Hugging Face

Hugging Face (Twitter)

RT @acemusicAI: We're releasing ACE-Step-v1.5(2B), a fast, high-quality open-source music model.

It runs locally on a consumer-grade GPU, generates a full song in under 2 seconds(on an A100), supports LoRA fine-tuning, and beats SUNO on common eval metrics.

GitHub: https://github.com/ace-step/ACE-Step-1.5

Key traits:
Quality: beats Suno on common eval scores
Speed: full song under 2s on A100
Local: ~4GB VRAM, under 10s on RTX 3090
LoRA: train your own style with a few songs
License: MIT, free for commercial use
Data: fully authorized plus synthetic

The music AI space lacks commercial-grade open models. Many creators are forced to rely on closed-source services, and can’t fully own, run locally, or fine-tune their own models. We want to help change that.

Hugging Face (Twitter)

RT @allen_ai: Since launching Open Coding Agents, it's been exciting to see how quickly the community has adopted them. Today we're releasing SERA-14B – a new 14B-parameter coding model – plus a major refresh of our open training datasets. 🧵

Hugging Face (Twitter)

RT @Alibaba_Qwen: 🚀 Introducing Qwen3-Coder-Next, an open-weight LM built for coding agents & local development.
What’s new:
🤖 Scaling agentic training: 800K verifiable tasks + executable envs
📈 Efficiency–Performance Tradeoff: achieves strong results on SWE-Bench Pro with 80B total params and 3B active
✨ Supports OpenClaw, Qwen Code, Claude Code, web dev, browser use, Cline, etc
🤗 Hugging Face: https://huggingface.co/collections/Qwen/qwen3-coder-next
🤖 ModelScope: https://modelscope.cn/collections/Qwen/Qwen3-Coder-Next
📝 Blog: https://qwen.ai/blog?id=qwen3-coder-next
📄 Tech report: https://github.com/QwenLM/Qwen3-Coder/blob/main/qwen3_coder_next_tech_report.pdf

Hugging Face (Twitter)

RT @hcompany_ai: Holo2-235B-A22B: #1 on ScreenSpot-Pro, #1 on OSWorldG 🎯

🚀 Today, we are releasing Holo2-235B-A22B 🤗, our most capable GUI localization model yet! Holo2 now leads on all major GUI localization benchmarks: 78.5% on ScreenSpot-Pro and 79.0% on OSWorld-G!

Hugging Face (Twitter)

RT @Wauplin: New in huggingface_hub: teach your AI coding assistant the hf CLI in one command

$ hf skills add --claude

Works with Claude Code, Codex, and @opencode . Your AI assistant now knows how to search the Hub, download models, manage repos, and more.

Hugging Face (Twitter)

RT @AdinaYakup: GLM @Zai_org just entered the OCR field🔥

https://huggingface.co/zai-org/GLM-OCR

✨ 0.9B
✨ MIT licensed
✨ Multimodal GLM-V architecture
✨ #1 on OmniDocBench v1.5 (94.62)

Hugging Face (Twitter)

🤗 https://twitter.com/leonliuzx/status/2018554408028791063#m

Hugging Face (Twitter)

RT @StepFun_ai: ⚡️ Step 3.5 Flash is coming: Fast Enough to Think. Reliable Enough to Act！

We’re dropping our most capable open-source foundation model yet. Frontier reasoning meets extreme efficiency.

It leverages a sparse Mixture of Experts (MoE) architecture, 196B total → 11B active.

Key Capabilities:
✅Reasoning at Speed: MTP-3 powered throughput at 100–300 tok/s (350 tok/s peak for single-stream coding tasks).
✅Agentic Power: ⚡️ 74.4% SWE-bench Verified ⚡️ 51.0% Terminal-Bench 2.0. Proven stability for complex, long-horizon tasks.
✅256K Efficient Context: 3:1 SWA ratio + Full Attention. Massive datasets or long codebases support with minimal overhead. Consistent performance, hybrid efficiency.
✅Local-First Deployment: Optimized for Mac Studio M4 Max, NVIDIA DGX Spark. Secure, private, and frontier-capable. Your data, your hardware, your agent.

You can try Step 3.5 Flash right now:
👉 OpenRouter:...

