stabilityai/stable-audio-open-small
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ACE-Step/Ace-Step1.5
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as-cle-bert's
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๐๐๐ข๐๐๐ฐ๐ฌ๐๐จ๐ญ - ๐๐๐ฉ๐จ๐ซ๐ญ ๐๐๐ข๐ฅ๐ฒ ๐๐๐ข๐๐ง๐๐ ๐ง๐๐ฐ๐ฌ ๐จ๐ง ๐๐ฅ๐ฎ๐๐๐ค๐ฒ
GitHub ๐ https://github.com/AstraBert/SciNewsBot
BlueSky ๐ https://bsky.app/profile/sci-news-bot.bsky.social
Hi there HF Community!๐ค
I just created a very simple AI-powered bot that shares fact-checked news about Science, Environment, Energy and Technology on BlueSky :)
The bot takes news from Google News, filters out the sources that are not represented in the Media Bias Fact Check database, and then evaluates the reliability of the source based on the MBFC metrics. After that, it creates a catchy headline for the article and publishes the post on BlueSky๐ฐ
The cool thing? SciNewsBot is open-source and is cheap to maintain, as it is based on https://huggingface.co/mistralai/Mistral-Small-24B-Instruct-2501 (via Mistral API). You can reproduce it locally, spinning it up on your machine, and even launch it on cloud through a comfy Docker setup๐
Have fun and spread Science!โจ
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1409
๐๐๐ข๐๐๐ฐ๐ฌ๐๐จ๐ญ - ๐๐๐ฉ๐จ๐ซ๐ญ ๐๐๐ข๐ฅ๐ฒ ๐๐๐ข๐๐ง๐๐ ๐ง๐๐ฐ๐ฌ ๐จ๐ง ๐๐ฅ๐ฎ๐๐๐ค๐ฒ
GitHub ๐ https://github.com/AstraBert/SciNewsBot
BlueSky ๐ https://bsky.app/profile/sci-news-bot.bsky.social
Hi there HF Community!๐ค
I just created a very simple AI-powered bot that shares fact-checked news about Science, Environment, Energy and Technology on BlueSky :)
The bot takes news from Google News, filters out the sources that are not represented in the Media Bias Fact Check database, and then evaluates the reliability of the source based on the MBFC metrics. After that, it creates a catchy headline for the article and publishes the post on BlueSky๐ฐ
The cool thing? SciNewsBot is open-source and is cheap to maintain, as it is based on mistralai/Mistral-Small-24B-Instruct-2501 (via Mistral API). You can reproduce it locally, spinning it up on your machine, and even launch it on cloud through a comfy Docker setup๐
Have fun and spread Science!โจ
GitHub ๐ https://github.com/AstraBert/SciNewsBot
BlueSky ๐ https://bsky.app/profile/sci-news-bot.bsky.social
Hi there HF Community!๐ค
I just created a very simple AI-powered bot that shares fact-checked news about Science, Environment, Energy and Technology on BlueSky :)
The bot takes news from Google News, filters out the sources that are not represented in the Media Bias Fact Check database, and then evaluates the reliability of the source based on the MBFC metrics. After that, it creates a catchy headline for the article and publishes the post on BlueSky๐ฐ
The cool thing? SciNewsBot is open-source and is cheap to maintain, as it is based on mistralai/Mistral-Small-24B-Instruct-2501 (via Mistral API). You can reproduce it locally, spinning it up on your machine, and even launch it on cloud through a comfy Docker setup๐
Have fun and spread Science!โจ
liked
2
models
over 1 year ago
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Space
over 1 year ago
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fdaudens's
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over 1 year ago
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3080
The Nobel Prize background for Hopfield and Hinton's work on neural networks is pure gold. It's a masterclass in explaining AI basics.
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics ๐ค AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
Key takeaways from the conclusion:
- ML applications are expanding rapidly. We're still figuring out which will stick.
- Ethical discussions are crucial as the tech develops.
- Physics ๐ค AI: A two-way street of innovation.
Some mind-blowing AI applications in physics:
- Discovering the Higgs particle
- Cleaning up gravitational wave data
- Hunting exoplanets
- Predicting molecular structures
- Designing better solar cells
We're just scratching the surface. The interplay between AI and physics is reshaping both fields.
