AdinaY (Adina Yakefu)

posted an update about 3 hours ago

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Wan2.1 🔥📹 new OPEN video model by Alibaba Wan team!

Model: Wan-AI/Wan2.1-T2V-14B
Demo: Wan-AI/Wan2.1

✨Apache 2.0
✨8.19GB VRAM, runs on most GPUs
✨Multi-Tasking: T2V, I2V, Video Editing, T2I, V2A
✨Text Generation: Supports Chinese & English
✨Powerful Video VAE: Encode/decode 1080P w/ temporal precision

posted an update about 21 hours ago

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Try QwQ-Max-Preview, Qwen's reasoning model here👉 https://chat.qwen.ai
Can't wait for the model weights to drop on the Hugging Face Hub 🔥

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reacted to fdaudens's post with ❤️ about 21 hours ago

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🚀 Just launched: A toolkit of 20 powerful AI tools that journalists can use right now - transcribe, analyze, create. 100% free & open-source.

Been testing all these tools myself and created a searchable collection of the most practical ones - from audio transcription to image generation to document analysis. No coding needed, no expensive subscriptions.

Some highlights I've tested personally:
- Private, on-device transcription with speaker ID in 100+ languages using Whisper
- Website scraping that just works - paste a URL, get structured data
- Local image editing with tools like Finegrain (impressive results)
- Document chat using Qwen 2.5 72B (handles technical papers well)

Sharing this early because the best tools come from the community. Drop your favorite tools in the comments or join the discussion on what to add next!

👉 JournalistsonHF/ai-toolkit

reacted to their post with 🔥 1 day ago

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Two AI startups, DeepSeek & Moonshot AI , keep moving in perfect sync 👇

✨ Last December: DeepSeek & Moonshot AI released their reasoning models on the SAME DAY.
DeepSeek: deepseek-ai/DeepSeek-R1
MoonShot: https://github.com/MoonshotAI/Kimi-k1.5

✨ Last week: Both teams published papers on modifying attention mechanisms on the SAME DAY AGAIN.
DeepSeek: Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2502.11089)
Moonshot: MoBA: Mixture of Block Attention for Long-Context LLMs (2502.13189)

✨ TODAY:
DeepSeek unveiled Flash MLA: a efficient MLA decoding kernel for NVIDIA Hopper GPUs, optimized for variable-length sequences.
https://github.com/deepseek-ai/FlashMLA

Moonshot AI introduces Moonlight: a 3B/16B MoE trained on 5.7T tokens using Muon, pushing the Pareto frontier with fewer FLOPs.
moonshotai/Moonlight-16B-A3B

What's next? 👀

posted an update 1 day ago

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1798

Two AI startups, DeepSeek & Moonshot AI , keep moving in perfect sync 👇

✨ Last December: DeepSeek & Moonshot AI released their reasoning models on the SAME DAY.
DeepSeek: deepseek-ai/DeepSeek-R1
MoonShot: https://github.com/MoonshotAI/Kimi-k1.5

✨ Last week: Both teams published papers on modifying attention mechanisms on the SAME DAY AGAIN.
DeepSeek: Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2502.11089)
Moonshot: MoBA: Mixture of Block Attention for Long-Context LLMs (2502.13189)

✨ TODAY:
DeepSeek unveiled Flash MLA: a efficient MLA decoding kernel for NVIDIA Hopper GPUs, optimized for variable-length sequences.
https://github.com/deepseek-ai/FlashMLA

Moonshot AI introduces Moonlight: a 3B/16B MoE trained on 5.7T tokens using Muon, pushing the Pareto frontier with fewer FLOPs.
moonshotai/Moonlight-16B-A3B

What's next? 👀

posted an update 5 days ago

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VLM-R1🔥bringing DeepSeek’s R1 method to vision language models!

GitHub: https://github.com/om-ai-lab/VLM-R1
Demo: omlab/VLM-R1-Referral-Expression

reacted to clem's post with ❤️🔥 6 days ago

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We crossed 1B+ tokens routed to inference providers partners on HF, that we released just a few days ago.

