SomosNLP

Fine-tune a SmolLM on domain-specific synthetic data from a LLM

Blog: https://huggingface.co/blog/davidberenstein1957/fine-tune-a-smollm-on-synthetic-data-of-llm

Fine-tuning ModernBERT for text classification using synthetic data generation

From prompt to model in 3 steps.
1 dataset description
20 minutes of generating
60 minutes of fine-tuning on my Macbook Pro

Tutorial: https://nbsanity.com/static/552eb50cbd91bedb4e5b73fddca2664a/fine-tune-modernbert-classifier.html

🐇 Tumble down the AI rabbit hole without any technical knowledge!

Explore AI models on the Hub by a simple and quick search

Demo: davidberenstein1957/transformers-pipeline-playground

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: argilla/synthetic-data-generator

Open Preference Dataset for Text-to-Image Generation by the 🤗 Community

Open Image Preferences is an Apache 2.0 licensed dataset for text-to-image generation. This dataset contains 10K text-to-image preference pairs across common image generation categories, while using different model families and varying prompt complexities.

https://huggingface.co/blog/image-preferences

🌐 Announcing Global-MMLU: an improved MMLU Open dataset with evaluation coverage across 42 languages, built with Argilla and the Hugging Face community.

Global-MMLU is the result of months of work with the goal of advancing Multilingual LLM evaluation. It's been an amazing open science effort with collaborators from Cohere For AI, Mila - Quebec Artificial Intelligence Institute, EPFL, Massachusetts Institute of Technology, AI Singapore, National University of Singapore, KAIST, Instituto Superior Técnico, Carnegie Mellon University, CONICET, and University of Buenos Aires.

🏷️ +200 contributors used Argilla MMLU questions where regional, dialect, or cultural knowledge was required to answer correctly. 85% of the questions required Western-centric knowledge!

Thanks to this annotation process, the open dataset contains two subsets:

1. 🗽 Culturally Agnostic: no specific regional, cultural knowledge is required.
2. ⚖️ Culturally Sensitive: requires dialect, cultural knowledge or geographic knowledge to answer correctly.

Moreover, we provide high quality translations of 25 out of 42 languages, thanks again to the community and professional annotators leveraging Argilla on the Hub.

I hope this will ensure a better understanding of the limitations and challenges for making open AI useful for many languages.

Dataset: CohereForAI/Global-MMLU

This is amazing for cheap models fine-tunes without the hassle of actual deployment! TIL: LoRA fine-tunes for models on the Hub can directly be used for inference!

The Data Is Better Together community is set to release the first Apache 2 licensed image preference dataset!

Great work and let's give this a final push :)

@aashish1904 congrats on your month of HF pro. There is more to win during this sprint!

@aashish1904 @AnyaDesdein @davidberenstein1957 @Malalatiana @beta3 @fffiloni @munish0838 @Reza2kn @bbunzeck @Creazycreator @andrei-saceleanu @jafhaponiuk @rca-etl @kf120 @burtenshaw @mmhamdy @grib0ed0v @Doopus @AnyaDes @ttkap @Xceron @Lewox @davanstrien @Azazelle @adirik @Ashish08 @AntonVic @kenantang @sdiazlor @g-ronimo @dennis-rall @prithivMLmods @girtss3 @flozi00 @WaveCut @Taylor658 @Wildminder @Sara9999 @phaelishall @sararob @dvilasuero @pgabrys @plaguss @CDS899 @timajwilliams @rudzinskimaciej @pavel-ai @aggr8 @ignacioct @MouseAI @Leeps @MaksKul @NicolasDmln @Muinez @kusht55 @caiolang @Jakub-Brand24 @loamy @Demijan @eliab96 @Viewegger @JosephCatrambone @p1atdev @mrshu @o639 @Targezed @Aviv-anthonnyolime @thliang01 @Ahmed-Amine @glards @pranaykoppula @nataliaElv @MaPirlet @alvarobartt @gabrielmbmb @zlicastro @Jaydip @Chouettecheveche @lilcheaty @ruyrdiaz @robintema @fdaudens @ggcristian @a-r-r-o-w @pates @joheras @stopsatgreen @bezo97 @chachi902 @iamyann @liamcripwell @dmb23 @korbih @anonymous7743 @akbdx18 @OVAWARE @severo @akontra @lichorosario @lhoestq @SebastianBodza @Vishnou @ameerazam08 @appoose @Mukei @mearco @joaquincabezas @Fizzarolli @thomastraum @igortopolski @OxxoCodes @patrickfleith @asoria @bn22 @sitammeur @Krodolf @bergr7f @Sbxxn @wietsevenema @sugatoray @Iamladi @MikeTrizna @feveromo @mokady @Bolero @prath @Dowwie @kfahn @decodingchris @alili2050 @RahulRaman @yzimmermann @Ameeeee @ecyht2 @MattMC001 @hemanthkumarak @Thegorgibus @akos2 @LawRun @ramithuh @SuperMuel @sjans @peterizsak @mosama @Eyel @mtr3 @cfahlgren1 @legentil @clem @Citaman @Aurelien-Morgan @AntoineBourgois @TotoB12 @Stanmey @osanseviero @multimodalart @maxiw @ariG23498 @ngk89 @femboysLover @dvs @tacohiddink @blanchon @DavidJimenez

