How does multi-token prediction improve LLM performance?
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Answers ( 3 )
Multi-token prediction improves LLM performance by increasing sample efficiency and reducing GPU memory usage. It achieves significant performance gains in coding and natural language tasks, with up to 3x faster inference speeds. For example, a 13B parameter model solved 12% more problems on HumanEval and 17% more on MBPP.
The potential applications of multi-token prediction include:
- Training LLMs for higher efficiency and performance, particularly in generative tasks like coding.
- Real-time applications due to faster inference speeds, such as speculative decoding for 2.7x to 3x acceleration.
Multi-token prediction is increasingly effective for larger model sizes. Performance gains become more pronounced as model parameters increase, making it particularly useful for models with parameters ranging from 300M to 13B, trained on extensive datasets.