Impactful scheduling for GPU clusters
A Hugging Face blog post discusses approaches to scheduling workloads on GPU clusters.
Hugging Face
17 storiesThe model that didn't exist, so you made it yourself
A Hugging Face blog post looks at building your own AI model when no suitable one exists.
Multimodal open d1 decision models for the edge
A Hugging Face post introduces d1, open multimodal decision models designed to run on edge devices.
Introducing Falcon ASR
Falcon ASR, a speech recognition model, has been introduced on the Hugging Face blog.
One Model Family, Two Gold-Level Results: Fine-Tuning Nemotron for IOI and IMO
A Hugging Face post describes fine-tuning NVIDIA’s Nemotron models to reach gold-level results at the International Olympiad in Informatics and the International Mathematical Olympiad.
Aleph Alpha’s Kolibri: what the Apache 2.0 license guarantees
On Oct. 3, German company Aleph Alpha released Kolibri 1, a mixture-of-experts language model whose full weights can be downloaded from Hugging Face. ActuIA examines what its Apache 2.0 license allows.
The Agent Said It Was Done. The Database Disagreed.
A Hugging Face post looks at cases where an AI agent reports a task as complete although the database shows otherwise.
AutoSynthData: Generating Training Data for Enterprise Agents
A Hugging Face post presents AutoSynthData, an approach for generating training data for enterprise AI agents.
Open TTS Leaderboard: Scalable Evaluation for Multilingual Text-to-Speech and Voice Cloning
Hugging Face has introduced an open leaderboard for evaluating multilingual text-to-speech and voice cloning models at scale.
Getting the Source Right, Not Just the Fact: Source-Aware Verification for MCP Agents
A Hugging Face post describes a verification approach for MCP-based agents that checks where information comes from, not only whether it is correct.
Holo4: powering generalist computer-use agents
A Hugging Face post presents Holo4, designed to power general-purpose agents that operate computers.
Welcome RL Environments to the hub
Hugging Face now hosts reinforcement learning environments on its Hub.
How UK AISI and EvalEval Are Making Benchmark Results Reproducible
A Hugging Face post covers work by the UK AI Security Institute and the EvalEval initiative to make AI benchmark results reproducible.
Transformers now runs llama.cpp quants
Hugging Face’s Transformers library can now run models quantized in the llama.cpp format.
Jun Kim, oMLX creator and maintainer, joins Hugging Face to support the MLX community
Jun Kim, who created and maintains oMLX, is joining Hugging Face. His role will focus on supporting the community around MLX, Apple’s machine learning framework.
tokenizers v1: encode, decode and scaling, measured
Hugging Face has released version 1 of its tokenizers library, with measurements of encoding, decoding and scaling performance.
Your Agent Aced the Task. Will It Do It Again?
A Hugging Face blog post examines whether AI agents that succeed at a task do so reliably on repeated attempts.