Technology Innovation Institute (TII) will make its Falcon-H1 large language model available as an NVIDIA NIM microservice. Falcon-H1 features a hybrid Transformer–Mamba architecture supporting context windows of up to 256k tokens. The model's availability on NVIDIA NIM aims to provide enterprises with a plug-and-play asset for building AI systems. Why it matters: This integration will simplify deployment and scaling of Falcon-H1 for enterprises, potentially accelerating the adoption of sovereign AI solutions in the region.
Technology Innovation Institute (TII) has released Falcon Mamba 7B, a new large language model and the first State Space Language Model (SSLM) in its Falcon series. Falcon Mamba 7B is the top-ranked open-source SSLM globally, outperforming Meta's Llama 3.1 8B, Llama 3 8B, and Mistral’s 7B on HuggingFace benchmarks. SSLMs excel at understanding complex, evolving situations and have applications in NLP tasks like machine translation and text summarization. Why it matters: This release strengthens the UAE's position as an AI hub, demonstrating TII's commitment to pioneering research and open-source AI development in the region.
Abu Dhabi’s Technology Innovation Institute (TII) has launched Falcon-H1 Arabic, a new large language model based on a hybrid Mamba-Transformer architecture. The Falcon-H1 family comes in 3B, 7B, and 34B parameter sizes and outperforms existing models on the Open Arabic LLM Leaderboard (OALL). The model features improvements in data quality, dialect coverage, and long-context stability. Why it matters: This release strengthens the UAE's position in Arabic AI and provides a high-performing model tailored to the linguistic and cultural needs of the region.
Technology Innovation Institute (TII) has launched Falcon H1R 7B, an open-source 7B parameter AI model with reasoning capabilities. It outperforms larger models like Microsoft Phi 4 Reasoning Plus 14B, Alibaba Qwen3 32B, and NVIDIA Nemotron H 47B on key benchmarks. The model uses a hybrid Transformer–Mamba architecture for improved accuracy and speed and is available on Hugging Face under the Falcon TII License. Why it matters: This release highlights the UAE's growing role in AI innovation by providing an efficient and accessible model for global research and development.
Researchers from MBZUAI have developed MMRINet, a Mamba-based neural network for efficient brain tumor segmentation in MRI scans. The model uses Dual-Path Feature Refinement and Progressive Feature Aggregation to achieve high accuracy with only 2.5M parameters, making it suitable for low-resource clinical environments. MMRINet achieves a Dice score of 0.752 and HD95 of 12.23 on the BraTS-Lighthouse SSA 2025 benchmark.
MBZUAI researchers developed GroupMamba, a new set of state-space models (SSMs) for computer vision that addresses limitations in existing SSMs related to computational efficiency and optimization challenges. GroupMamba introduces a new layer called modulated group mamba, improving efficiency and stability. In benchmark tests, GroupMamba performed as well as similar SSM systems, but more efficiently, offering a backbone for tasks like image classification, object detection, and segmentation. Why it matters: This research aims to bridge the gap between vision transformers and CNNs by improving SSMs, potentially leading to more efficient and powerful computer vision models.