7 papers
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
TiMoE: Time-Aware Mixture of Language Experts
Robin Faro, Dongyang Fan, Tamar Alphaidze +1
Large language models (LLMs) are typically trained on fixed snapshots of the web, which means that their knowledge becomes stale and their predictions risk temporal leakage: relyin…
Training Dynamics of the Cooldown Stage in Warmup-Stable-Decay Learning Rate Scheduler
Aleksandr Dremov, Alexander Hägele, Atli Kosson +1
Learning rate scheduling is essential in transformer training, where the final annealing plays a crucial role in getting the best performance. However, the mechanisms behind this c…
FineWeb2: One Pipeline to Scale Them All -- Adapting Pre-Training Data Processing to Every Language
Guilherme Penedo, Hynek Kydlíček, Vinko Sabolčec +7
Pre-training state-of-the-art large language models (LLMs) requires vast amounts of clean and diverse text data. While the open development of large high-quality English pre-traini…
Towards Fully FP8 GEMM LLM Training at Scale
Alejandro Hernández-Cano, Dhia Garbaya, Imanol Schlag +1
Despite the significant potential of FP8 data formats for large language model (LLM) pre-training, their adoption has been limited due to challenges in maintaining stability at sca…
URLs Help, Topics Guide: Understanding Metadata Utility in LLM Training
Dongyang Fan, Vinko Sabolčec, Martin Jaggi
Large Language Models (LLMs) are commonly pretrained on vast corpora of text without utilizing contextual metadata such as source, quality, or topic, leading to a context-free lear…