Hugging Face

Introducing EmbeddingGemma 2, our new open embeddings model for on-device use cases!

👀 Code, image, video, audio, and text embeddings
🎛️ Modular footprint (270M text/code up to 740M full multimodal)
🧪 Matryoshka embeddings (truncate down to 128d for 6x storage savings)
🚀 +14% boost on MTEB Code vs. v1
🤗 Apache 2.0 licenseCheck out the complete developer guide with code samples:

Microduck got its new brain!

Our first fully custom SBC in a Pollen robot! It replaces the Radxa + board sandwich from the prototype.

Built by Seeed around the same Rockchip CPU but including better memory, better WiFi/BT, dual NFC antennas, user-facing status LEDs, an RGB flashlight, and major improvements in cooling, cable management and boot speed.

First revision arrived, and we got everything running on it within a day. Pretty good day :)

A big milestone on the road to shipping!

🚨 The incentives to share scientific knowledge openly are crumbling.

Historically, researchers shared scientific knowledge for two primary reasons: (1) the prestige conferred by discovery, and (2) a sense of expanding the pool of human knowledge. Even within industry, scientific discovery was far too vast an endeavor for a single scientist or team to tackle alone. So scientists explored different frontiers, usually published their findings, and collectively moved the field forward.

However, as more scientific knowledge is uncovered simply by throwing massive compute at autonomous agents with minimal human intervention, these incentives are breaking down: (1) discoveries are becoming commoditized. If anyone could have found the same result simply by running the same agentic prompt with enough compute, the prestige of discovery and the pride in one's work disappears, a shift we are already witnessing in software engineering. (2) the explosion of low-quality, low-barrier publications makes genuine breakthroughs increasingly difficult to filter, discover, and appropriately credit. (3) and for companies, keeping discoveries proprietary is increasingly advantageous. Instead of publishing and waiting for the broader scientific community to build upon the work, an organization can simply turn its own compute back onto the problem to iterate faster internally.

In the short term, the incentives reward secrecy. But my goal -- and at Hugging Face, our goal -- is the sustainable, long-term expansion of human knowledge that survives individual, closed companies. So we have to build new tools that reward and facilitate open sharing of knowledge. If agents are accelerating discovery, we are going to figure out how to use agents and other tools to preserve scientific integrity.

Starting with this: Paper Reproductions with ML Intern -- we’ve made it possible to take a published paper and task agents with independently reproducing its experiments, helping create more signals (e.g. open artifacts) that can help identify high-quality work.

It's crazy that I can run MiMo 2.6 Flash across my RTX 6000 GPU and my M5 laptop at 40 tokens/sec over 10 GbE 🤯

These are the native mxfp4 weights of a state-of-the-art model, on heterogeneous hardware.

Supported out of the box in llama.cpp.

Today we release Open d1: two open-weight multimodal models in our d1 decision model family.

> d1-3B: text + vision
> d1-omni-600M: text + image or text + audio
> Real-time decision making anywhere, from data centers such as @nvidia DGX to RTX workstations to Jetson at the edge.

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