...

## Quick start

The fastest way to feel the magic is to run the speedrun script [speedrun.sh](speedrun.sh), which trains and inferences the $100 tier of nanochat. On an 8XH100 node at $24/hr, this gives a total run time of about 4 hours. Boot up a new 8XH100 GPU box from your favorite provider (e.g. I use and like [Lambda](https://lambda.ai/service/gpu-cloud)), and kick off the training script:

```bash
bash speedrun.sh
```

Alternatively, since the script runs for 4 hours, I like to launch it like this inside a new screen session `speedrun` (and also log output to `speedrun.log`):

```bash
screen -L -Logfile speedrun.log -S speedrun bash speedrun.sh
```

See the [screen cheatsheet](https://gist.github.com/jctosta/af918e1618682638aa82) if you are less familiar. You can watch it go inside the screen session, or detach with `Ctrl-a d` and `tail speedrun.log` to view progress. Now wait 4 hours. Once it's done, you can talk to your LLM via the ChatGPT-like web UI. Make sure again that your local uv virtual environment is active (run `source .venv/bin/activate`), and serve it:

```bash
python -m scripts.chat_web
```

And then visit the URL shown. Make sure to access it correctly, e.g. on Lambda use the public IP of the node you're on, followed by the port, so for example [http://209.20.xxx.xxx:8000/](http://209.20.xxx.xxx:8000/), etc. Then talk to your LLM as you'd normally talk to ChatGPT! Get it to write stories or poems. Ask it to tell you who you are to see a hallucination. Ask it why the sky is blue. Or why it's green. The speedrun is a 4e19 FLOPs capability model so it's a bit like talking to a kindergartener :).

---

<img width="2672" height="1520" alt="image" src="https://github.com/user-attachments/assets/ed39ddf8-2370-437a-bedc-0f39781e76b5" />

---

You can also `cat report.md` file which appeared in the project directory and contains the "report card" of the run, i.e. a bunch of evaluations and metrics. At the very end, you'll see a summary table, for example:

---

- Characters: 333,989
- Lines: 8,304
- Files: 44
- Tokens (approx): 83,497
- Dependencies (uv.lock lines): 2,004

| Metric          | BASE     | MID      | SFT      | RL       |
|-----------------|----------|----------|----------|----------|
| CORE            | 0.2219   | -        | -        | -        |
| ARC-Challenge   | -        | 0.2875   | 0.2807   | -        |
| ARC-Easy        | -        | 0.3561   | 0.3876   | -        |
| GSM8K           | -        | 0.0250   | 0.0455   | 0.0758   |
| HumanEval       | -        | 0.0671   | 0.0854   | -        |
| MMLU            | -        | 0.3111   | 0.3151   | -        |
| ChatCORE        | -        | 0.0730   | 0.0884   | -        |

Total wall clock time: 3h51m

---

(Your table might be missing the RL number by default). For a lot more information around the speedrun script and what to look for and expect, please refer to the walkthrough that I posted in Discussions of the repo: ["Introducing nanochat: The best ChatGPT that $100 can buy"](https://github.com/karpathy/nanochat/discussions/1).

## Bigger models

Unsurprisingly, $100 is not enough to train a highly performant ChatGPT clone. In fact, LLMs are famous for their multi-million dollar capex. For our purposes, I think there are two more scales of interest. First is the ~$300 tier d26 model (i.e. depth=26) that trains in ~12 hours, which slightly outperforms GPT-2 CORE score. Second is the $1000 tier (~41.6 hours), just because it's a nice round number. But both of these are not yet fully supported and therefore not attached here in the master branch yet.

That said, to give a sense, the example changes needed for the [speedrun.sh](speedrun.sh) file to train a GPT-2 grade model d26 only involve three changes:

```bash
...
# you'll need to download more data shards for pretraining
# get the number of parameters, multiply 20 to get tokens, multiply by 4.8 to get chars,
# divide by 250 million to get number of shards. todo need to improve this...
python -m nanochat.dataset -n 450 &
...
# use --depth to increase model size. to not oom, halve device batch size 32 -> 16:
torchrun --standalone --nproc_per_node=8 -m scripts.base_train -- --depth=26 --device_batch_size=16
...
# make sure to use the same later during midtraining:
torchrun --standalone --nproc_per_node=8 -m scripts.mid_train -- --device_batch_size=16
```

That's it! The biggest thing to pay attention to is making sure you have enough data shards to train on (the code will loop and do more epochs over the same training set otherwise, decreasing learning speed a bit), and managing your memory/VRAM, primarily by decreasing the `device_batch_size` until things fit (the scripts automatically compensates by increasing the number of gradient accumulation loops, simply turning parallel compute to sequential compute).

