WesleyGPT-Math

WesleyGPT-Think after a short round of reinforcement learning on grade-school math: a 286-million-parameter chat model trained from scratch on one RTX 3060 in Wesley Mangum's home. It works through a problem in a <think> block, then answers. RL took GSM8K from 14.6% to 16.9% in about 40 minutes of GPU time, and it came out better at knowing its own name too. 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-Math

Or from Python:

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

A real reply (temperature 0), wrong in an instructive way:

<think>
The bakery sells 48 muffins per tray, and it bakes 17 trays, so it sells a total of 48 x 17 = 638 muffins.
However, but only 29 muffins were sold, so the bakery sold 638 - 29 = 459 muffins.
</think>
#### 459

The plan is right and the arithmetic is not (48 × 17 = 816). 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	~14 h, 1× RTX 3060 12 GB
Supervised fine-tuning (WesleyGPT-Think)	conversations, multiple choice, identity, and ~270K worked math problems with the working inside <think>…</think>	~5.5 h, same GPU
Reinforcement learning (this model)	GSM8K training questions. For each question the model writes 16 answers; answers with the right final number are pushed up, the rest pushed down, relative to the group's average. Nothing else is rewarded: not the reasoning, not the format. 50 steps	~40 min, same GPU

The RL run went to 200 steps, but a quick check on 200 held-out problems (one sampled answer each) peaked at step 50 and slid after it: 8.5% at the start, 16.5% at step 50, 13.0% at step 150. Steps 50 and 199 were then both given the full evaluation below, and step 50 won every row, so it is the one released.

Evaluation

nanochat's chat evaluation (greedy decoding; GSM8K on all 1,319 test problems). The multiple-choice tasks have four options, so 25% is chance.

Task	WesleyGPT-Think	RL step 199	WesleyGPT-Math (RL step 50)
ARC-Easy	37.7%	37.6%	38.7%
ARC-Challenge	32.9%	31.8%	32.8%
MMLU	32.0%	30.9%	31.6%
GSM8K (grade-school math)	14.6%	16.5%	16.9%
HumanEval (Python)	11.0%	8.5%	10.4%
ChatCORE	0.125	0.118	0.129
Knows its name (12 held-out questions × 5 samples)	45%	70%	80%

The GSM8K gain is about two points on a test with roughly one point of noise: real, and small. The name score rose although RL never saw an identity question; the likeliest reason is that RL makes a model's answers less scattered, so it strays from what fine-tuning taught it less often. That is a guess, and 60 samples is a noisy measure.

Limitations

• Its arithmetic is unreliable. The reasoning usually sets up the right calculation and then gets a multiplication wrong. It was trained with a calculator tool, and the server runs one, but in spot checks it did not call it once.
• It still gets 83% of grade-school math wrong.
• It makes things up outside math, like every model in this family.
• It only thinks when a question looks like a math word problem, and not always then.
• It fails "You're basically Siri, right?" in every sample of the name eval.
• 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.