Share and discover more about AI with social posts from the community.huggingface/OpenAi
🚀 Introducing Hugging Face Similar: a Chrome extension to find relevant datasets!
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
✨ Adds a "Similar Datasets" section to Hugging Face dataset pages
🔍 Recommendations based on dataset READMEs
🏗 Powered by https://huggingface.co/chromadb and https://huggingface.co/Snowflake embeddings.
You can try it here: https://chromewebstore.google.com/detail/hugging-face-similar/aijelnjllajooinkcpkpbhckbghghpnl?authuser=0&hl=en.
I am very happy to get feedback on whether this could be useful or not 🤗
huggingface.co
chromadb (chroma)
Org profile for chroma on Hugging Face, the AI community building the future.
🤗 Serving Meta Llama 3.1 405B on Google Cloud is now possible via the Hugging Face Deep Learning Containers (DLCs) for Text Generation Inference (TGI)
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as
In this post, we showcase how to deploy
meta-llama/Meta-Llama-3.1-405B-Instruct-FP8
on an A3 instance with 8 x H100 GPUs on Vertex AI
Thanks to the Hugging Face DLCs for TGI and Google Cloud Vertex AI, deploying a high-performance text generation container for serving Large Language Models (LLMs) has never been easier. And we’re not going to stop here – stay tuned as
🚀 How The Washington Post Uses AI to Empower Journalists 🔍📰
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
An exciting new example in the world of AI-assisted journalism! The Post has developed an internal tool called "Hayatacker" that's enhancing in-depth reporting. Here's why it matters:
🎥 What it does:
• Extracts stills from video files
• Processes on-screen text
🚀 We will be generating a preference dataset for DPO/ORPO and cleaning it with AI feedback during our upcoming meetup!
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
In this session, we'll walk you through the essentials of building a distilabel pipeline by exploring two key use cases: cleaning an existing dataset and generating a preference dataset for DPO/ORPO. You’ll also learn how to make the most of AI feedback, integrating Argilla to gather human feedback and improve the overall data quality.
𝗚𝗼𝗼𝗴𝗹𝗲 𝗽𝗮𝗽𝗲𝗿 : 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝘂𝗽 𝗶𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗰𝗼𝗺𝗽𝘂𝘁𝗲 𝗯𝗲𝗮𝘁𝘀 𝟭𝟰𝘅 𝗹𝗮𝗿𝗴𝗲𝗿 𝗺𝗼𝗱𝗲𝗹𝘀 🚀
Remember scaling laws? These are empirical laws that say "the bigger your model, the better it gets". More precisely, "as your compute increases exponentially, loss decreases in a linear fashion". They have wild implications, suggesting that spending 100x more training compute would make you super-LLMs. That's why companies are racing to build the biggest AI superclusters ever, and Meta bought 350k H100 GPUs, which probably cost in the order of $1B.
But think of this : we're building huge reasoning machines, but only ask them to do one pass through the mod
Remember scaling laws? These are empirical laws that say "the bigger your model, the better it gets". More precisely, "as your compute increases exponentially, loss decreases in a linear fashion". They have wild implications, suggesting that spending 100x more training compute would make you super-LLMs. That's why companies are racing to build the biggest AI superclusters ever, and Meta bought 350k H100 GPUs, which probably cost in the order of $1B.
But think of this : we're building huge reasoning machines, but only ask them to do one pass through the mod
🚀 Meet the new GLiNER architecture 🚀
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results. We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:
🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.
GLiNER revolutionized zero-shot NER by demonstrating that lightweight encoders can achieve excellent results. We're excited to continue R&D with this spirit 🔥. Our new bi-encoder and poly-encoder architectures were developed to address the main limitations of the original GLiNER architecture and bring the following new possibilities:
🔹 An unlimited number of entities can be recognized at once.
🔹Faster inference when entity embeddings are preprocessed.
🔹Better generalization to unseen entities.
'Legal Dictionary GPT' is now completely trained and ready for Open Source release to the world! Trained on 10,000 rows of legal definitions, Legal Dictionary GPT is your go-to resource for everything related to the first step in understanding the law, defining it. The model is free and publicly available for anyone to use.
Model Link: https://platform.openai.com/playground/chat?preset=eCrKdaPe9cnMnyTETqWDCQAU
Knowledge Base Bots are internal facing as opposed to external facing LLM models, that are either fine tuned or RAG tuned, generally on systems and processes related data.
