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Post
2963
The Chinese community is shipping 🚢
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Can’t wait to see what’s next!
DeepSeek V3 (685 B MoE) has quietly released on the hub!
Base: deepseek-ai/DeepSeek-V3-Base
Instruct: deepseek-ai/DeepSeek-V3
Can’t wait to see what’s next!
- 1 reply
Post
3864
🎬 Revolutionize Your Video Creation
Dokdo Multimodal AI Transform a single image into a stunning video with perfect audio harmony! 🚀
Superior Technology 💫
Advanced Flow Matching: Smoother video transitions surpassing Kling and Sora
Intelligent Sound System: Automatically generates perfect audio by analyzing video mood
Multimodal Framework: Advanced AI integrating image, text, and audio analysis
Outstanding Performance 🎯
Ultra-High Resolution: 4K video quality with bfloat16 acceleration
Real-Time Optimization: 3x faster processing with PyTorch GPU acceleration
Smart Sound Matching: Real-time audio effects based on scene transitions and motion
Exceptional Features ✨
Custom Audio Creation: Natural soundtrack matching video tempo and rhythm
Intelligent Watermarking: Adaptive watermark adjusting to video characteristics
Multilingual Support: Precise translation engine powered by Helsinki-NLP
Versatile Applications 🌟
Social Media Marketing: Create engaging shorts for Instagram and YouTube
Product Promotion: Dynamic promotional videos highlighting product features
Educational Content: Interactive learning materials with enhanced engagement
Portfolio Enhancement: Professional-grade videos showcasing your work
Experience the video revolution with Dokdo Multimodal, where anyone can create professional-quality content from a single image. Elevate your content with perfectly synchronized video and audio that captivates your audience! 🎨
Start creating stunning videos that stand out from the crowd - whether you're a marketer, educator, content creator, or business owner. Join the future of AI-powered video creation today!
ginipick/Dokdo-multimodal
#VideoInnovation #AITechnology #PremiumContent #MarketingSolution
🔊 Please turn on your sound for the best viewing experience!
Dokdo Multimodal AI Transform a single image into a stunning video with perfect audio harmony! 🚀
Superior Technology 💫
Advanced Flow Matching: Smoother video transitions surpassing Kling and Sora
Intelligent Sound System: Automatically generates perfect audio by analyzing video mood
Multimodal Framework: Advanced AI integrating image, text, and audio analysis
Outstanding Performance 🎯
Ultra-High Resolution: 4K video quality with bfloat16 acceleration
Real-Time Optimization: 3x faster processing with PyTorch GPU acceleration
Smart Sound Matching: Real-time audio effects based on scene transitions and motion
Exceptional Features ✨
Custom Audio Creation: Natural soundtrack matching video tempo and rhythm
Intelligent Watermarking: Adaptive watermark adjusting to video characteristics
Multilingual Support: Precise translation engine powered by Helsinki-NLP
Versatile Applications 🌟
Social Media Marketing: Create engaging shorts for Instagram and YouTube
Product Promotion: Dynamic promotional videos highlighting product features
Educational Content: Interactive learning materials with enhanced engagement
Portfolio Enhancement: Professional-grade videos showcasing your work
Experience the video revolution with Dokdo Multimodal, where anyone can create professional-quality content from a single image. Elevate your content with perfectly synchronized video and audio that captivates your audience! 🎨
Start creating stunning videos that stand out from the crowd - whether you're a marketer, educator, content creator, or business owner. Join the future of AI-powered video creation today!
ginipick/Dokdo-multimodal
#VideoInnovation #AITechnology #PremiumContent #MarketingSolution
🔊 Please turn on your sound for the best viewing experience!
- 1 reply
Post
844
🐋 DeepSeek 🐋v3 achieves a solid 7 point jump than v2.5, surpassing GPT-4o, but is still behind 🍓 o1 🍓and Claude 3.5.
onekq-ai/WebApp1K-models-leaderboard
onekq-ai/WebApp1K-models-leaderboard
davanstrien
posted an update
1 day ago
Post
1786
🇸🇰 Hovorte po slovensky? Help build better AI for Slovak!
We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!
Your contribution will help create better language models for 5+ million Slovak speakers.
Annotate here: data-is-better-together/fineweb-c.
Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
We only need 90 more annotations to include Slovak in the next Hugging Face FineWeb2-C dataset ( data-is-better-together/fineweb-c) release!
Your contribution will help create better language models for 5+ million Slovak speakers.
Annotate here: data-is-better-together/fineweb-c.
Read more about why we're doing it: https://huggingface.co/blog/davanstrien/fineweb2-community
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- 3 replies
Post
1272
CS2 Highlights Video Dataset -
nyuuzyou/cs2-highlights
A collection of 4,857 high-quality Counter-Strike 2 gameplay highlights featuring:
- Professional and competitive gameplay recordings at 1080p resolution
- Complete metadata including Steam IDs and clip titles
- Preview thumbnails for all videos
- Both 60 FPS (842 clips) and 120 FPS (4,015 clips) content
- Gameplay from Faceit and official competitive modes
This extensive highlights collection provides a valuable resource for developing and evaluating video-based AI applications, especially in esports and competitive gaming contexts. Released under Creative Commons Zero (CC0) license.
