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Making AI decisions understandable and transparent to users

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derek-thomasย  updated a collection about 21 hours ago
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clboetticherย  updated a collection 9 days ago
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florentgbelidjiย 
posted an update about 16 hours ago
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๐—ฃ๐—น๐—ฎ๐—ป๐—ป๐—ถ๐—ป๐—ด ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ก๐—ฒ๐˜…๐˜ ๐—ฆ๐—ธ๐—ถ ๐—”๐—ฑ๐˜ƒ๐—ฒ๐—ป๐˜๐˜‚๐—ฟ๐—ฒ ๐—๐˜‚๐˜€๐˜ ๐—š๐—ผ๐˜ ๐—ฆ๐—บ๐—ฎ๐—ฟ๐˜๐—ฒ๐—ฟ: ๐—œ๐—ป๐˜๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐—ถ๐—ป๐—ด ๐—”๐—น๐—ฝ๐—ถ๐—ป๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜!๐Ÿ”๏ธโ›ท๏ธ

With the big hype around AI agents these days, I couldnโ€™t stop thinking about how AI agents could truly enhance real-world activities.
What sort of applications could we build with those AI agents: agentic RAG? self-correcting text-to-sql? Nah, boringโ€ฆ

Passionate about outdoors, Iโ€™ve always dreamed of a tool that could simplify planning mountain trips while accounting for all potential risks. Thatโ€™s why I built ๐—”๐—น๐—ฝ๐—ถ๐—ป๐—ฒ ๐—”๐—ด๐—ฒ๐—ป๐˜, a smart assistant designed to help you plan safe and enjoyable itineraries in the French Alps and Pyrenees.

Built using Hugging Face's ๐˜€๐—บ๐—ผ๐—น๐—ฎ๐—ด๐—ฒ๐—ป๐˜๐˜€ library, Alpine Agent combines the power of AI with trusted resources like ๐˜š๐˜ฌ๐˜ช๐˜ต๐˜ฐ๐˜ถ๐˜ณ.๐˜ง๐˜ณ (https://skitour.fr/) and METEO FRANCE. Whether itโ€™s suggesting a route with moderate difficulty or analyzing avalanche risks and weather conditions, this agent dynamically integrates data to deliver personalized recommendations.

In my latest blog post, I share how I developed this projectโ€”from defining tools and integrating APIs to selecting the best LLMs like ๐˜˜๐˜ธ๐˜ฆ๐˜ฏ2.5-๐˜Š๐˜ฐ๐˜ฅ๐˜ฆ๐˜ณ-32๐˜‰-๐˜๐˜ฏ๐˜ด๐˜ต๐˜ณ๐˜ถ๐˜ค๐˜ต, ๐˜“๐˜ญ๐˜ข๐˜ฎ๐˜ข-3.3-70๐˜‰-๐˜๐˜ฏ๐˜ด๐˜ต๐˜ณ๐˜ถ๐˜ค๐˜ต, or ๐˜Ž๐˜—๐˜›-4.

โ›ท๏ธ Curious how AI can enhance adventure planning?โ€จTry the app and share your thoughts: florentgbelidji/alpine-agent

๐Ÿ‘‰ Want to build your own agents? Whether for cooking, sports training, or other passions, the possibilities are endless. Check out the blog post to learn more: https://huggingface.co/blog/florentgbelidji/alpine-agent

Many thanks to @m-ric for helping on building this tool with smolagents!
andrewrreedย 
posted an update 11 days ago
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๐Ÿš€ Supercharge your LLM apps with Langfuse on Hugging Face Spaces!

Langfuse brings end-to-end observability and tooling to accelerate your dev workflow from experiments through production

Now available as a Docker Space directly on the HF Hub! ๐Ÿค—

๐Ÿ” Trace everything: monitor LLM calls, retrieval, and agent actions with popular frameworks
1โƒฃ One-click deployment: on Spaces with persistent storage and integrated OAuth
๐Ÿ›  Simple Prompt Management: Version, edit, and update without redeployment
โœ… Intuitive Evals: Collect user feedback, run model/prompt evaluations, and improve quality
๐Ÿ“Š Dataset Creation: Build datasets directly from production data to enhance future performance

Kudos to the Langfuse team for this collab and the awesome, open-first product theyโ€™re building! ๐Ÿ‘ @marcklingen @Clemo @MJannik

