multi-token-prediction

Model overview

The multi-token-prediction model, developed by Facebook, is a 7B parameter language model trained on code. It is accompanied by a set of baseline models trained on 200 billion and 1 trillion tokens of code. The multi-token prediction model differs from the baseline models in that it is trained to predict multiple tokens at once, rather than just the next single token. This approach can lead to faster generation of code-like text.

The model is compatible with the standard LLaMA 2 SentencePiece tokenizer, which is included in the repository. The implementation of the model's forward pass allows for returning either the standard next-token logits or the logits for multiple future tokens.

Model inputs and outputs

Inputs

• Text prompts: The model takes in text prompts as input, similar to other autoregressive language models.
• return_all_heads flag: An optional flag that can be set to return the logits for multiple future tokens, rather than just the next token.

Outputs

• Next token logits: The standard output is the logits for the next token in the sequence.
• Multi-token logits: If the return_all_heads flag is set, the model will return the logits for multiple future tokens, with a shape of (batch_size, seq_len, n_future_tokens, vocab_size).

Capabilities

The multi-token-prediction model is designed to generate code-like text more efficiently than a standard single-token prediction model. By predicting multiple tokens at once, the model can produce longer stretches of coherent code-like output with fewer model evaluations. This could be useful for applications that require the generation of code snippets or other structured text.

What can I use it for?

The multi-token-prediction model could be used for a variety of applications that involve the generation of code-like text, such as:

• Automated code completion: The model could be used to suggest or generate the next few tokens in a code snippet, helping programmers write code more quickly.
• Code generation: The model could be used to generate entire functions, classes, or even full programs based on a high-level prompt.
• Text summarization: The model's ability to predict multiple tokens at once could be leveraged for efficient text summarization, particularly for technical or code-heavy documents.

Things to try

One interesting aspect of the multi-token-prediction model is its ability to return the logits for multiple future tokens. This could be useful for exploring the model's understanding of code structure and semantics. For example, you could try:

• Providing a partial code snippet as a prompt and seeing how the model's predictions for the next few tokens evolve.
• Experimenting with different values for the n_future_tokens parameter to see how the model's uncertainty and confidence changes as it looks further into the future.
• Analyzing the patterns in the model's multi-token predictions to gain insights into its understanding of common code structures and idioms.

Overall, the multi-token-prediction model provides an interesting approach to language modeling that could have applications in a variety of code-related tasks.