huggingface-legal/takedown-notices
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nyuuzyou PRO
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liked
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about 10 hours ago
huggingface-legal/takedown-notices
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luigi12345's
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🚀 Meta’s Llama 4 Models Now on Hugging Face!
Meta has released Llama 4 Scout and Llama 4 Maverick, now available on Hugging Face:
• Llama 4 Scout: 17B active parameters, 16-expert Mixture of Experts (MoE) architecture, 10M token context window, fits on a single H100 GPU. 
• Llama 4 Maverick: 17B active parameters, 128-expert MoE architecture, 1M token context window, optimized for DGX H100 systems. 
🔥 Key Features:
• Native Multimodality: Seamlessly processes text and images. 
• Extended Context Window: Up to 10 million tokens for handling extensive inputs.
• Multilingual Support: Trained on 200 languages, with fine-tuning support for 12, including Arabic, Spanish, and German. 
🛠️ Access and Integration:
• Model Checkpoints: Available under the meta-llama organization on the Hugging Face Hub.
• Transformers Compatibility: Fully supported in transformers v4.51.0 for easy loading and fine-tuning.
• Efficient Deployment: Supports tensor-parallelism and automatic device mapping.
These models offer developers enhanced capabilities for building sophisticated, multimodal AI applications. 
new activity
about 16 hours ago
huggingface-legal/takedown-notices:File 2023/2023-07-06-StabilityAI.md missing from index.csv
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nyuuzyou's activity
liked
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dataset
about 10 hours ago
reacted to
luigi12345's
post with 👍
about 10 hours ago
Post
793
🚀 Meta’s Llama 4 Models Now on Hugging Face!
Meta has released Llama 4 Scout and Llama 4 Maverick, now available on Hugging Face:
• Llama 4 Scout: 17B active parameters, 16-expert Mixture of Experts (MoE) architecture, 10M token context window, fits on a single H100 GPU. 
• Llama 4 Maverick: 17B active parameters, 128-expert MoE architecture, 1M token context window, optimized for DGX H100 systems. 
🔥 Key Features:
• Native Multimodality: Seamlessly processes text and images. 
• Extended Context Window: Up to 10 million tokens for handling extensive inputs.
• Multilingual Support: Trained on 200 languages, with fine-tuning support for 12, including Arabic, Spanish, and German. 
🛠️ Access and Integration:
• Model Checkpoints: Available under the meta-llama organization on the Hugging Face Hub.
• Transformers Compatibility: Fully supported in transformers v4.51.0 for easy loading and fine-tuning.
• Efficient Deployment: Supports tensor-parallelism and automatic device mapping.
These models offer developers enhanced capabilities for building sophisticated, multimodal AI applications. 
Meta has released Llama 4 Scout and Llama 4 Maverick, now available on Hugging Face:
• Llama 4 Scout: 17B active parameters, 16-expert Mixture of Experts (MoE) architecture, 10M token context window, fits on a single H100 GPU. 
• Llama 4 Maverick: 17B active parameters, 128-expert MoE architecture, 1M token context window, optimized for DGX H100 systems. 
🔥 Key Features:
• Native Multimodality: Seamlessly processes text and images. 
• Extended Context Window: Up to 10 million tokens for handling extensive inputs.
• Multilingual Support: Trained on 200 languages, with fine-tuning support for 12, including Arabic, Spanish, and German. 
🛠️ Access and Integration:
• Model Checkpoints: Available under the meta-llama organization on the Hugging Face Hub.
• Transformers Compatibility: Fully supported in transformers v4.51.0 for easy loading and fine-tuning.
• Efficient Deployment: Supports tensor-parallelism and automatic device mapping.
These models offer developers enhanced capabilities for building sophisticated, multimodal AI applications. 
File 2023/2023-07-06-StabilityAI.md missing from index.csv
1
#8 opened 3 days ago
by
nyuuzyou
reacted to
jsulz's
post with 🔥
2 days ago
Post
3204
Huge week for
xet-team
as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.
Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.
We expect builders on the Hub to see even more improvements, helping power innovation across the community.
With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.
Thanks to the
meta-llama
team for launching on Xet!
xet-team
as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.
