6 papers
RTTC: Reward-Guided Collaborative Test-Time Compute
J. Pablo Muñoz, Jinjie Yuan
Test-Time Compute (TTC) has emerged as a powerful paradigm for enhancing the performance of Large Language Models (LLMs) at inference, leveraging strategies such as Test-Time Train…
The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text
Nikhil Kandpal, Brian Lester, Colin Raffel +24
Large language models (LLMs) are typically trained on enormous quantities of unlicensed text, a practice that has led to scrutiny due to possible intellectual property infringement…
SmolVLM: Redefining small and efficient multimodal models
Andrés Marafioti, Orr Zohar, Miquel Farré +14
Large Vision-Language Models (VLMs) deliver exceptional performance but require significant computational resources, limiting their deployment on mobile and edge devices. Smaller V…
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Loubna Ben Allal, Anton Lozhkov, Elie Bakouch +19
While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challe…
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Guilherme Penedo, Hynek KydlÃÄek, Loubna Ben allal +5
The performance of a large language model (LLM) depends heavily on the quality and size of its pretraining dataset. However, the pretraining datasets for state-of-the-art open LLMs…
Scaling Laws and Compute-Optimal Training Beyond Fixed Training Durations
Alexander Hägele, Elie Bakouch, Atli Kosson +3
Scale has become a main ingredient in obtaining strong machine learning models. As a result, understanding a model's scaling properties is key to effectively designing both the rig…