🔥 Bespoke curator: Synthetic Data Curation for Post-Training & Structured Data Extraction Create synthetic data pipelines with easy! - Retries and caching included - inference via LiteLLM, vLLM, and popular batch APIs - asynchronous operations 🔗 URL: buff.ly/ajPRT1l

🔥One > token > at > a > time < a < at < token < One 🔥 token-explorer is a simple tool that lets you explore different possible paths that an LLM might sample! - Arrow keys to navigate, pop and append tokens - View the token probabilities and entropies. GitHub: buff.ly/FQgsczM

🍽️ Let’s dissect the Synthetic Dataset Generator 💬 Natural language prompt to data 🦙 Ollama ensures secure local LLM inference ✍🏼 Argilla’s data curation capabilities complete the workflow 🔗 GitHub: buff.ly/5pX49Xc

GitHub - argilla-io/synthetic-data-generator: Build datasets using natural language

Build datasets using natural language. Contribute to argilla-io/synthetic-data-generator development by creating an account on GitHub.

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🔥 Text2SQL, explore and share any data analysis! 🤗 Hugging Face - Dataset Studio is an amazing new feature. 🚀 Start yourself: buff.ly/pjpOKav

🔥 Vicinity: SEVEN semantic search BACK-ENDS, ONE single INTERFACE! 🫸 New release to push vector search to the Hub and work with any serialisable objects. 🧑‍🏫 KNN, HNSW, USEARCH, ANNOY, PYNNDESCENT, FAISS, and VOYAGER. 🔗 Library:

GitHub - MinishLab/vicinity: Lightweight Nearest Neighbors with Flexible Backends

Lightweight Nearest Neighbors with Flexible Backends - MinishLab/vicinity

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🔥 NEW cool NO-CODE solution for clicking together AI WEB APPS! 🎨 Gradio released "gradio sketch" 🚼 Really easy way to create web apps with minimal code. ⚙️ Start with `pip install gradio` & `gradio sketch` 📒 Release: https://buff.ly/41aeLoA

Vector Search - let's keep it clean and lightweight! ⚡️ <100K records, no problem! >100K, some scaling issues ANN DuckDB index, sub-second response times Notebook:

vector_search_with_hub_as_backend.ipynb

Run, share, and edit Python notebooks

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🔥 The smolagents module has arrived in the agents course! 💻 Code agents optimised for software development 🔧 Tool calling agents that create modular, function-driven workflows 🔍 Retrieval agents designed to access and synthesise information Course: https://buff.ly/4kcj6Ai

🧑‍🏫 Awesome. My talk for PyCon Italy 2025 got accepted! Got data problems? Relax. Synthetic data is here to help. Talk: https://buff.ly/3QzoZKj

🐳 Announcing docker support to Quickly set up your Synthetic Data Generator with (Gradio + Ollama + Argilla)! 🔥 Build genuinely useful datasets using natural language! ