WesleyGPT

A 286-million-parameter chat model trained from scratch on one RTX 3060 in Wesley Mangum's home: about 14 hours of pretraining, then about 5 hours of fine-tuning to hold a conversation. Built on nanochat, Andrej Karpathy's open-source project for training small chat models end to end; the training and serving code is at WasueM/WesleyGPT.

It is a small model and it sounds like one: fluent, confident, and often wrong. It is a demonstration of what a single consumer GPU can do, not an assistant to rely on.

Try it

git clone https://github.com/WasueM/WesleyGPT && cd WesleyGPT
uv sync --extra cpu --extra release
uv run python -m wesleygpt.release chat Wasue/WesleyGPT

Or from Python:

from wesleygpt.release import fetch_release, load_release
model, tokenizer = load_release(fetch_release("Wasue/WesleyGPT", "wesleygpt-chat"))

It runs on a laptop CPU. It is not a transformers model, so AutoModel will not load it.

Model

Parameters	286,261,730
Layers / width / heads	12 / 768 / 6 (query and key-value)
Context	2,048 tokens, sliding-window attention (3 short-window layers, then 1 full)
Tokenizer	32,768-token byte-level BPE, trained by nanochat on the pretraining data
Precision	bfloat16 weights

Training

Stage	Data	Compute
Pretraining (WesleyGPT-Base)	1.32 billion tokens of ClimbMix (2,520 steps × 524,288 tokens)	~14 h, 1× RTX 3060 12 GB
Supervised fine-tuning	SmolTalk conversations, plus MMLU (×3) and GSM8K (×4) training sets, plus a small slice of synthetic conversations about its own identity, 934 steps	~5 h, same GPU

Evaluation

nanochat's chat evaluation, before and after fine-tuning. The multiple-choice tasks have four options, so 25% is chance.

Task	Base (pretrained only)	WesleyGPT (chat)
ARC-Easy	23.6%	36.8%
ARC-Challenge	25.1%	32.3%
MMLU	26.9%	31.8%
GSM8K (grade-school math)	0.0%	1.1%
HumanEval (Python)	0.0%	9.2%
ChatCORE	0.002	0.090
Knows its name (12 held-out questions × 5 samples)	–	28%

Limitations

• It makes things up. Asked about Paris, it said the city borders the French Riviera.
• It only sometimes knows its own name. Fine-tuning on identity conversations raised correct self-identification on unseen phrasings from 0% to 28%; the rest of the time it falls back on generic assistant personas from its chat data.
• Arithmetic and multi-step reasoning are close to zero (GSM8K 1.1%).
• English only. No safety tuning beyond what the fine-tuning data carries.

License

CC-BY-NC-4.0 for these weights: free to use, share, and adapt with credit, but not commercially. That matches the license of NVIDIA's Nemotron-ClimbMix, from which the pretraining data is derived. The code is MIT.