Hugging Face (Twitter)

RT @abhinavexists_: Finally done with deploy my upscaling model on @huggingface.
> implementing Multi-Recurrent Branches from scratch.
> currently upscaling with a PSNR of 34.2 db , will be improving it.

hf deployment:
https://huggingface.co/Abhinavexists/SeeSharp

Hugging Face (Twitter)

RT @gm8xx8: Gemma 3 270M joins the family.
⮕ More smol models, please.

Hugging Face (Twitter)

RT @TencentHunyuan: We've heard the community! 📣📣📣

Following the open-source release of our Hunyuan 3D World Model 1.0, we're excited to introduce the new 1.0-Lite version, optimized for consumer-grade GPUs!
This is the first open-source, explorable world generation model compatible with CG pipelines, now more accessible than ever.

Key Technical Optimizations:
🔹Dynamic FP8 Quantization: We’ve cut VRAM requirements by 35%—from 26GB to under 17GB—making it easy to run on consumer GPUs without compromising performance.
🔹SageAttention Quantization: Our method quantizes the Q, K, and V matrices in the Transformer to INT8, combined with dynamic smoothing and hardware optimizations, to achieve an inference speedup of over 3x with less than 1% precision loss.
🔹Cache Algorithm Acceleration: By optimizing redundant time steps, we've significantly improved inference efficiency for a smoother user experience.

Now, developers can run a complex world model...

Перейти на оригинальный пост

Hugging Face (Twitter)

RT @xenovacom: Google just released their smallest Gemma model ever: Gemma 3 270M! 🤯
🤏 Highly compact & efficient
🤖 Strong instruction-following capabilities
🔧 Perfect candidate for fine-tuning

It's so tiny that it can even run 100% locally in your browser with Transformers.js! 🤗

Hugging Face (Twitter)

RT @vanstriendaniel: On @huggingface Hub!

https://huggingface.co/datasets/YiboZhang2001/TexVerse https://twitter.com/YiboZhang2001/status/1956302856358711689#m

Hugging Face (Twitter)

RT @QGallouedec: 🚨 Big news! We decided that @huggingface’s post-training library, TRL, will natively supports training Vision Language Models 🖼️

This builds on our recent VLM support in SFTTrainer — and we’re not stopping until TRL is the #1 VLM training library 🥇

More here 👉 hf.co/blog/trl-vlm-alignment
Huge thanks to @mervenoyann , @SergioPaniego , and @ariG23498 🔥

Hugging Face (Twitter)

RT @Tu7uruu: 🚀 Big update: Open ASR goes multilingual!

We’re kicking off with 🇩🇪🇫🇷🇮🇹🇪🇸🇵🇹 — German, French, Italian, Spanish & Portuguese.
English ASR has reached a strong level of maturity, so we’re exploring new languages 🌍

More languages coming soon... Which one should we add next?

Hugging Face (Twitter)

RT @Zai_org: Just saw GLM-4.5V is trending #2 on Hugging Face
https://huggingface.co/zai-org/GLM-4.5V

Hugging Face (Twitter)

RT @kadirnardev: We have released our LFM2-350M based TTS model as open source 🚀 We have also released many different FT models.

GPU Platform: @hyperbolic_labs
Data: Emilia + Emilia Yodas(EN)
LLM Model: LFM2-350M @LiquidAI_
Disk and Space: @huggingface

I'm very happy to have released this model as open source. Many thanks to @VyvoSmartChain

#opensource #speech #tts #huggingface #lfm #gpu

Hugging Face (Twitter)

RT @jetbrains: We didn’t just build Mellum for us.

We open-sourced it for everyone.