Just getting started of course but early users seem to like it & always happy to be able to partner with cool startups in the ecosystem.

Have you been using any integration and how can we make it better?

https://huggingface.co/blog/inference-providers

reacted to ginipick's post with 🚀🔥 6 days ago

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🚀 FLUX Workflow Canvas

Welcome to Workflow Canvas, your ultimate AI-driven platform for crafting stunning design concepts and intricate workflow diagrams that empower your business! 🤖✨

ginigen/Workflow-Canvas

Features
Product Design 🛠️
Transform your ideas into reality with sleek, industrial product designs that blend modern aesthetics with advanced technology.

Mindmap 🧠
Generate vibrant, educational mind maps that outline your strategies and processes in a clear, visually engaging layout.

Mockup 📱
Quickly prototype intuitive app interfaces and web designs using clean, hand-drawn wireframes that capture your vision.

Infographic 📊
Build polished, data-rich infographics that communicate complex corporate metrics and trends with style and clarity.

Diagram 📈
Illustrate comprehensive, end-to-end business workflows—from market analysis to implementation—with detailed and organized diagrams.

Flowchart 🔄
Design easy-to-follow, hand-drawn style flowcharts that map out your operational processes using vibrant colors and minimalistic icons.

How It Works
Set Your Parameters:
Customize your creative process by adjusting the seed, dimensions, inference steps, and guidance scale through the intuitive sidebar.

Choose Your Visual Style:
Explore our diverse range of tabs—from Product Design and Mindmap to Flowchart—each tailored to a unique creative output.

Get Inspired:
Dive into our rich library of example prompts featuring detailed lists and tree structures to instantly populate your design ideas.

Generate Your Masterpiece:
Click the “Generate” button and watch as your ideas come to life in beautifully rendered images! 🎨

Experience the fusion of art and technology with Workflow Canvas – where your business ideas transform into dynamic, visual masterpieces. Get started today and revolutionize the way you design! 🚀

1 reply

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reacted to m-ric's post with 👍🚀 6 days ago

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Less is More for Reasoning (LIMO): a 32B model fine-tuned with 817 examples can beat o1-preview on math reasoning! 🤯

Do we really need o1's huge RL procedure to see reasoning emerge? It seems not.
Researchers from Shanghai Jiaotong University just demonstrated that carefully selected examples can boost math performance in large language models using SFT —no huge datasets or RL procedures needed.

Their procedure allows Qwen2.5-32B-Instruct to jump from 6.5% to 57% on AIME and from 59% to 95% on MATH, while using only 1% of the data in previous approaches.

⚡ The Less-is-More Reasoning Hypothesis:
‣ Minimal but precise examples that showcase optimal reasoning patterns matter more than sheer quantity
‣ Pre-training knowledge plus sufficient computational resources at inference levels up math skills

➡️ Core techniques:
‣ High-quality reasoning chains with self-verification steps
‣ 817 handpicked problems that encourage deeper reasoning
‣ Enough inference-time computation to allow extended reasoning

💪 Efficiency gains:
‣ Only 817 examples instead of 100k+
‣ 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data

This really challenges the notion that SFT leads to memorization rather than generalization! And opens up reasoning to GPU-poor researchers 🚀

Read the full paper here 👉 LIMO: Less is More for Reasoning (2502.03387)

posted an update 7 days ago

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🚀 StepFun阶跃星辰 is making BIG open moves!

Last year, their GOT-OCR 2.0 took the community by storm 🔥but many didn’t know they were also building some amazing models. Now, they’ve just dropped something huge on the hub!

📺 Step-Video-T2V: a 30B bilingual open video model that generates 204 frames (8-10s) at 540P resolution with high information density & consistency.
stepfun-ai/stepvideo-t2v

🔊 Step-Audio-TTS-3B : a TTS trained with the LLM-Chat paradigm on a large synthetic dataset, capable of generating RAP & Humming
stepfun-ai/step-audio-67b33accf45735bb21131b0b

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posted an update 7 days ago

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The latest paper of DeepSeek is now available on the Daily Papers page 🚀
You can reach out to the authors directly on this page👇
Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention (2502.11089)

1 reply

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reacted to clem's post with 🔥 7 days ago

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What are the best organizations to follow on @huggingface ?