Перейти на оригинальный пост

Hugging Face (Twitter)

RT @mervenoyann: huge fan of IBM lately, they drop open-source agents and eval suites for solid real-world problems 👏

Dec they dropped CUGA, an agent to automate boring CRM tasks

now they drop AssetOpsBench and ITBench, agentic frameworks for industry & IT domains 🙌🏻

Hugging Face (Twitter)

RT @lancedb: 1/3 Lance ❤️ @huggingface 🤗

We’re excited to announce native support for Lance on the Hugging Face Hub!

You can now share your large multimodal datasets with the world as a single, searchable artifact (including blobs, embeddings and indexes) all in one place.

Hugging Face (Twitter)

RT @fepegar_: We are excited to release the weights of @MSFTResearch's COLIPRI, our 3D vision–language encoder for chest CT scans, on @huggingface🤗

Model: aka.ms/colipri
Demo: aka.ms/colipri-demo
Paper: aka.ms/colipri-paper

Why does COLIPRI matter?

🧵0/12 👇

Hugging Face (Twitter)

RT @awnihannun: Over 10k MLX models have been uploaded to the @huggingface Hub.

Models for every modality from the latest LLMs, to speech recognition and synthesis, video generation, and more.

Thanks to the amazing community!

Hugging Face (Twitter)

RT @Gradio: All this through a new Python library called daggr!🔥

Build AI workflows that can connect ML models, custom Python functions, and AI apps together🤯

Hugging Face (Twitter)

RT @abidlabs: To see how useful (and fun) building Daggr flows can be, you have to try it yourself. Luckily the Python library is very simple to get started -- an intuitive API for users and plenty of logging for LLMs to use easily.

Install and star Daggr today: github.com/gradio-app/daggr

Hugging Face (Twitter)

RT @abidlabs: After working on Gradio for more than 6 years, I'm finally launching a new library to build AI workflows called: Daggr 🔥

We created it to solve the BIGGEST pain point we heard from users: how do you build reliable, complex AI apps from unreliable, simple models?

Hugging Face (Twitter)

Read the blog: huggingface.co/blog/daggr
Check features apps: https://huggingface.co/collections/ysharma/daggr-hf-spaces
daggr on GitHub: github.com/gradio-app/daggr 🤗

Hugging Face (Twitter)

Introducing daggr: a new way of building apps 🔥

daggr combines best of all worlds, mix-and-match model endpoints, Gradio apps, functions programmatically, inspect the pipeline visually 🙌🏻

Try it out, build and share to get featured!

Hugging Face (Twitter)

RT @cgeorgiaw: This breakthrough model from @GoogleDeepMind can evaluate DNA sequences of up to 1 million base pairs + predict at single base-pair resolution for:
🧬 gene expression
🧬 splicing patterns
🧬 chromatin features
🧬 contact maps

All on @huggingface! Future of science is open 🤗 https://twitter.com/GoogleDeepMind/status/2016542480955535475#m

Hugging Face (Twitter)

RT @Alibaba_Qwen: Qwen3-ASR and Qwen3-ForcedAligner are now open source — production-ready speech models designed for messy, real-world audio, with competitive performance and strong robustness.
● 52 languages & dialects with auto language ID (30 languages + 22 dialects/accents)
● Robust in noisy and complex settings (yes, singing and songs too)
● Long audio support: up to 20 minutes per pass
● Word/phrase-level timestamps:  high-precision alignment for 11 languages via Qwen3-ForcedAligner, stronger than MFA/CTC/CIF-style aligners
Also included: a full open-source inference & finetuning stack with vLLM batch, streaming, and async serving.
GitHub:  github.com/QwenLM/Qwen3-ASR
Hugging Face: https://huggingface.co/collections/Qwen/qwen3-asr
ModelScope: https://modelscope.cn/collections/Qwen/Qwen3-ASR
Hugging Face Demo:  https://huggingface.co/spaces/Qwen/Qwen3-...

Перейти на оригинальный пост

Hugging Face (Twitter)

RT @Xianbao_QIAN: It hasn't been noticed by many but @UnitreeRobotics
has open sourced the weights of UnifoLM-VLA model on @huggingface together with a few robotics datasets collected using G1 in @LeRobotHF format in Apache license

Let's rock!

Check them out: https://huggingface.co/unitreerobotics