We expect builders on the Hub to see even more improvements, helping power innovation across the community.
With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.
Thanks to the
meta-llama
team for launching on Xet!
liked
2
models
2 days ago
reacted to
merterbak's
post with 🔥
2 days ago
Post
2774
Meta has unveiled its Llama 4 🦙 family of models, featuring native multimodality and mixture-of-experts architecture. Two model families are available now:
Models🤗: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Blog Post: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
HF's Blog Post: https://huggingface.co/blog/llama4-release
- 🧠 Native Multimodality - Process text and images in a unified architecture
- 🔍 Mixture-of-Experts - First Llama models using MoE for incredible efficiency
- 📏 Super Long Context - Up to 10M tokens
- 🌐 Multilingual Power - Trained on 200 languages with 10x more multilingual tokens than Llama 3 (including over 100 languages with over 1 billion tokens each)
🔹 Llama 4 Scout
- 17B active parameters (109B total)
- 16 experts architecture
- 10M context window
- Fits on a single H100 GPU
- Beats Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1
🔹 Llama 4 Maverick
- 17B active parameters (400B total)
- 128 experts architecture
- It can fit perfectly on DGX H100(8x H100)
- 1M context window
- Outperforms GPT-4o and Gemini 2.0 Flash
- ELO score of 1417 on LMArena currently second best model on arena
🔹 Llama 4 Behemoth (Coming Soon)
- 288B active parameters (2T total)
- 16 experts architecture
- Teacher model for Scout and Maverick
- Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks
Models🤗: meta-llama/llama-4-67f0c30d9fe03840bc9d0164
Blog Post: https://ai.meta.com/blog/llama-4-multimodal-intelligence/
HF's Blog Post: https://huggingface.co/blog/llama4-release
- 🧠 Native Multimodality - Process text and images in a unified architecture
- 🔍 Mixture-of-Experts - First Llama models using MoE for incredible efficiency
- 📏 Super Long Context - Up to 10M tokens
- 🌐 Multilingual Power - Trained on 200 languages with 10x more multilingual tokens than Llama 3 (including over 100 languages with over 1 billion tokens each)
🔹 Llama 4 Scout
- 17B active parameters (109B total)
- 16 experts architecture
- 10M context window
- Fits on a single H100 GPU
- Beats Gemma 3, Gemini 2.0 Flash-Lite, and Mistral 3.1
🔹 Llama 4 Maverick
- 17B active parameters (400B total)
- 128 experts architecture
- It can fit perfectly on DGX H100(8x H100)
- 1M context window
- Outperforms GPT-4o and Gemini 2.0 Flash
- ELO score of 1417 on LMArena currently second best model on arena
🔹 Llama 4 Behemoth (Coming Soon)
- 288B active parameters (2T total)
- 16 experts architecture
- Teacher model for Scout and Maverick
- Outperforms GPT-4.5, Claude Sonnet 3.7, and Gemini 2.0 Pro on STEM benchmarks
upvoted
a
collection
2 days ago
🚩 Report: Copyright infringement
1
#1 opened 3 days ago
by
ItakuEe
published
a
dataset
3 days ago
updated
a
dataset
3 days ago
reacted to
clem's
post with 🔥
3 days ago
Post
1844
Llama models (arguably the most successful open AI models of all times) just represented 3% of total model downloads on Hugging Face in March.
People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!
Kudos to all the small AI builders out there!
People and media like stories of winner takes all & one model/company to rule them all but the reality is much more nuanced than this!
Kudos to all the small AI builders out there!
- 2 replies
reacted to
Reality123b's
post with 🧠
3 days ago
Post
459
Does anyone know how to convert a replit app into a huggingface spaces app?
reacted to
ritvik77's
post with 🔥
3 days ago
Post
3126
Hi 🤗HF Community,
I would be incredibly grateful for an opportunity to contribute — in any capacity — and learn alongside researchers here. Is there any possibility I could collaborate or assist with any of your research works ?
I’m happy to support ongoing projects, contribute to data analysis, code, documentation, or anything that adds value.
Thank you for your time and consideration!
