README.md

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---

# **Multi-token prediction models and baselines**

Models accompanying the research paper "Better & Faster Large Language Models via Multi-token Prediction" (https://arxiv.org/abs/2404.19737).

Included are the following four 7B parameter models trained on code:
- baseline model (`n=1`) trained on 200B tokens of code: [7B_200B_1/](7B_200B_1/)
- multi-token prediction model (`n=4`) trained on 200B tokens of code: [7B_200B_4/](7B_200B_4/)
- baseline model (`n=1`) trained on 1T tokens of code: [7B_1T_1/](7B_1T_1/)
- multi-token prediction model (`n=4`) trained on 1T tokens of code: [7B_1T_4/](7B_1T_4/)

Tokenizer: standard Llama 2 SentencePiece tokenizer in [tokenizer.model](tokenizer.model).

## *Quickstart*

Install `torch`, `fairscale`, `fire` and `sentencepiece` and run
```
torchrun --nproc_per_node 1 example_completion.py --ckpt_dir 7B_200B_4/ --tokenizer_path tokenizer.model --max_seq_len 128 --max_batch_size 2
```
replacing `7B_200B_4` by the respective checkpoint directory.

## *Format*

The Pytorch `state_dicts` are compatible with Llama format: the layers of the shared trunk and the next-token prediction head layer are numbered contiguously. Additional prediction heads for tokens further in the future are names `extra_heads` and can be ignored for standard autoregressive inference.

The implementation of `forward()` in [llama/model.py](llama/model.py) provides an additional argument `return_all_heads`. If set, the additional prediction heads are called and the logits are returned in shape `(batch_size, seq_len, n_future_tokens, vocab_size)`. Otherwise, the logit's shape is `(batch_size, seq_len, 1, vocab_size)`.

## *Citation*

Gloeckle, F., Idrissi, B. Y., Rozière, B., Lopez-Paz, D., & Synnaeve, G. (2024). Better & faster large language models via multi-token prediction. arXiv preprint arXiv:2404.19737.

Bibtex entry:
```
@article{gloeckle2024better,
  title={Better \& faster large language models via multi-token prediction},
  author={Gloeckle, Fabian and Idrissi, Badr Youbi and Rozi{\`e}re, Baptiste and Lopez-Paz, David and Synnaeve, Gabriel},
  journal={arXiv preprint arXiv:2404.19737},
  year={2024}
}
```

## Feedback and comments
Please report risks as indicated in the Acceptable Use Policy and address bugs and any other comments to the corresponding authors as indicated in the research paper.