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Raviraj Joshi

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2 papers

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NeurIPS Conference 2025 Conference Paper

Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning

Ali Taghibakhshi
Sharath Turuvekere Sreenivas
Saurav Muralidharan
Marcin Chochowski
Yashaswi Karnati
Raviraj Joshi
Ameya Mahabaleshwarkar
ZIJIA CHEN

Hybrid language models that combine Attention and State Space Models (SSMs) have been shown to achieve state-of-the-art accuracy and runtime performance. Recent work has also demonstrated that applying pruning and distillation to Attention-only models yields smaller, more accurate models at a fraction of the training cost. In this work, we explore the effectiveness of compressing Hybrid architectures. To this end, we introduce a novel group-aware pruning method for Mamba layers that preserves the structural integrity of SSM blocks and their sequence modeling capabilities. We combine this method with FFN, embedding dimension, and layer pruning, along with knowledge distillation-based retraining to obtain a unified compression recipe for hybrid models. Using this recipe, we compress the Nemotron-H 8B Hybrid model down to 4B parameters with up to $40\times$ fewer training tokens compared to similarly-sized models. The resulting model surpasses the accuracy of similarly-sized models while achieving $\sim2\times$ faster inference throughput, significantly advancing the Pareto frontier.

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NeurIPS Conference 2024 Conference Paper

Compact Language Models via Pruning and Knowledge Distillation

Saurav Muralidharan
Sharath Turuvekere Sreenivas
Raviraj Joshi
Marcin Chochowski
Mostofa Patwary
Mohammad Shoeybi
Bryan Catanzaro
Jan Kautz

Large language models (LLMs) targeting different deployment scales and sizes are currently produced by training each variant from scratch; this is extremely compute-intensive. In this paper, we investigate if pruning an existing LLM and then re-training it with a fraction <3% of the original training data can be a suitable alternative to repeated, full retraining. To this end, we develop a set of practical and effective compression best practices for LLMs that combine depth, width, attention and MLP pruning with knowledge distillation-based retraining; we arrive at these best practices through a detailed empirical exploration of pruning strategies for each axis, methods to combine axes, distillation strategies, and search techniques for arriving at optimal compressed architectures. We use this guide to compress the Nemotron-4 family of LLMs by a factor of 2-4x, and compare their performance to similarly-sized models on a variety of language modeling tasks. On these tasks, we perform better than Nemotron-3 8B and LLaMa2 7B using up to 40x fewer training tokens}, on par with Mistral 7B and Gemma 7B using up to 85x fewer tokens and slightly worse than LLaMa3 8B using up to 159x fewer tokens. Our models also compare favorably to state-of-the-art compression techniques from the literature.

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