1 citations · 1 across the 7 of their papers we have counts for
6 papers · 1 filter
Shieldstral
Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273
We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…
Phoenix-VL 1.5 Medium Technical Report
Team Phoenix, :, Arka Ray +29
We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Singapore context. Developed as a…
EVE: A Domain-Specific LLM Framework for Earth Intelligence
Àlex R. Atrio, Antonio Lopez, Jino Rohit +6
We introduce Earth Virtual Expert (EVE), the first open-source, end-to-end initiative for developing and deploying domain-specialized LLMs for Earth Intelligence. At its core is EV…
Ministral 3
Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116
We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…
Reflect, Retry, Reward: Self-Improving LLMs via Reinforcement Learning
Shelly Bensal, Umar Jamil, Christopher Bryant +5
We explore a method for improving the performance of large language models through self-reflection and reinforcement learning. By incentivizing the model to generate better self-re…
Writing in the Margins: Better Inference Pattern for Long Context Retrieval
Melisa Russak, Umar Jamil, Christopher Bryant +4
In this paper, we introduce Writing in the Margins (WiM), a new inference pattern for Large Language Models designed to optimize the handling of long input sequences in retrieval-o…