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

Hugging Face (Twitter)

RT @ctnzr: Today we're releasing NVIDIA Nemotron Nano v2 - a 9B hybrid SSM that is 6X faster than similarly sized models, while also being more accurate.

Along with this model, we are also releasing most of the data we used to create it, including the pretraining corpus.

Links to the models, datasets, and tech report are here:

https://research.nvidia.com/labs/adlr/NVIDIA-Nemotron-Nano-2/

Hugging Face (Twitter)

RT @NielsRogge: Ok ngl this is cool! The end of LoRa's??

Powered by @FAL as inference provider. Try it out below! https://twitter.com/Alibaba_Qwen/status/1957500569029079083#m