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Making AI decisions understandable and transparent to users

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derek-thomasย  updated a collection 20 days ago
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clboetticher-hfย  updated a collection 28 days ago
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clboetticher-hfย  updated a collection 28 days ago
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florentgbelidjiย 
posted an update 20 days ago
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1436
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!
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andrewrreedย 
posted an update 30 days ago
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2695
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 about 1 month ago
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597
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 3 months ago
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1032
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 3 months ago
jeffboudierย 
posted an update 4 months ago
jeffboudierย 
posted an update 5 months ago
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459
Inference Endpoints got a bunch of cool updates yesterday, this is my top 3
jeffboudierย 
posted an update 5 months ago
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4043
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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andrewrreedย 
posted an update 9 months ago
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2583
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 10 months ago
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2323
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
andrewrreedย 
posted an update 12 months ago
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๐Ÿš€ It's now easier than ever to switch from OpenAI to open LLMs

Hugging Face's TGI now supports an OpenAI compatible Chat Completion API

This means you can transition code that uses OpenAI client libraries (or frameworks like LangChain ๐Ÿฆœ and LlamaIndex ๐Ÿฆ™) to run open models by changing just two lines of code ๐Ÿค—

โญ Here's how:
from openai import OpenAI

# initialize the client but point it to TGI
client = OpenAI(
    base_url="<ENDPOINT_URL>" + "/v1/",  # replace with your endpoint url
    api_key="<HF_API_TOKEN>",  # replace with your token
)
chat_completion = client.chat.completions.create(
    model="tgi",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Why is open-source software important?"},
    ],
    stream=True,
    max_tokens=500
)

# iterate and print stream
for message in chat_completion:
    print(message.choices[0].delta.content, end="")


๐Ÿ”— Blog post โžก https://huggingface.co/blog/tgi-messages-api
๐Ÿ”— TGI docs โžก https://huggingface.co/docs/text-generation-inference/en/messages_api
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derek-thomasย 
updated a Space almost 2 years ago