Csplk (Ci Splunk)

reacted to clem's post with 🤗 11 days ago

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It's just become easier to share your apps on the biggest AI app store (aka HF spaces) for unlimited storage, more visibility and community interactions.

Just pick a React, Svelte, or Vue template when you create your space or add app_build_command: npm run build in your README's YAML and app_file: build/index.html in your README's YAML block.

Or follow this link: https://huggingface.co/new-space?sdk=static

Let's build!

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replied to Yehor's post about 2 months ago

Time to podman my songs

reacted to Yehor's post with 🔥🔥 about 2 months ago

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2099

Convert your audio data to Parquet/DuckDB files with blazingly fast speeds!

Repository with pre-built binaries: https://github.com/crs-org/audios-to-dataset

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reacted to danielhanchen's post with ❤️ about 2 months ago

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You can now run Llama 4 on your own local device! 🦙
Run our Dynamic 1.78-bit and 2.71-bit Llama 4 GGUFs:
unsloth/Llama-4-Scout-17B-16E-Instruct-GGUF

You can run them on llama.cpp and other inference engines. See our guide here: https://docs.unsloth.ai/basics/tutorial-how-to-run-and-fine-tune-llama-4

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reacted to MikeDoes's post with 😎 3 months ago

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🚀 Ai4Privacy Team is excited to unveil PII-Masking-1M, our most significant release yet! 🎉

This publication series 📦 includes datasets 📊, models 🤖, and applications ⚙️ to advance PII masking with AI systems 🛡️

Starting on Monday with daily posts at 7 PM CET ⏰

reacted to thomwolf's post with ❤️ 3 months ago

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We've kept pushing our Open-R1 project, an open initiative to replicate and extend the techniques behind DeepSeek-R1.

And even we were mind-blown by the results we got with this latest model we're releasing: ⚡️OlympicCoder ( open-r1/OlympicCoder-7B and open-r1/OlympicCoder-32B)

It's beating Claude 3.7 on (competitive) programming –a domain Anthropic has been historically really strong at– and it's getting close to o1-mini/R1 on olympiad level coding with just 7B parameters!

And the best part is that we're open-sourcing all about its training dataset, the new IOI benchmark, and more in our Open-R1 progress report #3: https://huggingface.co/blog/open-r1/update-3

Datasets are are releasing:
- open-r1/codeforces
- open-r1/codeforces-cots
- open-r1/ioi
- open-r1/ioi-test-cases
- open-r1/ioi-sample-solutions
- open-r1/ioi-cots
- open-r1/ioi-2024-model-solutions

reacted to prithivMLmods's post with 🔥 3 months ago

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Dropping some of the custom fine-tunes based on SigLIP2,
with a single/multi label classification problem type! 🌀🧤

- AI vs Deepfake vs Real : prithivMLmods/AI-vs-Deepfake-vs-Real-Siglip2
- Deepfake Detect : prithivMLmods/Deepfake-Detect-Siglip2
- Fire Detection : prithivMLmods/Fire-Detection-Siglip2
- Deepfake Quality Assess : prithivMLmods/Deepfake-Quality-Assess-Siglip2
- Guard Against Unsafe Content : prithivMLmods/Guard-Against-Unsafe-Content-Siglip2

🌠Collection : prithivMLmods/siglip2-custom-67bcdb2de8fe96b99fb4e19e

reacted to davidberenstein1957's post with ❤️ 4 months ago

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🚀 Find banger tools for your smolagents!

I created the Tools gallery, which makes tools specifically developed by/for smolagents searchable and visible. This will help with:
- inspiration
- best practices
- finding cool tools

Space: davidberenstein1957/smolagents-and-tools

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reacted to hlarcher's post with 🔥 5 months ago

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We are introducing multi-backend support in Hugging Face Text Generation Inference!
With new TGI architecture we are now able to plug new modeling backends to get best performances according to selected model and available hardware. This first step will very soon be followed by the integration of new backends (TRT-LLM, llama.cpp, vLLM, Neuron and TPU).

We are polishing the TensorRT-LLM backend which achieves impressive performances on NVIDIA GPUs, stay tuned 🤗 !

Check out the details: https://huggingface.co/blog/tgi-multi-backend

reacted to CultriX's post with ❤️ 5 months ago

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# Space for Multi-Agent Workflows using AutoGen

Hi all, I created this "AutoGen Multi-Agent Workflow" space that allows you to experiment with multi-agent workflows.

