A Blog post by Adrien Gallouët on Hugging Face
MiniMaxAI/OctoCodingBench · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
mem-agent: Persistent, Human Readable Memory Agent Trained with Online RL
A Blog post by Dria on Hugging Face
Carbon-A: Finding genes in known and unknown genomes
A Blog post by Hugging Face Biology Research on Hugging Face
The Environmental Impacts of AI -- Primer
A Blog post by Sasha Luccioni on Hugging Face
Menlo/Jan-nano-128k · Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
The State of On-Device LLMs
Xuan-Son Nguyen, an engineer at Hugging Face, specializes in on-device large language models (LLMs) and runtime optimization, working extensively with llam...
The State of On-Device LLMs
Xuan-Son Nguyen, an engineer at Hugging Face, specializes in on-device large language models (LLMs) and runtime optimization, working extensively with llam...
Train AI models with Unsloth and Hugging Face Jobs for FREE
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Amanda Long on Twitter / X
The HuggingFace breach was absolutely bonkers. More than 17,000 complex actions were coordinated over several days by an autonomous agent framework.And…the model successfully completed its goal.Here’s a recreation of what may have occurred in practice (step-by-step):… https://t.co/0ZyjN46JGl— Amanda Long (@_amanda_long) July 24, 2026
continuedev/instinct-data · Datasets at Hugging Face
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Generative artificial intelligence (AI) and machine learning (ML) models are being adopted across a variety of domains. As these technologies develop, there is notable diversity in their levels of availability and paths of diffusion. For example, fully closed-source models may be available through chatbots and API calls, but their weights, source code, training data, and other artifacts remain hidden from view. In contrast, open models make some or all of these materials publicly available for developers and downstream users.