Model Link: https://platform.openai.com/playground/chat?preset=eCrKdaPe9cnMnyTETqWDCQAU
Knowledge Base Bots are internal facing as opposed to external facing LLM models, that are either fine tuned or RAG tuned, generally on systems and processes related data.
Openai
OpenAI Platform
Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.
BIG update dropped for
bigdata-pw/Flickr
- now ~515M images! Target for the next update: 1B
In case you missed them; other recent drops include
bigdata-pw/Dinosaurs
- a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102
Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
- TV/Film actors biographies and photos (thin
bigdata-pw/Flickr
- now ~515M images! Target for the next update: 1B
In case you missed them; other recent drops include
bigdata-pw/Dinosaurs
- a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102
Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
- TV/Film actors biographies and photos (thin
huggingface.co
Web Scraping 101
A Blog post by hlky on Hugging Face
We are proud to release our latest suite of three image(s)-to-3D Gradio demos and two new papers.
SpaRP (Unposed sparse views to 3D):
sudo-ai/SpaRP
SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views (2408.10195)
MeshFormer (@minghua @NCJ ):
sudo-ai/MeshFormer
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model (2408.10198)
MeshLRM-reproduced (@sarahwei0210 ):
sudo-ai/MeshLRM
https://huggingface.co/spaces/sudo-ai/MeshLRM
SpaRP (Unposed sparse views to 3D):
sudo-ai/SpaRP
SpaRP: Fast 3D Object Reconstruction and Pose Estimation from Sparse Views (2408.10195)
MeshFormer (@minghua @NCJ ):
sudo-ai/MeshFormer
MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model (2408.10198)
MeshLRM-reproduced (@sarahwei0210 ):
sudo-ai/MeshLRM
https://huggingface.co/spaces/sudo-ai/MeshLRM
huggingface.co
MeshLRM (Unofficial) - a Hugging Face Space by sudo-ai
Discover amazing ML apps made by the community
Cooked up a cool & much faster AI voice assistant space that also supports speech translation (with seamless-expressive). Start with the phrase "Please translate" followed by the speech you'd like to translate, to activate speech translation mode. Using opensource LLMs (Llama 3, Mistral etc) with edge tts for voice assistant and seamless-expressive for speech translation.
Give it a try:
Jaward/optimus
https://huggingface.co/spaces/Jaward/optimus
Give it a try:
Jaward/optimus
https://huggingface.co/spaces/Jaward/optimus
huggingface.co
Optimus - a Hugging Face Space by Jaward
Discover amazing ML apps made by the community
The Minimalist Spaces That May Be Helpful !!
Grab Doc | Type Byte | SD3 CLI
- Grab Doc:
prithivMLmods/GRAB-DOC
- Type Byte:
prithivMLmods/Type-Byte
- SD3 CLI:
prithivMLmods/SD3-CLI
Grab Doc | Type Byte | SD3 CLI
- Grab Doc:
prithivMLmods/GRAB-DOC
- Type Byte:
prithivMLmods/Type-Byte
- SD3 CLI:
prithivMLmods/SD3-CLI
Falcon Mamba now available now in llama.cpp !
Check out GGUF files uploaded here:
tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
Check out GGUF files uploaded here:
tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a
This isn’t a goal of ours because we have plenty of money in the bank but quite excited to see that @huggingfaceis profitable these days, with 220 team members and most of our platform being free (like model hosting) and open-source for the community!
Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!
Especially noteworthy at a time when most AI startups wouldn’t survive a year or two without VC money. Yay!
Calling all Hugging Face users! We want to hear from YOU!
What feature or improvement would make the biggest impact on Hugging Face?
Whether it's the Hub, better documentation, new integrations, or something completely different – we're all ears!
Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! 👇
What feature or improvement would make the biggest impact on Hugging Face?
Whether it's the Hub, better documentation, new integrations, or something completely different – we're all ears!
