5 citations · 6 across the 11 of their papers we have counts for
11 papers
Prefix Sliding for efficient test-time scaling
Niklas Muennighoff, Zhengyang Wang, Zeyi Chen +15
Test-time scaling uses extra test-time compute to improve performance, such as letting language models reason longer when solving a problem. As models keep the entire reasoning tra…
Prime Agent: A Self-Improving RLM Harness
Seth Karten, Alex L. Zhang, Kevin Thomas +8
Language models are sequential processors, but long-horizon agency requires external information and computation beyond model weights and active context. Prime Agent is an open-sou…
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-…
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…
METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring
Ollie Liu, Sami Jaghouar, Johannes Hagemann +4
We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer model, which we refer to as a metagenomic foundation model, on a novel corpus of diverse metagenomic DNA an…
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…
INTELLECT-1 Technical Report
Sami Jaghouar, Jack Min Ong, Manveer Basra +9
In this report, we introduce INTELLECT-1, the first 10 billion parameter language model collaboratively trained across the globe, demonstrating that large-scale model training is n…
OpenDiLoCo: An Open-Source Framework for Globally Distributed Low-Communication Training
Sami Jaghouar, Jack Min Ong, Johannes Hagemann
OpenDiLoCo is an open-source implementation and replication of the Distributed Low-Communication (DiLoCo) training method for large language models. We provide a reproducible imple…
Improving traffic sign recognition by active search
S. Jaghouar, H. Gustafsson, B. Mehlig +2
We describe an iterative active-learning algorithm to recognise rare traffic signs. A standard ResNet is trained on a training set containing only a single sample of the rare class…