10 papers
Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks
Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32
Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…
Arcee Trinity Large Technical Report
Varun Singh, Lucas Krauss, Sami Jaghouar +23
We present the technical report for Arcee Trinity Large, a sparse Mixture-of-Experts model with 400B total parameters and 13B activated per token. Additionally, we report on Trinit…
INTELLECT-3: Technical Report
Prime Intellect Team, Mika Senghaas, Fares Obeid +20
We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-…
TOPLOC: A Locality Sensitive Hashing Scheme for Trustless Verifiable Inference
Jack Min Ong, Matthew Di Ferrante, Aaron Pazdera +5
Large language models (LLMs) have proven to be very capable, but access to frontier models currently relies on inference providers. This introduces trust challenges: how can we be…
Prime Collective Communications Library -- Technical Report
Michael Keiblinger, Mario Sieg, Jack Min Ong +2
This report presents the Prime Collective Communications Library (PCCL), a novel fault-tolerant collective communication library designed for distributed ML workloads over the publ…
INTELLECT-2: A Reasoning Model Trained Through Globally Decentralized Reinforcement Learning
Prime Intellect Team, Sami Jaghouar, Justus Mattern +11
We introduce INTELLECT-2, the first globally distributed reinforcement learning (RL) training run of a 32 billion parameter language model. Unlike traditional centralized training…