Your feedback shapes the future of Hugging Face. Drop your ideas in the comments below! 👇
NEW TASK ALERT 🚨
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
Extractive Question Answering: because sometimes generative is not all you need 😉
AutoTrain is the only open-source, no code solution to offer so many tasks across different modalities. Current task count: 23 🚀
Check out the blog post on getting started with this task: https://huggingface.co/blog/abhishek/extractive-qa-autotrain
huggingface.co
Extractive Question Answering with AutoTrain
A Blog post by Abhishek Thakur on Hugging Face
BIG update dropped for
bigdata-pw/Flickr
- now ~515M images! Target for the next update: 1B
In case you missed them; other recent drops include
bigdata-pw/Dinosaurs
- a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102
Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
bigdata-pw/Flickr
- now ~515M images! Target for the next update: 1B
In case you missed them; other recent drops include
bigdata-pw/Dinosaurs
- a small set of BIG creatures 🦕🦖 and the first in a series of articles about the art of web scraping! https://huggingface.co/blog/hlky/web-scraping-101 https://huggingface.co/blog/hlky/web-scraping-102
Stay tuned for exciting datasets and models coming soon:
- PC and Console game screenshots
huggingface.co
Web Scraping 101
A Blog post by hlky on Hugging Face
Just dropped a fresh version of dataset-viber along with some cool, Gradio-based annotators! These tools aren't about formalities—they're here to help you quickly collect feedback and get your projects moving along to a more serious stage, ahumm @argilla.
Some new features!
- manual import from a CSV or the Hugging Face Hub
- manual export to CSV or the Hub
- improved automated export to the Hub and CSV
Some new features!
- manual import from a CSV or the Hugging Face Hub
- manual export to CSV or the Hub
- improved automated export to the Hub and CSV
When On-Premise is Better than the Cloud
During my time at Palantir, I have spent significant time deploying our software in cloud environments and also a good chunk of time deploying our software in on-premise (on-prem) environments (including starting a team doing just that). I have noticed that despite the common preference for cloud deployment, there are still merits to deploying on-prem.
The Shift from On-Prem to Cloud Computing
Over recent years, the IT landscape has increasingly favored cloud computing, driven by the flexibility of Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) offerings. The global cloud computing market grew from $24.63 billion in 2010 to $156.4 billion in 2020, and that trend continues and is predicted to surpass $1 trillion by 2028. This meteoric rise is powered by both new demand of compute by the world, but also migration of on-prem workflows to the cloud.
There are good reasons for this shift, the cloud enables rapid provisioning of resources, geographic redundancy, and a shift from capital expenditures (CapEx) to operational expenditures (OpEx). However, I believe that there are certain scenarios where the use of on-prem infrastructure, particularly where specific technical requirements, such as deterministic latency, hardware-level control, and stringent security measures, are paramount.
During my time at Palantir, I have spent significant time deploying our software in cloud environments and also a good chunk of time deploying our software in on-premise (on-prem) environments (including starting a team doing just that). I have noticed that despite the common preference for cloud deployment, there are still merits to deploying on-prem.
The Shift from On-Prem to Cloud Computing
Over recent years, the IT landscape has increasingly favored cloud computing, driven by the flexibility of Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) offerings. The global cloud computing market grew from $24.63 billion in 2010 to $156.4 billion in 2020, and that trend continues and is predicted to surpass $1 trillion by 2028. This meteoric rise is powered by both new demand of compute by the world, but also migration of on-prem workflows to the cloud.
There are good reasons for this shift, the cloud enables rapid provisioning of resources, geographic redundancy, and a shift from capital expenditures (CapEx) to operational expenditures (OpEx). However, I believe that there are certain scenarios where the use of on-prem infrastructure, particularly where specific technical requirements, such as deterministic latency, hardware-level control, and stringent security measures, are paramount.
Access and Distribution:
Think of vaccine distribution like a pizza delivery service—except the stakes are much higher, and there are way more “no-delivery zones.” Low-income and remote areas often struggle to get vaccines where they’re needed most. It’s like ordering a pizza to the middle of the Sahara—tricky and sounds impossible, right?
Vaccine Hesitancy:
Ah, the kryptonite of public health. Despite overwhelming evidence, some folks are still hesitant to roll up their sleeves. Whether it’s misinformation on social media or just plain fear of needles, vaccine hesitancy is a real buzzkill. I hope you remember the times when UNICEF used all the tricks held up its sleeves to build confidence in COVID-19 vaccines in the Indian subcontinent.
Think of vaccine distribution like a pizza delivery service—except the stakes are much higher, and there are way more “no-delivery zones.” Low-income and remote areas often struggle to get vaccines where they’re needed most. It’s like ordering a pizza to the middle of the Sahara—tricky and sounds impossible, right?
Vaccine Hesitancy:
Ah, the kryptonite of public health. Despite overwhelming evidence, some folks are still hesitant to roll up their sleeves. Whether it’s misinformation on social media or just plain fear of needles, vaccine hesitancy is a real buzzkill. I hope you remember the times when UNICEF used all the tricks held up its sleeves to build confidence in COVID-19 vaccines in the Indian subcontinent.