Props to @huggingface for helping us get it out there 👌

Find out more about Mellum here: jb.gg/mbz8bq

Hugging Face (Twitter)

RT @mervenoyann: how does DINOv3 perceive objects? 👀

I dropped a mini visualizer: you can upload images, click on objects and check
> patch similarities
> object boundaries
> most similar other objects 🤗

live on @huggingface Spaces

Hugging Face (Twitter)

RT @reach_vb: NVIDIA ON A ROLL! Canary 1B and Parakeet TDT (0.6B) SoTA ASR models - Multilingual, Open Source 🔥

- 1B and 600M parameters
- 25 languages
- automatic language detection and translation
- word and sentence timestamps
- transcribe up to 3 hours of audio in one go
- trained on 1 Million hours of data
- SoTA on Open ASR Leaderboard

- CC-BY licensed 💥

Available on Hugging Face, go check them out today!

Hugging Face (Twitter)

RT @Xianbao_QIAN: ToonComposer: You can now efficiently make cartoons on @huggingface for free

- Input: sketch based key frames + color reference frame
- This @Alibaba_Wan based model will combine in-betweening & colorization
- Model can also imagine areas left blank with a prompt
- Result: save up to 70% of manual work.

Huge thanks to B&T Studio and Gudong Animation Studio for their permission to use their animation content (Big Fish & Begonia and Mr. Miao) for academic illustration.

Hugging Face (Twitter)

RT @reach_vb: BEST PART: they released the entire 1 MILLION hours of data publicly on Hugging Face 🤯 https://twitter.com/reach_vb/status/1957148807562723809#m

Hugging Face (Twitter)

RT @dylan_ebert_: I automated my research discovery.

Claude Code + Hugging Face MCP + Research MCP (my server)

It makes discovering and keeping track of all related research artifacts MUCH faster and easier

here's how it works 👇

‌Hugging Face (Twitter)

RT @arundhati1504: 🎉 Introducing Granary — a 1M-hour, open multilingual speech dataset — plus new #opensource ASR models. 🌍
.
🤗 Now on HuggingFace: nvda.ws/3Jg0BwV

🔗 Learn more: nvda.ws/41DVP2s bit.ly/4mGyMMA

‌Hugging Face (Twitter)

RT @AdinaYakup: Before my vacation: Qwen releasing.
When I came back: Qwen still releasing
Respect!!🫡

Meet Qwen Image Edit 🔥 the image editing version of Qwen-Image by @Alibaba_Qwen
https://huggingface.co/Qwen/Qwen-Image-Edit

✨ Apache 2.0
✨ Semantic + Appearance Editing: rotate, restyle, add/remove 🎨
✨ Precise Text Editing → edit CN/EN text, keep style

‌Hugging Face (Twitter)

RT @VentureBeat: Nvidia releases a new small, open model Nemotron-Nano-9B-v2 with toggle on/off reasoning

‌Hugging Face (Twitter)

RT @ClementDelangue: Do we have an org or posts with all the cool Japanese releases on HF? Which ones are the most interesting ones?

Hugging Face (Twitter)

RT @gm8xx8: NVIDIA Nemotron-Nano v2

Models: 12B Base, 9B Reasoning, 9B Base
- Arch: Hybrid Mamba2–Transformer (128K ctx, 4 attn layers)
- Training: 10.6T tokens (3.5T synthetic from DeepSeek, Qwen, Nemotron-4, phi-4, etc.)
- 15 natural languages + 43 programming languages
- Datasets: Nemotron-CC v2 + Nemotron-CC-Math (133B tokens, 5.5× FineMath)

Benchmarks
- Math: 91.4 GSM8K CoT, 63.6 MATH L5, +30→56.7 AIME
- Code: 58.5 HumanEval+, 58.9 MBPP+
- Commonsense: 90.7 ARC, 79.9 HellaSwag
- Long-context: 82.2 RULER-128K

Highlights
- Nemotron-CC-Math: First scalable pipeline using Lynx + LLM cleanup to preserve LaTeX + code in web data. Delivers SOTA boosts (+12.6 MATH, +14.3 MBPP+) vs prior open math sets
- Efficiency: Distilled 12B→9B (480B tokens), ~1.5e24 FLOPs, ~724 MWh disclosed
- Deployment: Hugging Face, NGC, NeMo, TRT-LLM, vLLM | GPU-optimized
- Open: Models, datasets, and full extraction pipelines released