AI news for: Natural Language Processing
Explore AI news and updates focusing on natural language processing for the last 7 days.
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Pruning and Distilling LLMs Using NVIDIA TensorRT Model Optimizer
Large language models (LLMs) have set a high bar in natural language processing (NLP) tasks such as coding, reasoning, and math. However, their deploy...
TL;DR
NVIDIA demonstrates a method that combines structured weight pruning with knowledge distillation for compressing large language models into smaller, efficient variants without significant loss in quality.
Key Takeaways:
Key Takeaways:
- Pruning and distillation are highly cost-effective methods to shrink LLMs while matching or exceeding baseline accuracy across domains.
- Research shows that width pruning typically achieves better accuracy than depth pruning, though depth pruning often reduces inference latency more at the same number of parameters.
- The 6B pruned model demonstrates a significant advancement in performance compared to its 4B counterpart, achieving a 30% increase in speed and a 2.5% increase in accuracy on the MMLU benchmark.
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14
Oct
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Oct
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Oct
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Oct
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Oct
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Oct
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