81 citations · 429 across the 42 of their papers we have counts for
6 papers · 1 filter
Llama Guard 3-1B-INT4: Compact and Efficient Safeguard for Human-AI Conversations
Igor Fedorov, Kate Plawiak, Lemeng Wu +17
This paper presents Llama Guard 3-1B-INT4, a compact and efficient Llama Guard model, which has been open-sourced to the community during Meta Connect 2024. We demonstrate that Lla…
SpinQuant: LLM quantization with learned rotations
Zechun Liu, Changsheng Zhao, Igor Fedorov +6
Post-training quantization (PTQ) techniques applied to weights, activations, and the KV cache greatly reduce memory usage, latency, and power consumption of Large Language Models (…
Bitune: Leveraging Bidirectional Attention to Improve Decoder-Only LLMs
Dawid J. Kopiczko, Tijmen Blankevoort, Yuki M. Asano
Decoder-only large language models typically rely solely on masked causal attention, which limits their expressiveness by restricting information flow to one direction. We propose…
InterroGate: Learning to Share, Specialize, and Prune Representations for Multi-task Learning
Babak Ehteshami Bejnordi, Gaurav Kumar, Amelie Royer +3
Jointly learning multiple tasks with a unified model can improve accuracy and data efficiency, but it faces the challenge of task interference, where optimizing one task objective…
Think Big, Generate Quick: LLM-to-SLM for Fast Autoregressive Decoding
Benjamin Bergner, Andrii Skliar, Amelie Royer +3
Large language models (LLMs) have become ubiquitous in practice and are widely used for generation tasks such as translation, summarization and instruction following. However, thei…
GPTVQ: The Blessing of Dimensionality for LLM Quantization
Mart van Baalen, Andrey Kuzmin, Ivan Koryakovskiy +6
In this work we show that the size versus accuracy trade-off of neural network quantization can be significantly improved by increasing the quantization dimensionality. We propose…