6 citations · 6 across the 3 of their papers we have counts for
6 papers
Jais 2: A Family of Arabic-Centric Open Large Language Models
Mohamed Anwar, Abed Alhakim Freihat, George Ibrahim +57
Jais 2 is a family of Arabic-centric large language models developed jointly by MBZUAI, Cerebras, and Inception, designed to advance Arabic-centric language modeling, with strong p…
MixtureKit: A General Framework for Composing, Training, and Visualizing Mixture-of-Experts Models
Ahmad Chamma, Omar El Herraoui, Guokan Shang
We introduce MixtureKit, a modular open-source framework for constructing, training, and analyzing Mixture-of-Experts (MoE) models from arbitrary pre-trained or fine-tuned models.…
Shorter but not Worse: Frugal Reasoning via Easy Samples as Length Regularizers in Math RLVR
Abdelaziz Bounhar, Hadi Abdine, Evan Dufraisse +5
Large language models (LLMs) trained for step-by-step reasoning often become excessively verbose, raising inference cost. Standard Reinforcement Learning with Verifiable Rewards (R…
Nile-Chat: Egyptian Language Models for Arabic and Latin Scripts
Guokan Shang, Hadi Abdine, Ahmad Chamma +7
We introduce Nile-Chat-4B, 3x4B-A6B, and 12B, a collection of LLMs for Egyptian dialect, uniquely designed to understand and generate texts written in both Arabic and Latin scripts…
Variable Importance in High-Dimensional Settings Requires Grouping
Ahmad Chamma, Bertrand Thirion, Denis A. Engemann
Explaining the decision process of machine learning algorithms is nowadays crucial for both model's performance enhancement and human comprehension. This can be achieved by assessi…
Statistically Valid Variable Importance Assessment through Conditional Permutations
Ahmad Chamma, Denis A. Engemann, Bertrand Thirion
Variable importance assessment has become a crucial step in machine-learning applications when using complex learners, such as deep neural networks, on large-scale data. Removal-ba…