XLSCOUT Unveils ParaEmbed 2.0: a Powerful Embedding Model Tailored for Patents and IP with Expert Support from Hugging Face
โข
10
Andrew Reed
andrewrreed
AI & ML interests
Applied ML, Practical AI, Inference & Deployment, LLMs, Multi-modal Models, RAG
Recent Activity
upvoted
an
article
about 14 hours ago
๐ค Serve Anything with Inference Endpoints + Custom Handlers
posted
an
update
1 day ago
Trace LLM calls with Arize AI's Phoenix observability dashboards on Hugging Face Spaces! ๐
โจ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1๏ธโฃ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2๏ธโฃ Configure LLM tracing with the ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ๐น๐ฒ๐๐ ๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฃ๐
3๏ธโฃ Observe multi-agent application runs with the CrewAI integration
๐ข๐ฏ๐๐ฒ๐ฟ๐๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ถ๐ ๐ฐ๐ฟ๐๐ฐ๐ถ๐ฎ๐น for building robust LLM apps.
Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!
๐ Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
๐ Phoenix docs: https://docs.arize.com/phoenix
New activity
1 day ago
social-post-explorers/README:Add support for tagging orgs in posts
Articles
Organizations
andrewrreed's activity
upvoted
an
article
about 14 hours ago
Article
๐ค Serve Anything with Inference Endpoints + Custom Handlers
By
โข
โข
2
posted
an
update
1 day ago
Post
419
Trace LLM calls with Arize AI's Phoenix observability dashboards on Hugging Face Spaces! ๐
โจ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1๏ธโฃ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2๏ธโฃ Configure LLM tracing with the ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ๐น๐ฒ๐๐ ๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฃ๐
3๏ธโฃ Observe multi-agent application runs with the CrewAI integration
๐ข๐ฏ๐๐ฒ๐ฟ๐๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ถ๐ ๐ฐ๐ฟ๐๐ฐ๐ถ๐ฎ๐น for building robust LLM apps.
Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!
๐ Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
๐ Phoenix docs: https://docs.arize.com/phoenix
โจ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1๏ธโฃ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2๏ธโฃ Configure LLM tracing with the ๐ฆ๐ฒ๐ฟ๐๐ฒ๐ฟ๐น๐ฒ๐๐ ๐๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐๐ฃ๐
3๏ธโฃ Observe multi-agent application runs with the CrewAI integration
๐ข๐ฏ๐๐ฒ๐ฟ๐๐ฎ๐ฏ๐ถ๐น๐ถ๐๐ ๐ถ๐ ๐ฐ๐ฟ๐๐ฐ๐ถ๐ฎ๐น for building robust LLM apps.
Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!
๐ Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
๐ Phoenix docs: https://docs.arize.com/phoenix
Add support for tagging orgs in posts
#44 opened 1 day ago
by
andrewrreed
andrewrreed
Reacted to
m-ric's
post with โค๏ธ
1 day ago
Post
654
Made a new app to visualize the LLM race โ ๐ก๐ผ ๐๐๐ฟ๐ผ๐ฝ๐ฒ๐ฎ๐ป ๐ฐ๐ผ๐บ๐ฝ๐ฎ๐ป๐ ๐ถ๐ป ๐๐ต๐ฒ ๐๐ผ๐ฝ ๐ญ๐ฌ ๐ช๐บโ
See the app here ๐ m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US ๐บ๐ธ and Chinese ๐จ๐ณ companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI ๐ซ๐ท.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up ๐ฌ
โ ๏ธ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
See the app here ๐ m-ric/llm-race-to-the-top
I've adapted an app by @andrewrreed that tracks progress of LLMs ( andrewrreed/closed-vs-open-arena-elo), on the Chatbot Arena leaderboard, to compare companies from different countries.
The outcome is quite sad, as a Frenchman and European.
The top 10 is exclusively US ๐บ๐ธ and Chinese ๐จ๐ณ companies (after great Chinese LLM releases recently, like the Qwen2.5 series), with the notable exception of Mistral AI ๐ซ๐ท.
American companies are making fast progress, Chinese ones even faster. Europe is at risk of being left behind. And the EU AI Act hasn't even come into force yet to slow down the EU market. We need to wake up ๐ฌ
โ ๏ธ Caution: This Chatbot Arena ELO ranking is not the most accurate, especially at high scores like this, because LLM makers can game it to some extent.
liked
a
Space
1 day ago
updated
a
Space
2 days ago
fix colors
1
#4 opened 3 days ago
by
nbroad
nbroad
updated
a
collection
3 days ago
liked
2
Spaces
3 days ago
updated
a
collection
3 days ago
liked
2
Spaces
3 days ago
Reacted to
jsulz's
post with โค๏ธ๐ฅ
3 days ago
Post
2783
When the XetHub crew joined Hugging Face this fall,
@erinys
and I started brainstorming how to share our work to replace Git LFS on the Hub. Uploading and downloading large models and datasets takes precious time. Thatโs where our chunk-based approach comes in.
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
โฉ Only upload the chunks that changed.
๐ Download just the updates, not the whole file.
๐ง We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isnโt just a performance boost. Itโs a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks
Instead of versioning files (like Git and Git LFS), we version variable-sized chunks of data. For the Hugging Face community, this means:
โฉ Only upload the chunks that changed.
๐ Download just the updates, not the whole file.
๐ง We store your file as deduplicated chunks
In our benchmarks, we found that using CDC to store iterative model and dataset version led to transfer speedups of ~2x, but this isnโt just a performance boost. Itโs a rethinking of how we manage models and datasets on the Hub.
We're planning on our new storage backend to the Hub in early 2025 - check out our blog to dive deeper, and let us know: how could this improve your workflows?
https://huggingface.co/blog/from-files-to-chunks
upvoted
a
paper
4 days ago
updated
a
collection
4 days ago
liked
a
Space
4 days ago
liked
a
Space
5 days ago
upvoted
an
article
5 days ago
Article
The Beginners Guide to Cleaning a Dataset
By
โข
โข
23