Hugging Face

How Much Memory Does Your Agent Actually Need?

Hugging Face investigates the amount of memory required to run AI agents.

Multi-Vector (Late Interaction) Embedding Models with Sentence Transformers

Hugging Face announced support for multi-vector late interaction embedding models using the Sentence Transformers library.

Same Cluster, 33 Points More Utilization: What Changed Was the Order

Hugging Face achieved a thirty-three point increase in cluster utilization by optimizing the execution order of workloads.

State of Open Models: Summer 2026 Observations

Record, train, and deploy from one place with Strands Agents, LeRobot, and Hugging Face Storage Buckets

What We Learned by Reproducing 2,200 papers from ICML

Introducing OlmoEarth embeddings: Custom embedding exports from OlmoEarth Studio for downstream analysis

LFM2.5-VL-3B for Better and Faster Vision Capabilities for the Edge