By default, it allows code generation with built-in quality control and automatic documentation generation. It achieves this by leveraging multiple AI agents working together to produce high-quality code snippets, ensuring they meet the specified requirements.

In addition to the default, the space allows users to set custom system messages for each assistant, potentially completely changing the workflow.

# Workflow Steps
1. User Input:
- The user defines a prompt, such as "Write a random password generator using python."
- Outcome: A clear task for the primary assistant to accomplish.

2. Primary Assistant Work:
- The primary assistant begins working on the provided prompt.
It generates an initial code snippet based on the user's request.
- Outcome: An initial proposal for the requested code.

3. Critic Feedback:
- The critic reviews the generated code provides feedback or (if the output meets the criteria), broadcasts the APPROVED message.
(This process repeats until the output is APPROVED or 10 messages have been exchanged).
- Outcome: A revised Python function that incorporates the critic's feedback.

4. Documentation Generation:
- Once the code is approved, it is passed to a documentation assistant.
The documentation assistant generates a concise documentation for the final code.
- Outcome: A short documentation including function description, parameters, and return values.

Enjoy!
CultriX/AutoGen-MultiAgent-Example

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replied to singhsidhukuldeep's post 5 months ago

Paper for those who wish to read it themselves https://arxiv.org/abs/2305.19860

Github from authors https://github.com/WLiK/LLM4Rec-Awesome-Papers

reacted to cfahlgren1's post with 🔥 5 months ago

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Wow, I just added Langfuse tracing to the Deepseek Artifacts app and it's really nice 🔥

It allows me to visualize and track more things along with the cfahlgren1/react-code-instructions dataset.

It was just added as a one click Docker Space template, so it's super easy to self host 💪

reacted to davidberenstein1957's post with ❤️ 6 months ago

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Introducing the Synthetic Data Generator, a user-friendly application that takes a no-code approach to creating custom datasets with Large Language Models (LLMs). The best part: A simple step-by-step process, making dataset creation a non-technical breeze, allowing anyone to create datasets and models in minutes and without any code.

Blog: https://huggingface.co/blog/synthetic-data-generator
Space: https://huggingface.co/spaces/argilla/synthetic-data-generator

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reacted to alielfilali01's post with ❤️ 6 months ago

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Unpopular opinion: Open Source takes courage to do !

Not everyone is brave enough to release what they have done (the way they've done it) to the wild to be judged !
It really requires a high level of "knowing wth are you doing" ! It's kind of a super power !

Cheers to the heroes here who see this!

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reacted to dylanebert's post with 🔥 6 months ago

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Generate meshes with AI locally in Blender

📢 New open-source release

meshgen, a local blender integration of LLaMa-Mesh, is open source and available now 🤗

get started here: https://github.com/huggingface/meshgen

reacted to prithivMLmods's post with 👍 7 months ago

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Weekend Dribble 📦🍺

Adapters for Product Ad Backdrops, Smooth Polaroids, Minimalist Sketch cards, Super Blends!!

🤏Demo on: prithivMLmods/FLUX-LoRA-DLC

Stranger Zones :
👉🏼{ Super Blend } : strangerzonehf/Flux-Super-Blend-LoRA

👉🏼{ Product Concept Ad } : prithivMLmods/Flux-Product-Ad-Backdrop
👉🏼{ Frosted Mock-ups } : prithivMLmods/Flux.1-Dev-Frosted-Container-LoRA
👉🏼{ Polaroid Plus } : prithivMLmods/Flux-Polaroid-Plus
👉🏼{Sketch Cards} : prithivMLmods/Flux.1-Dev-Sketch-Card-LoRA

👉Stranger Zone:

strangerzonehf

👉Flux LoRA Collections: prithivMLmods/flux-lora-collections-66dd5908be2206cfaa8519be

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@prithivMLmods 🤗

reacted to csabakecskemeti's post with 👍 7 months ago

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Some time ago, I built a predictive LLM router that routes chat requests between small and large LLM models based on prompt classification. It dynamically selects the most suitable model depending on the complexity of the user input, ensuring optimal performance while maintaining conversation context. I also fine-tuned a RoBERTa model to use with the package, but you can plug and play any classifier of your choice.

Project's homepage:
https://devquasar.com/llm-predictive-router/
Pypi:
https://pypi.org/project/llm-predictive-router/
Model:
DevQuasar/roberta-prompt_classifier-v0.1
Training data:
DevQuasar/llm_router_dataset-synth
Git:
https://github.com/csabakecskemeti/llm_predictive_router_package

Feel free to check it out, and/or contribute.