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andrewrreedย 
posted an update 4 days ago
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2528
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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andrewrreedย 
posted an update about 2 months ago
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982
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
andrewrreedย 
posted an update 8 months ago
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2559
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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andrewrreedย 
posted an update 9 months ago
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2318
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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andrewrreedย 
posted an update 11 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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