On top of my head:
- Deepseek (35,000 followers): https://huggingface.co/deepseek-ai
- Meta Llama (27,000 followers): https://huggingface.co/meta-llama
- Black Forrest Labs (11,000 followers): https://huggingface.co/black-forest-labs
- OpenAI (5,000 followers): https://huggingface.co/openai
- Nvidia (16,000 followers): https://huggingface.co/nvidia
- MIcrosoft (9,000 followers): https://huggingface.co/microsoft
- AllenAI (2,000 followers): https://huggingface.co/allenai
- Mistral (5,000 followers): https://huggingface.co/mistralai
- XAI (600 followers): https://huggingface.co/xai-org
- Stability AI (16,000 followers): https://huggingface.co/stabilityai
- Qwen (16,000 followers): https://huggingface.co/Qwen
- GoogleAI (8,000 followers): https://huggingface.co/google
- Unsloth (3,000 followers): https://huggingface.co/unsloth
- Bria AI (4,000 followers): https://huggingface.co/briaai
- NousResearch (1,300 followers): https://huggingface.co/NousResearch

Bonus, the agent course org with 17,000 followers: https://huggingface.co/agents-course

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posted an update 12 days ago

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Ovis2 🔥 a multimodal LLM released by Alibaba AIDC team.
AIDC-AI/ovis2-67ab36c7e497429034874464
✨1B/2B/4B/8B/16B/34B
✨Strong CoT for deeper problem solving
✨Multilingual OCR – Expanded beyond English & Chinese, with better data extraction

posted an update 12 days ago

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InspireMusic 🎵🔥 an open music generation framework by Alibaba FunAudio Lab
Model: FunAudioLLM/InspireMusic-1.5B-Long
Demo: FunAudioLLM/InspireMusic
✨ Music, songs, audio - ALL IN ONE
✨ High quality audio: 24kHz & 48kHz sampling rates
✨ Long-Form Generation: enables extended audio creation
✨ Efficient Fine-Tuning: precision (BF16, FP16, FP32) with user-friendly scripts

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reacted to lin-tan's post with 🔥 20 days ago

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🚀 Excited to share that our paper, "SELP: Generating Safe and Efficient Task Plans for Robot Agents with Large Language Models", has been accepted to #ICRA2025! 🔗 Preprint: https://arxiv.org/pdf/2409.19471

We introduce SELP (Safe Efficient LLM Planner), a novel approach for generating plans that adhere to user-specified constraints while optimizing for time-efficient execution. By leveraging linear temporal logic (LTL) to interpret natural language commands, SELP effectively handles complex commands and long-horizon tasks. 🤖

💡SELP presents three key insights:
1️⃣ Equivalence Voting: Ensures robust translations from natural language instructions into LTL specifications.
2️⃣ Constrained Decoding: Uses the generated LTL formula to guide the autoregressive inference of plans, ensuring the generated plans conform to the LTL.
3️⃣ Domain-Specific Fine-Tuning: Customizes LLMs for specific robotic tasks, boosting both safety and efficiency.

📊 Experiment: Our experiments demonstrate SELP’s effectiveness and generalizability across diverse tasks. In drone navigation, SELP outperforms state-of-the-art LLM planners by 10.8% in safety rate and by 19.8% in plan efficiency. For robot manipulation, SELP achieves a 20.4% improvement in safety rate.

@yiwu @jiang719

#ICRA2025 #LLM #Robotics #Agent #LLMPlanner

reacted to their post with 🔥 20 days ago

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Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
✨ 7B/72B
✨ Apache 2.0
✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto

posted an update 20 days ago

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Xwen 🔥 a series of open models based on Qwen2.5 models, developed by a brilliant research team of PhD students from the Chinese community.
shenzhi-wang/xwen-chat-679e30ab1f4b90cfa7dbc49e
✨ 7B/72B
✨ Apache 2.0
✨ Xwen-72B-Chat outperformed DeepSeek V3 on Arena Hard Auto

Adina Yakefu

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