WesleyGPT-LongContext

A 286-million-parameter chat model trained from scratch on one RTX 3060 in Wesley Mangum's home, fine-tuned to stay sharp deep into a long conversation. It is WesleyGPT-Think's training plus 100,000 long conversations stitched together from shorter ones, most of them with later questions that can only be answered from something said earlier. It is the best WesleyGPT at math (GSM8K 17.6%), and unlike Think it stays that good after eight earlier turns. Tell it something in passing, even a kind of fact it never trained on, and nine turns later it still knows. 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 an experiment, 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-LongContext

Or from Python:

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

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	Everything WesleyGPT-Think trained on (SmolTalk, MMLU ×3, GSM8K ×4 and ~240K MetaMathQA problems with the working in <think>…</think>, and its identity), plus 100,000 stitched conversations described below. 1,307 steps	~8 h, same GPU

Stitched conversations. Each one joins whole conversations drawn from SmolTalk, MMLU and MetaMathQA until it nearly fills the 2,048-token window, so the model practises answering its tenth question as well as its first. Pieces are drawn in proportion to each dataset's size: an earlier version drew the small datasets at fixed rates, repeated GSM8K and the identity set many extra times, and memorised them.

70% of stitched conversations also get one to three added turns that depend on an earlier one: recalling a fact the user mentioned in passing ("By the way, I drive a Subaru Outback" … "What car do I drive?"), naming the answer to an earlier math or quiz question, doing arithmetic on a number the user gave earlier, using a corrected fact rather than the original, and, rarely, saying "You haven't told me … yet" when the fact was never given.

The facts come in 160 kinds (relatives' names and cities, favorites, codes, colors, ages, jobs, days and times), each introduced in about 20 different ways, and the question sometimes comes straight after the fact rather than turns later. An earlier version trained on 13 kinds, always introduced the same way: it learned the phrasing rather than the skill, and for any other kind of fact it answered "You haven't told me … yet".

Evaluation

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

Task	WesleyGPT-Think	WesleyGPT-LongContext
ARC-Easy	37.7%	36.7%
ARC-Challenge	32.9%	31.7%
MMLU	32.0%	30.9%
GSM8K (grade-school math)	14.6%	17.6%
HumanEval (Python)	11.0%	8.5%
ChatCORE	0.125	0.117
Knows its name (12 held-out questions × 5 samples)	45%	52%

Deep into a conversation. GSM8K test problems asked after unrelated held-out conversation, 200 problems per depth:

Earlier tokens (≈ earlier turns)	0 (0)	400 (4)	800 (7)	1,300 (9)
WesleyGPT-Think	13%	6%	5%	9.5%
WesleyGPT-LongContext	18%	17.5%	19%	17%

Remembering what you told it. A fact is given at the start ("Before we start, one thing to remember: my locker code is 3407."), then unrelated held-out conversation, then the question. None of these six kinds of fact (a dog's name, a sister's name, home city, favorite color, favorite number, locker code), nor that way of introducing one, appears in training. 100 questions per depth:

Tokens between fact and question (≈ turns)	0 (0)	400 (4)	800 (7)	1,300 (9)
WesleyGPT-Think	64%	9%	6%	0%
WesleyGPT-LongContext	100%	100%	100%	98%

Kinds of fact it did train on (name, car, job and so on), with values it never saw: 100%, 93%, 81% and 75% at the same depths (Think 66%, 22%, 15%, 9%).

Limitations

• Its memory is for simple personal facts. The held-out test above uses kinds of fact close to the ones it trained on; something stranger (the color of your bike seat, a list of three things) is untested.
• It makes things up, like every model this size.
• It only thinks step by step when a question looks like a math word problem, and still gets 83% of grade-school math wrong.
• 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.