๐Ÿ”— Space: langfuse/langfuse-template-space
๐Ÿ”— Docs: https://huggingface.co/docs/hub/spaces-sdks-docker-langfuse
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jeffboudierย 
posted an update 11 days ago
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NVIDIA just announced the Cosmos World Foundation Models, available on the Hub: nvidia/cosmos-6751e884dc10e013a0a0d8e6

Cosmos is a family of pre-trained models purpose-built for generating physics-aware videos and world states to advance physical AI development.
The release includes Tokenizers nvidia/cosmos-tokenizer-672b93023add81b66a8ff8e6

Learn more in this great community article by @mingyuliutw and @PranjaliJoshi https://huggingface.co/blog/mingyuliutw/nvidia-cosmos
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andrewrreedย 
posted an update about 2 months ago
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Trace LLM calls with Arize AI's Phoenix observability dashboards on Hugging Face Spaces! ๐Ÿš€

โœจ I just added a new recipe to the Open-Source AI Cookbook that shows you how to:
1๏ธโƒฃ Deploy Phoenix on HF Spaces with persistent storage in a few clicks
2๏ธโƒฃ Configure LLM tracing with the ๐—ฆ๐—ฒ๐—ฟ๐˜ƒ๐—ฒ๐—ฟ๐—น๐—ฒ๐˜€๐˜€ ๐—œ๐—ป๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—”๐—ฃ๐—œ
3๏ธโƒฃ Observe multi-agent application runs with the CrewAI integration

๐—ข๐—ฏ๐˜€๐—ฒ๐—ฟ๐˜ƒ๐—ฎ๐—ฏ๐—ถ๐—น๐—ถ๐˜๐˜† ๐—ถ๐˜€ ๐—ฐ๐—ฟ๐˜‚๐—ฐ๐—ถ๐—ฎ๐—น for building robust LLM apps.

Phoenix makes it easy to visualize trace data, evaluate performance, and track down issues. Give it a try!

๐Ÿ”— Cookbook recipe: https://huggingface.co/learn/cookbook/en/phoenix_observability_on_hf_spaces
๐Ÿ”— Phoenix docs: https://docs.arize.com/phoenix
jeffboudierย 
posted an update about 2 months ago
nbroadย 
posted an update 3 months ago
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3583
hi florent and livestream!
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jeffboudierย 
posted an update 3 months ago
jeffboudierย 
posted an update 4 months ago
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Inference Endpoints got a bunch of cool updates yesterday, this is my top 3
jeffboudierย 
posted an update 4 months ago
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Pro Tip - if you're a Firefox user, you can set up Hugging Chat as integrated AI Assistant, with contextual links to summarize or simplify any text - handy!

In this short video I show how to set it up
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derek-thomasย 
posted an update 5 months ago
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2184
Here is an AI Puzzle!
When you solve it just use a ๐Ÿ˜Ž emoji.
NO SPOILERS
A similar puzzle might have each picture that has a hidden meaning of summer, winter, fall, spring, and the answer would be seasons.

Its a little dated now (almost a year), so bottom right might be tough.

Thanks to @johko for the encouragement to post!
andrewrreedย 
posted an update 9 months ago
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๐Ÿ”ฌ Open LLM Progress Tracker ๐Ÿ”ฌ

Inspired by the awesome work from @mlabonne , I created a Space to monitor the narrowing gap between open and proprietary LLMs as scored by the LMSYS Chatbot Arena ELO ratings ๐Ÿค—

The goal is to have a continuously updated place to easily visualize these rapidly evolving industry trends ๐Ÿš€

๐Ÿ”— Open LLM Progress Tracker: andrewrreed/closed-vs-open-arena-elo
๐Ÿ”— Source of Inspiration: https://www.linkedin.com/posts/maxime-labonne_arena-elo-graph-updated-with-new-models-activity-7187062633735368705-u2jB/
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jeffboudierย 
posted an update 9 months ago
andrewrreedย 
posted an update 9 months ago
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IMO, the "grounded generation" feature from Cohere's CommandR+ has flown under the radar...

For RAG use cases, responses directly include inline citations, making source attribution an inherent part of generation rather than an afterthought ๐Ÿ˜Ž

Who's working on an open dataset with this for the HF community to fine-tune with??

๐Ÿ”—CommandR+ Docs: https://docs.cohere.com/docs/retrieval-augmented-generation-rag

๐Ÿ”—Model on the ๐Ÿค— Hub: CohereForAI/c4ai-command-r-plus
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jeffboudierย 
posted an update 10 months ago