And a bit more about computing environments that will run nanochat:

- The code will run just fine on the Ampere 8XA100 GPU node as well, but a bit slower.
- All code will run just fine on even a single GPU by omitting `torchrun`, and will produce ~identical results (code will automatically switch to gradient accumulation), but you'll have to wait 8 times longer.
- If your GPU(s) have less than 80GB, you'll have to tune some of the hyperparameters or you will OOM / run out of VRAM. Look for `--device_batch_size` in the scripts and reduce it until things fit. E.g. from 32 (default) to 16, 8, 4, 2, or even 1. Less than that you'll have to know a bit more what you're doing and get more creative.
- Most of the code is fairly vanilla PyTorch so it should run on anything that supports that - xpu, mps, or etc, but I haven't implemented this out of the box so it might take a bit of tinkering.

## Running on CPU / MPS

If you'd like to tinker with nanochat on your Macbook or a CPU machine, there is a work in progress [CPU|MPS PR](https://github.com/karpathy/nanochat/pull/88) up here. If you're on Macbook, use `--device_type=mps` when running `base_train.py`. See the PR and its diff for more. You're not going to get too far without GPU nodes, but at least you'll be able to run the code and maybe train a very tiny LLM with some patience.

## Questions

nanochat is designed to be short and sweet. One big advantage of this is that we can package up all of the files together and copy paste them to your favorite LLM to ask arbitrary questions. As an example, I like to package up the repo using the [files-to-prompt](https://github.com/simonw/files-to-prompt) utility like so:

```bash
files-to-prompt . -e py -e md -e rs -e html -e toml -e sh --ignore "*target*" --cxml > packaged.txt
```

This includes all py, rs, html, toml, sh files, excludes the `rustbpe/target` folder, and chooses the cxml output format. Everything is written to the `packaged.txt` file, which atm measures ~330KB (i.e. well below ~100K tokens for a state of the art LLM), and ~8K lines of code in 45 files.

Alternatively, I recommend using [DeepWiki](https://deepwiki.com/) from Devin/Cognition to ask questions of this repo. In the URL of this repo, simply change github.com to deepwiki.com, and you're off.

## Installation

To install nanochat for development or experimentation:

```bash
# Clone the repository
git clone https://github.com/karpathy/nanochat.git
cd nanochat

# Install dependencies (requires uv)
uv pip install .
```

**For GPU users:** The default installation includes CPU-only PyTorch. If you want GPU acceleration, install the appropriate PyTorch version after installation:

```bash
# For CUDA (Linux/Windows)
uv pip install torch --index-url https://download.pytorch.org/whl/cu121

# For ROCm (AMD GPUs)
uv pip install torch --index-url https://download.pytorch.org/whl/rocm6.0
```

## Tests

I haven't invested too much here but some tests exist, especially for the tokenizer. Run e.g. as:

```bash
python -m pytest tests/test_rustbpe.py -v -s
```

## Contributing

nanochat is nowhere finished. The goal is to improve the state of the art in micro models that are accessible to work with end to end on budgets of < $1000 dollars. Accessibility is about overall cost but also about cognitive complexity - nanochat is not an exhaustively configurable LLM "framework"; there will be no giant configuration objects, model factories, or if-then-else monsters in the code base. It is a single, cohesive, minimal, readable, hackable, maximally-forkable "strong baseline" codebase designed to run start to end and produce a concrete ChatGPT clone and its report card.

## Acknowledgements

- The name (nanochat) derives from my earlier project [nanoGPT](https://github.com/karpathy/nanoGPT), which only covered pretraining.
- nanochat is also inspired by [modded-nanoGPT](https://github.com/KellerJordan/modded-nanogpt), which gamified the nanoGPT repo with clear metrics and a leaderboard, and borrows a lot of its ideas and some implementation for pretraining.
- Thank you to [HuggingFace](https://huggingface.co/) for fineweb and smoltalk.
- Thank you [Lambda](https://lambda.ai/service/gpu-cloud) for the compute used in developing this project.
- Thank you to chief LLM whisperer 🧙‍♂️ Alec Radford for advice/guidance.

## Cite

If you find nanochat helpful in your research cite simply as:

```bibtex
@misc{nanochat,
  author = {Andrej Karpathy},
  title = {nanochat: The best ChatGPT that $100 can buy},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/karpathy/nanochat}
}
```

## License

MIT