🚀 Argilla v2.5.0 is out! 🎉
We’re excited to announce the latest version of Argilla, packed with features to make your data annotation workflows more powerful and seamless. Here’s what’s new:

✨ 1. Argilla Webhooks
With Argilla webhooks, you can:
* Trigger custom workflows
* Seamlessly integrate with external tools
* Build custom event-driven pipelines

🐍 2. Support for Python 3.13 and Pydantic v2
Argilla v2.5.0 now runs on:
* Python 3.13 for enhanced compatibility and speed
* Pydantic v2 for improved performance and type validation

🎨 3. Redesigned Home Page
Argilla's home page has been redesigned to provide a better user experience, showing a new
dataset card view, which provides a better overview of the datasets and annotation progress.

📖 Read the full release notes 👉 https://github.com/argilla-io/argilla/releases/tag/v2.5.0)
⬇️ Update now 👉 https://pypi.org/project/argilla)
or use the live demo 👉 argilla/argilla-template-space

🔥 Dataset Drop - Open Image Preferences

BlackForest Labs Flux Dev VS. Stability AI Stable Diffusion Large 3.5

Together with the ⁠data-is-better-together community, we've worked on an Apache 2.0 licensed open image preference dataset based on the fal ai imgsys prompts dataset. Thanks to the awesome community, we have managed to get 5K preference pairs in less than 2 days. The annotation alignment among annotators is great too.

Aashish Kumar won a month of Hugging Face Pro by making the most contributions! Congrats from the entire team 🥇

The best thing?! We are not done yet! Let's keep the annotations coming for 5K more in the second part of the sprint! (with more prices to go around).

Dataset: https://huggingface.co/datasets/data-is-better-together/image-preferences-results

Let’s make a generation of amazing image-generation models

The best image generation models are trained on human preference datasets, where annotators have selected the best image from a choice of two. Unfortunately, many of these datasets are closed source so the community cannot train open models on them. Let’s change that!

The community can contribute image preferences for an open-source dataset that could be used for building AI models that convert text to image, like the flux or stable diffusion families. The dataset will be open source so everyone can use it to train models that we can all use.

Blog: https://huggingface.co/blog/burtenshaw/image-preferences

Watch and learn!

Let's observe Qwen2.5-coder:0.5b on OpenAI HumanEval.

pip install observers

And start collecting your data on the Hugging Face Hub.
Dataset: davidberenstein1957/openai_records
Library: https://github.com/cfahlgren1/observers

🤗🔭 Introducing Observers: A Lightweight SDK for AI Observability 🔭🤗

Observers is an open-source Python SDK that provides comprehensive observability for AI applications. Our library makes it easy to:

- Track and record interactions with AI models
- Store observations in multiple backends
- Query and analyse your AI interactions with ease

https://huggingface.co/blog/davidberenstein1957/observers-a-lightweight-sdk-for-ai-observability

🚨 How green is your model? 🌱 Introducing a new feature in the Comparator tool: Environmental Impact for responsible #LLM research!
👉 open-llm-leaderboard/comparator
Now, you can not only compare models by performance, but also by their environmental footprint!

🌍 The Comparator calculates CO₂ emissions during evaluation and shows key model characteristics: evaluation score, number of parameters, architecture, precision, type... 🛠️
Make informed decisions about your model's impact on the planet and join the movement towards greener AI!