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Natural Language Processing (NLP) Course (CS613) – IIT Gandhinagar

While large language models (LLMs) excel in many domains, their complexity and scale challenge deployment in resource-limited environments. Current compression techniques, such as parameter pruning, often fail to effectively utilize the knowledge from pruned parameters. To address these challenges, we propose Manifold-Based Knowledge Alignment and Layer Merging Compression (MKA), a novel approach that uses manifold learning and the Information Bottleneck (IB) measure to merge similar layers, reducing model size while preserving essential performance. We evaluate MKA on multiple benchmark datasets and various LLMs. Our findings show that MKA not only preserves model performance but also achieves substantial compression ratios, outperforming traditional pruning methods. Moreover, when coupled with quantization, MKA delivers even greater compression. Specifically, on the MMLU dataset using the Llama3-8B model, MKA achieves a compression ratio of 43.75% with a minimal performance decrease of only 2.82%. The proposed MKA method offers a resource-efficient and performance-preserving model compression technique for LLMs. We make our code available at https://github.com/SempraETY/Pruning-...

These presentations were created as part of the NLP course at IIT Gandhinagar.

Paper Title: Pruning via Merging: Compressing LLMs via Manifold Alignment Based Layer Merging

Presented By:
Laksh Jain
Nishchay Bhutoria
Soham Gaonkar
Aditya Borate
Parthiv Patel
Rudra Pratap Singh
Aryan Solanki

For more information, visit the course website: https://sites.google.com/iitgn.ac.in/...