3 papers
cs.AI2026
Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling
Falcon LLM Team, Iheb Chaabane, Puneesh Khanna +8
This work introduces Falcon-H1R, a 7B-parameter reasoning-optimized model that establishes the feasibility of achieving competitive reasoning performance with small language models…
cs.CL2025
Falcon-H1: A Family of Hybrid-Head Language Models Redefining Efficiency and Performance
Jingwei Zuo, Maksim Velikanov, Ilyas Chahed +24
In this report, we introduce Falcon-H1, a new series of large language models (LLMs) featuring hybrid architecture designs optimized for both high performance and efficiency across…
cs.CL2025
VisCon-100K: Leveraging Contextual Web Data for Fine-tuning Vision Language Models
Gokul Karthik Kumar, Iheb Chaabane, Kebin Wu
Vision-language models (VLMs) excel in various visual benchmarks but are often constrained by the lack of high-quality visual fine-tuning data. To address this challenge, we introd…