| --- |
| license: apache-2.0 |
| language: |
| - en |
| tags: |
| - text-generation |
| - gpt |
| - nanogpt |
| - pytorch |
| - from-scratch |
| - small-language-model |
| - slm |
| - pretraining |
| - muon-optimizer |
| - rope |
| - single-gpu |
| - base-model |
| - research |
| pipeline_tag: text-generation |
| --- |
| |
|  |
|
|
| # GTM-3-base by OpenGCM |
|
|
| A ~75M parameter, decoder-only GPT-style **base** language model, trained |
| from scratch on a single RTX Pro 6000, using RoPE (rotary position |
| embeddings) |
|
|
| **This is a base (pretrained) model, not an instruction-tuned or chat |
| model.** It completes/continues text; it does not reliably follow |
| instructions or answer questions directly. It also has **no fine-tuning |
| for factual accuracy** — it can and does confidently generate fluent, |
| plausible-sounding but factually incorrect content. Treat it as a small, |
| from-scratch research/hobby model, not a production system. |
|
|
|
|
| ## Model details |
|
|
| - **Architecture**: nanoGPT-style decoder-only transformer with **RoPE** |
| (rotary position embeddings) instead of learned absolute position |
| embeddings |
| - 10 layers, 8 attention heads, 608 embedding dim |
| - ~74.9M parameters (weight-tied embeddings/output head) |
| - Context length: 1024 tokens |
| - Uses PyTorch's fused `scaled_dot_product_attention` (flash-attention kernel) |
| - MLP, LayerNorm, and overall block structure otherwise match the |
| previous GTM-v2-base recipe — RoPE is the one architectural change |
| in this version |
| - **Tokenizer**: `tiktoken` GPT-2 BPE encoding (`tiktoken.get_encoding("gpt2")`), |
| vocab size 50,257. No custom tokenizer was trained. |
| - **Optimizer**: Muon (for 2D weight matrices) + AdamW (for embeddings, |
| layernorms, biases) — a hybrid setup, following the approach popularized |
| in recent efficient-pretraining recipes. |
| - **Precision**: trained with bf16 autocast; released weights are fp32. |
| - **Training data**: streamed via HuggingFace `datasets`, mixed and |
| tokenized on the fly: |
| - FineWeb-Edu (`HuggingFaceFW/fineweb-edu`, `sample-10BT`) — 35% |
| - Cosmopedia-v2 (`HuggingFaceTB/cosmopedia-v2`) — 25% |
| - FineMath (`HuggingFaceTB/finemath`, `finemath-4plus`) — 15% |
| - TxT360 (`LLM360/TxT360`, `default` config) — 15% |
| - Wikipedia (`wikimedia/structured-wikipedia`, `enwiki_namespace_0` |
| config) — 10%, flattened from its nested article/section JSON schema |
| into plain text |
| |
| This mix replaces the raw FineWeb slice used in earlier GTM releases |
| with TxT360 (a more heavily deduplicated/curated web corpus) and adds a |
| dedicated Wikipedia slice for denser factual content — both aimed at |
| known weak points from earlier GTM models (noisy web text, unreliable |
| factual recall). |
|
|
| Note: code data (The Stack v2, StarCoderData, the-stack-smol) was again |
| left out of this base-pretraining mix — every BigCode-hosted code corpus |
| checked so far is gated behind a terms-of-use click-through on |
| HuggingFace. Code is planned as a separate SFT-stage release |
| (GTM-3-coder) rather than being forced into this mix. |
|
|
| ## Known limitations |
|
|
| - **No reliable factual recall.** E.g. prompted with "The capital of France |
| is", the model may not consistently produce "Paris" — it can produce |
| fluent but factually invented content instead. The added Wikipedia data |
| is intended to help this relative to earlier GTM releases, but this has |
| not yet been verified. |
| - **No code capability.** No code-specific training data was included (see |
| above). The model can produce code-*shaped* text (recognizing "write |
| code" prompts and using plausible syntax) but not functionally correct |
| code. |
| - **Not instruction-tuned.** Does not follow instructions or answer |
| questions in a chat-like way — it continues text. |
| - **Repetition tendency.** Base generation (greedy or low-temperature |
| sampling) can fall into repetition loops or lock onto structural |
| templates when it lacks a confident continuation. A repetition penalty |
| (see usage example) substantially reduces this. |
| - **Smaller than prior GTM releases**. This |
| was a deliberate choice for this run, not a regression, but it means |
| raw capability is likely lower in absolute terms even where the |
| architecture/data improvements help. |
|
|
| ## Usage |
|
|
| Requires `model.py` (included in this repo) alongside the checkpoint — |
| this is a plain PyTorch model, not a `transformers` `AutoModel`. |
|
|
| ```bash |
| pip install torch safetensors tiktoken |
| ``` |
|
|
| ```python |
| import json |
| import torch |
| from safetensors.torch import load_file |
| from model import GPT, GPTConfig |
| |
| with open("config.json") as f: |
| cfg_dict = json.load(f) |
| config = GPTConfig( |
| vocab_size=cfg_dict["vocab_size"], block_size=cfg_dict["block_size"], |
| n_layer=cfg_dict["n_layer"], n_head=cfg_dict["n_head"], |
| n_embd=cfg_dict["n_embd"], dropout=cfg_dict["dropout"], bias=cfg_dict["bias"], |
| rope_theta=cfg_dict.get("rope_theta", 10000.0), |
| ) |
| model = GPT(config) |
| state_dict = load_file("model.safetensors") |
| model.load_state_dict(state_dict) |
| model.eval() |
| |
| import tiktoken |
| enc = tiktoken.get_encoding("gpt2") |
| |
| prompt = "Once upon a time," |
| ids = enc.encode_ordinary(prompt) |
| x = torch.tensor([ids], dtype=torch.long) |
| |
| with torch.no_grad(): |
| out = model.generate( |
| x, max_new_tokens=128, temperature=0.8, top_k=50, |
| eot_token=enc.eot_token, repetition_penalty=1.3, |
| ) |
| |
| print(enc.decode(out[0].tolist())) |
| ``` |
|
|
| ## License |
|
|
| Apache 2.0 for this repo's contents (model weights, `model.py`, and this |
| README). The underlying training data retains its own licenses regardless |
| of the license on this trained model — FineWeb-Edu, Cosmopedia-v2, and |
| FineMath are each ODC-BY-1.0; TxT360 is ODC-BY-1.0; Wikipedia content via |
| `wikimedia/structured-wikipedia` is CC-BY-SA-4.0 + GFDL (see each |
| dataset's HF card for full terms and attribution requirements). This repo |
| distributes model weights, not the training data itself. |
