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OPENGCM/GTM-3-base

🤗 Hugging Face sourcetext-generationapache-2.0106M params422 MBsafetensors✓ HF checksums availableupdated today
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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.

pip install torch safetensors tiktoken

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.