NTP — 332m

Next-token prediction only — the no-mtp baseline. One of twelve checkpoints released with Multi-Token Prediction Done Right (arXiv:2610.38331): three objectives x four sizes, trained under an identical recipe so they are directly comparable.

Code: JaydenTeoh/Soft-MTP — configs/332m_fineweb.yaml.

Objective

Plain next-token cross-entropy. No MTP heads are built or trained (n_mtp: 0).

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("JaydenTeoh/ntp-332m-model", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("gpt2")          # the corpus tokenizer

ids = tok("The capital of France is", return_tensors="pt").input_ids
print(tok.decode(model.generate(ids, max_new_tokens=20)[0]))

trust_remote_code=True is required: the architecture (model_type: hf_mtp) is defined by the modelling files in this repo, not by transformers.

The tokenizer is stock GPT-2 — this repo ships no tokenizer files. vocab_size is padded from 50257 to 50304 for alignment; the extra rows are unused.

Model

No MTP heads: this is the plain next-token baseline the MTP arms are compared against.

Training

Single run per cell, one seed. The twelve checkpoints share seed, data order and schedule, so differences between them are attributable to the objective.

Citation

@article{teoh2026multitokenpredictionright,
  title={Multi-Token Prediction Done Right},
  author={Jayden Teoh},
  journal={arXiv preprint arXiv:2610.38331},
  year={2026},
}