Warm regards,
Ritvik Gaur
I would be incredibly grateful for an opportunity to contribute — in any capacity — and learn alongside researchers here. Is there any possibility I could collaborate or assist with any of your research works ?
I’m happy to support ongoing projects, contribute to data analysis, code, documentation, or anything that adds value.
Thank you for your time and consideration!
Warm regards,
Ritvik Gaur
- 1 reply
🚩 Report: Copyright infringement
1
#6 opened 4 days ago
by
no-mad
reacted to
AdinaY's
post with 🔥
4 days ago
Post
2449
Dolphin 🐬 an open ASR model released by DataOceanAI, one of the biggest AI data provider in China 🔥
✨ Supports 40 Eastern languages & 22 Chinese dialects
✨ Apache2.0
✨ With 21.2M hours of data (7.4M open data)
Model:
DataoceanAI/dolphin-base
DataoceanAI/dolphin-small
Paper:
Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages (2503.20212)
✨ Supports 40 Eastern languages & 22 Chinese dialects
✨ Apache2.0
✨ With 21.2M hours of data (7.4M open data)
Model:
DataoceanAI/dolphin-base
DataoceanAI/dolphin-small
Paper:
Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern Languages (2503.20212)
- 1 reply
posted
an
update
4 days ago
Post
927
✈️ Thanks for the interest shown in the FlightAware Photos dataset (
nyuuzyou/flightaware). Seeing its potential, I'm working on expanding it to over 1 million images soon.
---
🎨 Introducing the PaintBerri Hand-Drawn Art Dataset - nyuuzyou/paintberri
A collection of 68,860 digital hand-drawn artworks featuring:
Unique images sourced directly from the paintberri.com online art community.
Rich metadata including creator-provided titles, descriptions, and timestamps.
Image dimensions, thumbnail URLs, and NSFW content flags.
Creator IDs (where available) and unique short identifiers for each piece.
This dataset offers a distinct visual archive capturing diverse styles and subjects from an active online drawing community, suitable for image classification and image-to-text tasks. Opt-out is available for creators wishing to remove their work.
---
🎨 Introducing the PaintBerri Hand-Drawn Art Dataset - nyuuzyou/paintberri
A collection of 68,860 digital hand-drawn artworks featuring:
Unique images sourced directly from the paintberri.com online art community.
Rich metadata including creator-provided titles, descriptions, and timestamps.
Image dimensions, thumbnail URLs, and NSFW content flags.
Creator IDs (where available) and unique short identifiers for each piece.
This dataset offers a distinct visual archive capturing diverse styles and subjects from an active online drawing community, suitable for image classification and image-to-text tasks. Opt-out is available for creators wishing to remove their work.
reacted to
abidlabs's
post with ❤️❤️
4 days ago
Post
3006
JOURNEY TO 1 MILLION DEVELOPERS
5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.
Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.
How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:
1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs
1. Invest in good primitives, not high-level abstractions
When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:
Read the rest here: https://x.com/abidlabs/status/1907886
5 years ago, we launched Gradio as a simple Python library to let researchers at Stanford easily demo computer vision models with a web interface.
Today, Gradio is used by >1 million developers each month to build and share AI web apps. This includes some of the most popular open-source projects of all time, like Automatic1111, Fooocus, Oobabooga’s Text WebUI, Dall-E Mini, and LLaMA-Factory.
How did we get here? How did Gradio keep growing in the very crowded field of open-source Python libraries? I get this question a lot from folks who are building their own open-source libraries. This post distills some of the lessons that I have learned over the past few years:
1. Invest in good primitives, not high-level abstractions
2. Embed virality directly into your library
3. Focus on a (growing) niche
4. Your only roadmap should be rapid iteration
5. Maximize ways users can consume your library's outputs
1. Invest in good primitives, not high-level abstractions
When we first launched Gradio, we offered only one high-level class (gr.Interface), which created a complete web app from a single Python function. We quickly realized that developers wanted to create other kinds of apps (e.g. multi-step workflows, chatbots, streaming applications), but as we started listing out the apps users wanted to build, we realized what we needed to do:
Read the rest here: https://x.com/abidlabs/status/1907886
updated
a
dataset
4 days ago