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.