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Post #2034 22
Hugging Face (Twitter)

RT @mishig25: At @huggingface, how we created doc chatbot system using open weights LLMs & open source software stack:

1. chunk & embed docs markdowns using open weights embedding models (BAAI/bge-base-en-v1.5, google/embeddinggemma-300m, we usually use http://hf.co/spaces/mteb/leaderboard to find the hottest models), use @meilisearch (http://github.com/meilisearch/meilisearch) as our semantic search db

2. hf.co/mcp that acts as gateway to semantically search hf docs (among other things). http://github.com/huggingface/hf-mcp-server by @evalstate

3. add hf.co/mcp to hf.co/chat, which uses latest open weights LLMs such as zai-org/GLM-4.6 (hf.co/models) through HF Inference Providers (http://hf.co/docs/inference-providers) that runs your inference through @FireworksAI_HQ, @togethercompute, @GroqInc, @novita_labs
Post #2032 15
Hugging Face (Twitter)

RT @osanseviero: Introducing Gemma Scope 2

🤗Largest open release of interpretability tools (over 1 trillion parameters trained!)
🔬Works as a microscope to analyze all Gemma 3 models' internal activations
🗣️Advanced tools for analyzing chat behaviors
Post #2031 17
Hugging Face (Twitter)

RT @jeffboudier: New perks for Enterprise organizations to engage with the AI community on @huggingface
📢 show your company with your profile activity
💼 show you're hiring on your profile

I show you how in the video
Post #2027 12
Hugging Face (Twitter)

RT @osanseviero: Introducing T5Gemma 2, the next generation of encoder-decoder models 🚀

Built on top of Gemma 3, we were able to build compact models at sizes of 270m-270m, 1B-1B, and 4B-4B sizes.

While most models today are decoder-only, T5Gemma 2 is the first (I'm aware of) multimodal, long-context, and heavily multilingual (140 languages) encoder-decoder model out there.

We hope this model enables the model research community as well as the community of devs ready to explore with new architectures.

Blog: https://blog.google/technology/developers/t5gemma-2
Models: https://huggingface.co/collections/google/t5gemma-2
Paper: arxiv.org/abs/2512.14856
Post #2025 9
Hugging Face (Twitter)

RT @xenovacom: NEW: Google releases FunctionGemma, a lightweight (270M), open foundation model built for creating specialized function calling models! 🤯

To test it out, I built a small game: use natural language to solve fun physics simulation puzzles, running 100% locally in your browser! 🕹️
Post #2022 9
Hugging Face (Twitter)

RT @MarioNawfal: GROK JUST TURNED VOICE AI INTO A REAL PRODUCT, FAST, AND EVERYWHERE

xAI just opened Grok Voice to developers, and this isn’t some early experiment dressed up as a launch.

It’s the same system already running inside millions of Teslas, now exposed through an API that actually works in the real world.

Speed is the first thing that jumps out.

Grok responds in under a second, which means conversations flow instead of stalling.

That alone separates it from most voice AI people tolerate rather than enjoy.

Language is the next flex.

Grok handles dozens of them, switches mid-sentence, and keeps accents and tone intact.

It sounds like someone talking, not software guessing.

Then there’s capability.

Grok can search live data, pull from X, use tools, plan routes, and control systems.

In Teslas, it already acts like a co-pilot, not a novelty.

No token gymnastics.

Source: @xai
Post #2021 10
Hugging Face (Twitter)

RT @jeffboudier: We landed SAM3 in Microsoft Foundry! 💥

You can use it to build image and video editing apps. 🎨📲

SAM3 by @AIatMeta offers state of the art image AND video segmentation and understanding 🏆

@alvarobartt made it available for easy deployment in Microsoft Foundry 😍

And created a step-by-step guide to walk you though it ⤵️
Post #2019 13
Hugging Face (Twitter)

RT @itazapo: Transformers v5 redesigns tokenization.

In this blog post we talk about:
> tokenization crash course
> tokenizers and transformers - the bridge
> v5 tokenizers backend

Major shoutout to @karpathy for his BPE video that got me interested in tokenization in the first place.
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