FineVideo: behind the scenes
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Thomas Wolf PRO
thomwolf
AI & ML interests
NLP and open-source :-)
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liked
a dataset
3 days ago
opensourceorg/osaid
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thomwolf's activity
Amazing! the perfect complementary to https://huggingface.co/datasets/PleIAs/common_corpus cc @Pclanglais
reacted to
nyuuzyou's
post with π₯
3 days ago
Post
896
πΌοΈ Introducing Public Domain Pictures Dataset -
nyuuzyou/publicdomainpictures
Dataset highlights:
- 644,412 public domain images with comprehensive metadata from publicdomainpictures.net
- English language metadata including titles, descriptions, and keywords
- Each entry contains rich metadata including:
- Unique image ID and full-size image URLs
- Detailed titles and descriptions
- Keyword/tag collections
- Creator attribution
- Released to the public domain under Creative Commons Zero (CC0) license
Dataset highlights:
- 644,412 public domain images with comprehensive metadata from publicdomainpictures.net
- English language metadata including titles, descriptions, and keywords
- Each entry contains rich metadata including:
- Unique image ID and full-size image URLs
- Detailed titles and descriptions
- Keyword/tag collections
- Creator attribution
- Released to the public domain under Creative Commons Zero (CC0) license
-
- 2 replies
posted
an
update
3 days ago
Post
1202
Very exciting new
mistralai/Pixtral-Large-Instruct-2411 model from Mistral-AI
Impressive performances, huge congrats @patrickvonplaten @sgvaze @pandora-s @devendrachaplot @sophiamyang and team!
Very nice to have SOTA Multilingual OCR and Chart understanding in an open-weights model
Impressive performances, huge congrats @patrickvonplaten @sgvaze @pandora-s @devendrachaplot @sophiamyang and team!
Very nice to have SOTA Multilingual OCR and Chart understanding in an open-weights model
exciting!
reacted to
LukeNeumann's
post with ππ₯
3 days ago
Post
1831
Hello Hugging Face community!
I wanted to introduce myself and my company @Overlaiapp . We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.
We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:
- Overlaiai/OregonCoastin4K
- Overlaiai/SubArcticPolarBear
Overlai.ai Dataset Features
π₯ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.
πΈ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.
π Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.
βοΈ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
π Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.
π Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.
I wanted to introduce myself and my company @Overlaiapp . We are a collective of filmmakers, photographers, and AI engineers working on high resolution (8K+) training data.
We plan to share a lot of our datasets with the community and are kicking things off with two curated datasets:
- Overlaiai/OregonCoastin4K
- Overlaiai/SubArcticPolarBear
Overlai.ai Dataset Features
π₯ Oversampled: Every clip is captured in stunning 8K resolution, delivering rich detail ideal for fine tuning scenic landscapes and ocean dynamics.
πΈ Variance: Includes close-up details, slow-motion footage of crashing waves, sweeping landscapes, and wildlife shots.
π Detailed Metadata: Every clip is paired with structured metadata, including creative descriptions, precise camera movements, lens information, field of view calculations, and shot settings, ensuring AI models can fully understand and replicate real-world cinematography with accuracy.
βοΈ Consistency: Re-thinking training data at the point of capture by "overshooting" a subject, enabling models to learn more nuanced relationships and views across scenes.
π Light: Shot during early morning and sunset light for optimal color contrast and dynamic range, maximizing visual quality for color and lighting-sensitive tasks.
π Curation: Curated specifically for machine learning, providing clean, high-quality data for next generation model training.
reacted to
merve's
post with β€οΈ
28 days ago
Post
2440
Lotus πͺ· is a new foundation model on monocular depth estimation β¨
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction π€
Find everything in this collection merve/lotus-6718fb957dc1c85a47ca1210
Compared to previous diffusion-based MDE models, Lotus is modified for dense prediction tasks
Authors also released a model for normal prediction π€
Find everything in this collection merve/lotus-6718fb957dc1c85a47ca1210
reacted to
singhsidhukuldeep's
post with β€οΈ
28 days ago
Post
2731
If you have ~300+ GB of V-RAM, you can run Mochi from
@genmo
A SOTA model that dramatically closes the gap between closed and open video generation models.
Mochi 1 introduces revolutionary architecture featuring joint reasoning over 44,520 video tokens with full 3D attention. The model implements extended learnable rotary positional embeddings (RoPE) in three dimensions, with network-learned mixing frequencies for space and time axes.
