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Pretam Ray

Pretam
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AI & ML interests

NLP

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published a model 13 days ago
Pretam/t5-small-finetuned-xsum
upvoted an article 7 months ago
Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA
reacted to vladbogo's post with 👍 11 months ago
A recent paper titled "ShortGPT: Layers in Large Language Models are More Redundant Than You Expect" proposes a simple and effective approach to pruning Large Language Models (LLMs) by removing redundant layers. Key points: * Discovers significant redundancy across layers in LLMs, with some layers playing a negligible role for the final performance. * Defines a new metric called Block Influence (BI) to quantify the importance of each layer in an LLM. * Removes layers with low BI scores, achieving up to 25% reduction in parameters and computation while maintaining 92% of the LLM's performance. Congrats to the authors for their work! Paper: https://huggingface.co/papers/2403.03853
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