| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pipeline_tag: text-generation |
| base_model: Wasue/WesleyGPT-Base |
| datasets: |
| - karpathy/climbmix-400b-shuffle |
| - HuggingFaceTB/smol-smoltalk |
| - cais/mmlu |
| - openai/gsm8k |
| - meta-math/MetaMathQA |
| tags: |
| - nanochat |
| - trained-from-scratch |
| - small-language-model |
| - consumer-gpu |
| - reasoning |
| --- |
| <!-- Wesley wrote this --> |
|
|
| # WesleyGPT-Think |
|
|
| A 286-million-parameter chat model trained **from scratch on one RTX 3060** in |
| Wesley Mangum's home that works through math problems step by step in a |
| `<think>` block before answering: about 14 hours of pretraining, then about |
| 5.5 hours of fine-tuning. The thinking took grade-school math (GSM8K) from |
| 1% to 14.6%. Built on |
| [nanochat](https://github.com/karpathy/nanochat), Andrej Karpathy's open-source |
| project for training small chat models end to end; the training and serving code |
| is at [WasueM/WesleyGPT](https://github.com/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 |
|
|
| ```bash |
| git clone https://github.com/WasueM/WesleyGPT && cd WesleyGPT |
| uv sync --extra cpu --extra release |
| uv run python -m wesleygpt.release chat Wasue/WesleyGPT-Think |
| ``` |
|
|
| Or from Python: |
|
|
| ```python |
| from wesleygpt.release import fetch_release, load_release |
| model, tokenizer = load_release(fetch_release("Wasue/WesleyGPT-Think", "wesleygpt-think")) |
| ``` |
|
|
| Replies to math word problems look like this: |
|
|
| ``` |
| <think> |
| Tom has 3 boxes with 12 apples each, so he has a total of 3 * 12 = 36 apples. |
| He eats 5 apples, so he now has 36 - 5 = 31 apples left. |
| </think> |
| #### 31 |
| ``` |
|
|
| 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](https://huggingface.co/Wasue/WesleyGPT-Base)) | 1.32 billion tokens of [ClimbMix](https://huggingface.co/datasets/karpathy/climbmix-400b-shuffle) (2,520 steps × 524,288 tokens) | ~14 h, 1× RTX 3060 12 GB | |
| | Supervised fine-tuning | [SmolTalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) conversations, plus [MMLU](https://huggingface.co/datasets/cais/mmlu) (×3) and [GSM8K](https://huggingface.co/datasets/openai/gsm8k) (×4) training sets, plus a small slice of synthetic conversations about its own identity. The GSM8K solutions are rewritten so the working sits in `<think>…</think>` before a `#### N` answer, and ~240K [MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) problems (rephrasings of GSM8K's *training* set) are added in the same format. 1,038 steps | ~5.5 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](https://huggingface.co/Wasue/WesleyGPT) | WesleyGPT-Think | |
| |---|---|---|---| |
| | ARC-Easy | 23.6% | 36.8% | **37.7%** | |
| | ARC-Challenge | 25.1% | 32.3% | **32.9%** | |
| | MMLU | 26.9% | 31.8% | **32.0%** | |
| | GSM8K (grade-school math) | 0.0% | 1.1% | **14.6%** | |
| | HumanEval (Python) | 0.0% | 9.2% | **11.0%** | |
| | ChatCORE | 0.002 | 0.090 | **0.125** | |
| | Knows its name (12 held-out questions × 5 samples) | – | 28% | 45% | |
|
|
| ## Limitations |
|
|
| - **It makes things up.** Asked about Paris, it said the city borders the French |
| Riviera. |
| - **It only thinks when a question looks like a math word problem.** Every |
| thinking example it trained on was one; asked "What is 17 times 3?" it |
| answered conversationally, and wrongly. |
| - The final answer is bare (`#### 31`), the format it was trained on. |
| - It still gets 85% of grade-school math wrong. |
| - It only sometimes knows its own name (45% of unseen phrasings; this may be |
| within noise of the 28% of WesleyGPT, since the eval has only 60 samples). |
| - 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](https://huggingface.co/datasets/nvidia/Nemotron-ClimbMix), |
| from which the pretraining data is derived. The code is MIT. |
|
|