A collection of 4,857 high-quality Counter-Strike 2 gameplay highlights featuring:
- Professional and competitive gameplay recordings at 1080p resolution
- Complete metadata including Steam IDs and clip titles
- Preview thumbnails for all videos
- Both 60 FPS (842 clips) and 120 FPS (4,015 clips) content
- Gameplay from Faceit and official competitive modes
This extensive highlights collection provides a valuable resource for developing and evaluating video-based AI applications, especially in esports and competitive gaming contexts. Released under Creative Commons Zero (CC0) license.
Post
1346
📢 Deligted to share the most recent milestone on quick deployment of Named Entity Recognition (NER) in Gen-AI powered systems.
Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
💎 Why is this important? In the era of GenAI the handling out textual output might be challenging. Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
📦: https://pypi.org/project/bulk-ner/0.25.0/
🌟: https://github.com/nicolay-r/bulk-ner
I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
In particular:
1. Processing CONLL format with B-I-O tags from model outputs
2. Input trimming: long input content might not be completely fitted
To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)
📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
👏 The code for pipeline deployment is taken from the AREkit project:
https://github.com/nicolay-r/AREkit
Releasing the bulk-ner 0.25.0 which represent a tiny framework that would save you time for deploing NER with any model.
💎 Why is this important? In the era of GenAI the handling out textual output might be challenging. Instead, recognizing named-entities via domain-oriented systems for your donwstream LLM would be preferable option.
📦: https://pypi.org/project/bulk-ner/0.25.0/
🌟: https://github.com/nicolay-r/bulk-ner
I noticed that the direct adaptaion of the LM for NER would result in spending signifcant amount of time on formatting your texts according to the NER-model needs.
In particular:
1. Processing CONLL format with B-I-O tags from model outputs
2. Input trimming: long input content might not be completely fitted
To cope with these problems, in version 0.25.0 I made a huge steps forward by providing:
✅ 🐍 Python API support: see screenshot below for a quick deployment (see screenshot below 📸)
✅ 🪶 No-string: dependencies are now clear, so it is purely Python implementation for API calls.
✅ 👌 Simplified output formatting: we use lists to represent texts with inner lists that refer to annotated objects (see screenshot below 📸)
📒 We have a colab for a quick start here (or screenshot for bash / Python API 📸)
https://colab.research.google.com/github/nicolay-r/ner-service/blob/main/NER_annotation_service.ipynb
👏 The code for pipeline deployment is taken from the AREkit project:
https://github.com/nicolay-r/AREkit
AkimfromParis
posted an update
2 days ago
Post
1446
🇺🇸 🇨🇦 🇬🇧 Nobel Prize winners against USSR & Japanese AI pioneers ☭🇯🇵
🇩🇪 Prof. Jürgen Schmidhuber: “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
1965 - First Deep Learning - USSR ☭ (Ukraine 🇺🇦 now)
Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
1969 - Rectified linear unit - Japan 🇯🇵
In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
1970 - Backpropagation - Finland 🇫🇮 😃
In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
1972 - Recurrent Neural Network - Japan 🇯🇵
In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
1979 - First Convolutional neural network - Japan 🇯🇵
CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
🇩🇪 Prof. Jürgen Schmidhuber: “The #NobelPrize in Physics 2024 for Hopfield & Hinton turns out to be a Nobel Prize for plagiarism. They republished methodologies developed in #Ukraine and #Japan by Ivakhnenko and Amari in the 1960s & 1970s, as well as other techniques, without citing the original inventors.”
1965 - First Deep Learning - USSR ☭ (Ukraine 🇺🇦 now)
Ivakhnenko and Lapa introduced the first deep learning in deep MLPs that learn internal representations of input data.
1967/68 - Deep Learning by Stochastic Gradient Descent - Japan 🇯🇵
Shun-Ichi Amari trained MLPs with many layers in non-incremental end-to-end fashion from scratch by stochastic gradient descent (SGD).
1969 - Rectified linear unit - Japan 🇯🇵
In 1969, Kunihiko Fukushima introduced ReLU in the context of visual feature extraction in hierarchical neural networks.
1970 - Backpropagation - Finland 🇫🇮 😃
In 1970, Seppo Linnainmaa was the first the reverse mode of automatic differentiation, now known as backpropagation.
1972 - Recurrent Neural Network - Japan 🇯🇵
In 1972, Shun-Ichi Amari published a learning recurrent neural network based on Lenz-Ising model (Amari's net was later called the "Hopfield network". Hopfield republished in 1982, without citing Amari papers.)
1979 - First Convolutional neural network - Japan 🇯🇵
CNN architecture was introduced in 1979 by Kunihiko Fukushima, also known as Neocognitron.
https://people.idsia.ch/~juergen/deep-learning-history.html#AMH2
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- 8 replies
Muhammadreza
posted an update
2 days ago
Post
1440
Does huggingface support URN/AIR system (like what we have in CivitAI). If yes, how can we access it?
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- 3 replies