Bonus: The illustrations accompanying the background document are really neat. (Credit: Johan Jarnestad/The Royal Swedish Academy of Sciences)
#AI #MachineLearning #Physics #Ethics #Innovation
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merve's
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over 1 year ago
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3824
Meta AI vision has been cooking
@facebook
They shipped multiple models and demos for their papers at @ECCV ๐ค
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos ๐
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
They shipped multiple models and demos for their papers at @ECCV ๐ค
Here's a compilation of my top picks:
- Sapiens is family of foundation models for human-centric depth estimation, segmentation and more, all models have open weights and demos ๐
All models have their demos and even torchscript checkpoints!
A collection of models and demos: facebook/sapiens-66d22047daa6402d565cb2fc
- VFusion3D is state-of-the-art consistent 3D generation model from images
Model: facebook/vfusion3d
Demo: facebook/VFusion3D
- CoTracker is the state-of-the-art point (pixel) tracking model
Demo: facebook/cotracker
Model: facebook/cotracker
liked
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Spaces
over 1 year ago
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m-ric's
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over 1 year ago
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1085
๐ง Stanford paper might be the key to OpenAI o1โs performance: Whatโs so effective about Chain of Thought? โ it unlocks radically different sequential tasks!
๐ญย Reminder: A Chain of Thought (CoT) means that you instruct the model to โthink step by stepโ. Often itโs literally just putting in the prompt โletโs think step by step.โ
๐คย This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
โ๏ธ For instance if you ask them the result of 3^2^2^2^โฆ, with 20 iterations, theyโll nearly always fail.
๐กย Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- ๐งฑ Each step in the CoT corresponds to simulating one operation in a complex circuit.
- ๐ This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- ๐ Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- ๐ Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- ๐ย The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper ๐ย Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)
๐ญย Reminder: A Chain of Thought (CoT) means that you instruct the model to โthink step by stepโ. Often itโs literally just putting in the prompt โletโs think step by step.โ
๐คย This method has been shown to be unreasonably effective to increase perf on benchmarks. However why it works so well remains unclear.
Here's the scoop: Transformers are amazing at parallel processing, but they've always struggled with tasks that require sequential reasoning.
โ๏ธ For instance if you ask them the result of 3^2^2^2^โฆ, with 20 iterations, theyโll nearly always fail.
๐กย Indeed, researchers prove mathematically, by assimilating transformers networks to logical circuits, that effectively they cannot solve sequential tasks that require more than a certain threshold of sequences.
But CoT enables sequential reasoning:
- ๐งฑ Each step in the CoT corresponds to simulating one operation in a complex circuit.
- ๐ This allows the transformer to "reset" the depth of intermediate outputs, overcoming previous limitations.
- ๐ Thus, with CoT, constant-depth transformers can now solve ANY problem computable by polynomial-size circuits! (That's a huge class of problems in computer science.)
- ๐ Transformers can now handle tricky tasks like iterated squares (computing 3^2^2^2^2) composed permutations and evaluating circuits - stuff that requires serial computation.
- ๐ย The improvement is especially dramatic for transformers with a limited depth. Empirical tests on four arithmetic problems showed massive accuracy gains with CoT on inherently serial tasks.
Main takeaway: Chain-of-thought isn't just a neat trick - it fundamentally expands what transformer models can do!
Read the paper ๐ย Chain of Thought Empowers Transformers to Solve Inherently Serial Problems (2402.12875)
updated
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collection
over 1 year ago
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DmitryRyumin's
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over 1 year ago
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3075
๐ฅ๐ญ๐ New Research Alert - HeadGAP (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors ๐
๐ Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
๐ฅ Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
๐ Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
๐ Github Page: https://headgap.github.io
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
๐ Title: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors ๐
๐ Description: HeadGAP introduces a novel method for generating high-fidelity, animatable 3D head avatars from few-shot data, using Gaussian priors and dynamic part-based modelling for personalized and generalizable results.
