Meta drops AI bombshell: Multi-token prediction models now open for research

by | Jul 4, 2024 | Technology

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Meta has thrown down the gauntlet in the race for more efficient artificial intelligence. The tech giant released pre-trained models on Wednesday that leverage a novel multi-token prediction approach, potentially changing how large language models (LLMs) are developed and deployed.

In April we published a paper on a new training approach for better & faster LLMs using multi-token prediction. To enable further exploration by researchers, we’ve released pre-trained models for code completion using this approach on @HuggingFace ⬇️https://t.co/OnUsGcDpYx— AI at Meta (@AIatMeta) July 3, 2024

This new technique, first outlined in a Meta research paper in April, breaks from the traditional method of training LLMs to predict just the next word in a sequence. Instead, Meta’s approach tasks models with forecasting multiple future words simultaneously, promising enhanced performance and drastically reduced training times.

The implications of this breakthrough could be far-reaching. As AI models balloon in size and complexity, their voracious appetite for computational power has raised concerns about cost and environmental impact. Meta’s multi-token prediction method might offer a way to curb this trend, making advanced AI more accessible and sustainable.

Democratizing AI: The promise and perils of efficient language models

The potential of this new approach extends beyond mere efficiency gains. By predicting multiple tokens at once, these models may develop a more nuanced understanding of language structure and context. This could lead to improvements in tasks ranging from code generation to creative writing, potentially bridging the gap between AI and human-level language understanding.

Countdown to VB Transform 2024

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However, the democratization of such powerful AI tools is a double-edged sword. While it could level the playing field for researchers and smaller companies, it also lo …

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We want to hear from you! Take our quick AI survey and share your insights on the current state of AI, how you’re implementing it, and what you expect to see in the future. Learn More

Meta has thrown down the gauntlet in the race for more efficient artificial intelligence. The tech giant released pre-trained models on Wednesday that leverage a novel multi-token prediction approach, potentially changing how large language models (LLMs) are developed and deployed.

In April we published a paper on a new training approach for better & faster LLMs using multi-token prediction. To enable further exploration by researchers, we’ve released pre-trained models for code completion using this approach on @HuggingFace ⬇️https://t.co/OnUsGcDpYx— AI at Meta (@AIatMeta) July 3, 2024

This new technique, first outlined in a Meta research paper in April, breaks from the traditional method of training LLMs to predict just the next word in a sequence. Instead, Meta’s approach tasks models with forecasting multiple future words simultaneously, promising enhanced performance and drastically reduced training times.

The implications of this breakthrough could be far-reaching. As AI models balloon in size and complexity, their voracious appetite for computational power has raised concerns about cost and environmental impact. Meta’s multi-token prediction method might offer a way to curb this trend, making advanced AI more accessible and sustainable.

Democratizing AI: The promise and perils of efficient language models

The potential of this new approach extends beyond mere efficiency gains. By predicting multiple tokens at once, these models may develop a more nuanced understanding of language structure and context. This could lead to improvements in tasks ranging from code generation to creative writing, potentially bridging the gap between AI and human-level language understanding.

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications into your industry. Register Now

However, the democratization of such powerful AI tools is a double-edged sword. While it could level the playing field for researchers and smaller companies, it also lo …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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