ReALLM: A general framework for LLM compression and fine-tuning

21 May 2024

Papers citing "ReALLM: A general framework for LLM compression and fine-tuning"

7 / 7 papers shown

Title
Mitigating the Impact of Outlier Channels for Language Model Quantization with Activation Regularization Aniruddha Nrusimha Mayank Mishra Naigang Wang Dan Alistarh Rameswar Panda Yoon Kim MQ 60 8 0 04 Apr 2024
Gemma: Open Models Based on Gemini Research and Technology Gemma Team Gemma Team Thomas Mesnard Cassidy Hardin Robert Dadashi Surya Bhupatiraju ... Armand Joulin Noah Fiedel Evan Senter Alek Andreev Kathleen Kenealy VLM LLMAG 123 415 0 13 Mar 2024
ApiQ: Finetuning of 2-Bit Quantized Large Language Model Baohao Liao Christian Herold Shahram Khadivi Christof Monz CLL MQ 34 12 0 07 Feb 2024
QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks Albert Tseng Jerry Chee Qingyao Sun Volodymyr Kuleshov Christopher De Sa MQ 126 91 0 06 Feb 2024
Extreme Compression of Large Language Models via Additive Quantization Vage Egiazarian Andrei Panferov Denis Kuznedelev Elias Frantar Artem Babenko Dan Alistarh MQ 98 87 0 11 Jan 2024
The Falcon Series of Open Language Models Ebtesam Almazrouei Hamza Alobeidli Abdulaziz Alshamsi Alessandro Cappelli Ruxandra-Aimée Cojocaru ... Quentin Malartic Daniele Mazzotta Badreddine Noune B. Pannier Guilherme Penedo AI4TS ALM 113 389 0 28 Nov 2023
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi Jose Caballero Ferenc Huszár J. Totz Andrew P. Aitken Rob Bishop Daniel Rueckert Zehan Wang SupR 190 5,138 0 16 Sep 2016