๐ฅ Authors: @zxz267 , @walsvid , @zhaohu2 , Weiyi Zhang, @hellozhuo , Xu Chang, Yang Zhao, Zheng Lv, Xiaoyuan Zhang, @yongjie-zhang-mail , Guidong Wang, and Lan Xu
๐ Paper: HeadGAP: Few-shot 3D Head Avatar via Generalizable Gaussian Priors (2408.06019)
๐ Github Page: https://headgap.github.io
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #HeadGAP #3DAvatar #FewShotLearning #GaussianPriors #AvatarCreation #3DModeling #MachineLearning #ComputerVision #ComputerGraphics #GenerativeAI #DeepLearning #AI
reacted to
DmitryRyumin's
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over 1 year ago
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1869
๐ฅ๐ญ๐ New Research Alert - ECCV 2024 (Avatars Collection)! ๐๐ญ๐ฅ
๐ Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos ๐
๐ Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
๐ฅ Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Github Page: https://shad0wta9.github.io/meshavatar-page
๐ Repository: https://github.com/shad0wta9/meshavatar
๐บ Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
๐ Title: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos ๐
๐ Description: MeshAvatar is a novel pipeline that generates high-quality triangular human avatars from multi-view videos, enabling realistic editing and rendering through a mesh-based approach with physics-based decomposition.
๐ฅ Authors: Yushuo Chen, Zerong Zheng, Zhe Li, Chao Xu, and Yebin Liu
๐ Conference: ECCV, 29 Sep โ 4 Oct, 2024 | Milano, Italy ๐ฎ๐น
๐ Paper: MeshAvatar: Learning High-quality Triangular Human Avatars from Multi-view Videos (2407.08414)
๐ Github Page: https://shad0wta9.github.io/meshavatar-page
๐ Repository: https://github.com/shad0wta9/meshavatar
๐บ Video: https://www.youtube.com/watch?v=Kpbpujkh2iI
๐ CVPR-2023-24-Papers: https://github.com/DmitryRyumin/CVPR-2023-24-Papers
๐ WACV-2024-Papers: https://github.com/DmitryRyumin/WACV-2024-Papers
๐ ICCV-2023-Papers: https://github.com/DmitryRyumin/ICCV-2023-Papers
๐ More Papers: more cutting-edge research presented at other conferences in the DmitryRyumin/NewEraAI-Papers curated by @DmitryRyumin
๐ Added to the Avatars Collection: DmitryRyumin/avatars-65df37cdf81fec13d4dbac36
๐ Keywords: #MeshAvatar #3DAvatars #MultiViewVideo #PhysicsBasedRendering #TriangularMesh #AvatarCreation #3DModeling #NeuralRendering #Relighting #AvatarEditing #MachineLearning #ComputerVision #ComputerGraphics #DeepLearning #AI #ECCV2024
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asoria's
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2628
๐ I wrote a tutorial on how to get started with the fine-tuning process using Hugging Face tools, providing an end-to-end workflow.
The tutorial covers creating a new dataset using the new SQL Console ๐ข and fine-tuning a model with SFT, guided by the Notebook Creator App ๐.
๐ You can read the full article here:
https://huggingface.co/blog/asoria/easy-fine-tuning-with-hf
asoria/auto-notebook-creator
The tutorial covers creating a new dataset using the new SQL Console ๐ข and fine-tuning a model with SFT, guided by the Notebook Creator App ๐.
๐ You can read the full article here:
https://huggingface.co/blog/asoria/easy-fine-tuning-with-hf
asoria/auto-notebook-creator
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over 1 year ago
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fdaudens's
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over 1 year ago
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A big day for multimodal models!
Llama 3.2 is out with a major update: it can now process images.
Key highlights:
โข 11B and 90B vision models
โข Small 1B and 3B text models for mobile devices
Eval results already on the leaderboard: open-llm-leaderboard/open_llm_leaderboard
Collection: meta-llama/llama-32-66f448ffc8c32f949b04c8cf
Llama 3.2 is out with a major update: it can now process images.
Key highlights:
โข 11B and 90B vision models
โข Small 1B and 3B text models for mobile devices
Eval results already on the leaderboard: open-llm-leaderboard/open_llm_leaderboard
Collection: meta-llama/llama-32-66f448ffc8c32f949b04c8cf
- 1 reply
liked
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models
over 1 year ago
