11 citations · 55 across the 26 of their papers we have counts for
10 papers · 1 filter
Aya Expanse: Combining Research Breakthroughs for a New Multilingual Frontier
John Dang, Shivalika Singh, Daniel D'souza +42
We introduce the Aya Expanse model family, a new generation of 8B and 32B parameter multilingual language models, aiming to address the critical challenge of developing highly perf…
If You Can't Use Them, Recycle Them: Optimizing Merging at Scale Mitigates Performance Tradeoffs
Muhammad Khalifa, Yi-Chern Tan, Arash Ahmadian +6
Model merging has shown great promise at combining expert models, but the benefit of merging is unclear when merging "generalist" models trained on many tasks. We explore merging i…
Nexus: Specialization meets Adaptability for Efficiently Training Mixture of Experts
Nikolas Gritsch, Qizhen Zhang, Acyr Locatelli +2
Efficiency, specialization, and adaptability to new data distributions are qualities that are hard to combine in current Large Language Models. The Mixture of Experts (MoE) archite…
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Viraat Aryabumi, Yixuan Su, Raymond Ma +6
Including code in the pre-training data mixture, even for models not specifically designed for code, has become a common practice in LLMs pre-training. While there has been anecdot…
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
Qizhen Zhang, Nikolas Gritsch, Dwaraknath Gnaneshwar +8
The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from sc…
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs
John Dang, Arash Ahmadian, Kelly Marchisio +3
Preference optimization techniques have become a standard final stage for training state-of-art large language models (LLMs). However, despite widespread adoption, the vast majorit…
How Does Quantization Affect Multilingual LLMs?
Kelly Marchisio, Saurabh Dash, Hongyu Chen +4
Quantization techniques are widely used to improve inference speed and deployment of large language models. While a wide body of work examines the impact of quantization on LLMs in…
Aya 23: Open Weight Releases to Further Multilingual Progress
Viraat Aryabumi, John Dang, Dwarak Talupuru +18
This technical report introduces Aya 23, a family of multilingual language models. Aya 23 builds on the recent release of the Aya model (Üstün et al., 2024), focusing on pairing a…
Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model
Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong +14
Recent breakthroughs in large language models (LLMs) have centered around a handful of data-rich languages. What does it take to broaden access to breakthroughs beyond first-class…
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to t…