The model incorporates cutting-edge improvements, including:
- SwiGLU feedforward layers
- Query-key normalization for enhanced stability
- Sandwich normalization for controlled internal activations
What is currently available?
The base model delivers impressive 480p video generation with exceptional motion quality and prompt adherence. Released under the Apache 2.0 license, it's freely available for both personal and commercial applications.
What's Coming?
Genmo has announced Mochi 1 HD, scheduled for release later this year, which will feature:
- Enhanced 720p resolution
- Improved motion fidelity
- Better handling of complex scene warping
A SOTA model that dramatically closes the gap between closed and open video generation models.
Mochi 1 introduces revolutionary architecture featuring joint reasoning over 44,520 video tokens with full 3D attention. The model implements extended learnable rotary positional embeddings (RoPE) in three dimensions, with network-learned mixing frequencies for space and time axes.
The model incorporates cutting-edge improvements, including:
- SwiGLU feedforward layers
- Query-key normalization for enhanced stability
- Sandwich normalization for controlled internal activations
What is currently available?
The base model delivers impressive 480p video generation with exceptional motion quality and prompt adherence. Released under the Apache 2.0 license, it's freely available for both personal and commercial applications.
What's Coming?
Genmo has announced Mochi 1 HD, scheduled for release later this year, which will feature:
- Enhanced 720p resolution
- Improved motion fidelity
- Better handling of complex scene warping
- 2 replies
reacted to
fdaudens's
post with β€οΈ
28 days ago
Post
2782
π€― Plot twist: Size isn't everything in AI! A lean 32B parameter model just showed up to the party and outperformed a 70B one. Efficiency > Scale? The AI world just got more interesting...
Cohere For AI released Aya Expanse, a new family of multilingual models (8B and 32B) spanning 23 popular languages.
Models: CohereForAI/c4ai-aya-expanse-671a83d6b2c07c692beab3c3
Blog post: https://huggingface.co/blog/aya-expanse
Demo: CohereForAI/aya_expanse
Cohere For AI released Aya Expanse, a new family of multilingual models (8B and 32B) spanning 23 popular languages.
Models: CohereForAI/c4ai-aya-expanse-671a83d6b2c07c692beab3c3
Blog post: https://huggingface.co/blog/aya-expanse
Demo: CohereForAI/aya_expanse
posted
an
update
28 days ago
Post
4045
Parents in the 1990: Teach the kids to code
Parents now: Teach the kids to fix the code when it starts walking around π€β¨
Parents now: Teach the kids to fix the code when it starts walking around π€β¨
- 2 replies
reacted to
singhsidhukuldeep's
post with π₯
2 months ago
Post
1219
Remember when
@Google
launched MediaPipe in an effort to create efficient on-device pipelines?
They've just unlocked the ability to run 7B+ parameter language models directly in your browser. This is a game-changer for on-device AI!
Yes, they are streaming 8.6 GB model files!
Currently, they have Gemma 2B/7B running, but imagine Dynamic LoRA, multimodal support, quantization, and you never leaving Chrome!
This is a significant technical advancement, especially in Memory Optimization:
- Redesigned the model-loading code to work around WebAssembly's 4 GB memory limit.
- Implemented asynchronous loading of transformer stack layers (28 for Gemma 1.1 7B).
- Reduced peak WebAssembly memory usage to less than 1% of previous requirements.
Cross-Platform Compatibility
- Compiled the C++ codebase to WebAssembly for broad browser support.
- Utilized the WebGPU API for native GPU acceleration in browsers.
Here's why this matters:
1. Privacy: No need to send data to remote servers.
2. Cost-Efficiency: Eliminates server expenses.
3. Offline Capabilities: Use powerful AI without an internet connection.
Blog: https://research.google/blog/unlocking-7b-language-models-in-your-browser-a-deep-dive-with-google-ai-edges-mediapipe/
They've just unlocked the ability to run 7B+ parameter language models directly in your browser. This is a game-changer for on-device AI!
Yes, they are streaming 8.6 GB model files!
Currently, they have Gemma 2B/7B running, but imagine Dynamic LoRA, multimodal support, quantization, and you never leaving Chrome!
This is a significant technical advancement, especially in Memory Optimization:
- Redesigned the model-loading code to work around WebAssembly's 4 GB memory limit.
- Implemented asynchronous loading of transformer stack layers (28 for Gemma 1.1 7B).
