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EmbeddingGemma 2: an open, lightweight multimodal embedding model
We introduced EmbeddingGemma last year to provide a lightweight option for high-quality text embeddings, to help your apps organize, search, and connect information directly on consumer hardware. The developer community’s response blew past our expectations. With more than 20 million downloads, builders have used it t…
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The Shift to Tabular Foundation Models Tabular data is the backbone of enterprise machine learning. Customer records, transactions, sensor logs, claims, and orders all live in tables, and predicting churn, default, demand, or price from them is among the most common machine learning tasks in industry. For two decades,…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 7, 2026
An environment gives an agent a task, responds to its actions with observations, and scores the outcome. The resulting rewards can measure an agent's performance during evaluation or provide a learning signal during training. For an introduction to this interaction loop, see our blogpost on environments . Within the e…
Hugging FacePublished Sep 28, 2026
Cross-checked · Hugging Face BlogVerified Oct 7, 2026
Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use.
Hugging FacePublished Sep 22, 2026
Cross-checked · Hugging Face BlogVerified Oct 7, 2026
Our latest paper, ProvenanceGuard: Source-Aware Factuality Verification for MCP-Based LLM Agents (read it on Hugging Face , or on arXiv in the meantime), targets that gap. The failure mode we care about is one we call cross-source conflation: a claim that is true somewhere in the evidence, but attributed to the wrong…
Hugging FacePublished Sep 29, 2026
Cross-checked · Hugging Face BlogVerified Oct 7, 2026