- Reduced peak WebAssembly memory usage to less than 1% of previous requirements.
Cross-Platform Compatibility
- Compiled the C++ codebase to WebAssembly for broad browser support.
- Utilized the WebGPU API for native GPU acceleration in browsers.
Here's why this matters:
1. Privacy: No need to send data to remote servers.
2. Cost-Efficiency: Eliminates server expenses.
3. Offline Capabilities: Use powerful AI without an internet connection.
Blog: https://research.google/blog/unlocking-7b-language-models-in-your-browser-a-deep-dive-with-google-ai-edges-mediapipe/
reacted to
alex-abb's
post with ππ₯
5 months ago
Post
4768
Hi everyone!
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models. With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.
alex-abb/LLM_Feeling_Analyzer
I'm Alex, I'm 16, I've been an internship at Hugging Face for a little over a week and I've already learned a lot about using and prompting LLM models. With @victor as tutor I've just finished a space that analyzes your feelings by prompting an LLM chat model. The aim is to extend it so that it can categorize hugging face posts.
alex-abb/LLM_Feeling_Analyzer
- 4 replies
reacted to
fdaudens's
post with β€οΈ
5 months ago
Post
3414
A nice improvement for Hugging Face on Sheets: You can now customize your prompt and select the model of your choice directly on the sheet.
Thanks to @louisbrulenaudet for the contribution. Really cool to see the community improving this tool!
Try it here: JournalistsonHF/huggingface-on-sheets
Thanks to @louisbrulenaudet for the contribution. Really cool to see the community improving this tool!
Try it here: JournalistsonHF/huggingface-on-sheets
reacted to
yunusserhat's
post with π
5 months ago
Post
3129
Hello everyone,
I am pleased to announce that I have founded the University of Glasgow organization on Huggingface. If you are affiliated with the University of Glasgow or have a relative who is, you can log in through the relevant link.
https://huggingface.co/UniversityofGlasgow
I am pleased to announce that I have founded the University of Glasgow organization on Huggingface. If you are affiliated with the University of Glasgow or have a relative who is, you can log in through the relevant link.
https://huggingface.co/UniversityofGlasgow
-
- 1 reply
reacted to
KnutJaegersberg's
post with π
6 months ago
Post
1555
What We Learned from a Year of Building with LLMs
It's a nice perspective outlined in here.
βWhen a measure becomes a target, it ceases to be a good measure.β
β Goodhartβs Law
https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/
It's a nice perspective outlined in here.
βWhen a measure becomes a target, it ceases to be a good measure.β
β Goodhartβs Law
https://www.oreilly.com/radar/what-we-learned-from-a-year-of-building-with-llms-part-i/
reacted to
frimelle's
post with β€οΈπ€
6 months ago
Post
1842
Wikimedia and Hugging Face seem kind of naturally complementary: Both are community-centred, value openness and consent. That's why I'd love to see more Wikipedia and other Wikimedia projects' datasets on Hugging Face to advance machine learning with diverse, community-curated data! See my new article on the Hugging Face hub for why and how to create more Wikimedia datasets on Hugging Face: https://huggingface.co/blog/frimelle/wikipedias-treasure-trove-ml-data
reacted to
Salama1429's
post with π
6 months ago
Post
2432
πΊ Introducing the YouTube-Commons Dataset πΊ
π Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.
π License: All videos are shared under the CC-BY license, with the majority (71%) in English.
π€ Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.
π Utilization: The text can be used for model training and is republishable for reproducibility purposes.
π€ Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.
π Explore the dataset here: https://lnkd.in/d_paWKFE
#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation
π Overview: The YouTube Commons Dataset is a comprehensive collection of 30 billion words from 15,112,121 original and automatically translated transcripts, drawn from 2,063,066 videos on YouTube.
π License: All videos are shared under the CC-BY license, with the majority (71%) in English.
π€ Applications: This dataset is ideal for training powerful AI models for converting speech to text (ASR) and translation models.
π Utilization: The text can be used for model training and is republishable for reproducibility purposes.
π€ Collaboration: This dataset is the result of a collaboration between state start-up LANGU:IA, the French Ministry of Culture, and DINUM. It will be expanded in the coming months.
π Explore the dataset here: https://lnkd.in/d_paWKFE
#YouTubeCommons #AIResearch #MachineLearning #OpenData #ArtificialIntelligence #NLP #Dataset #TechCollaboration #Innovation #DigitalTransformation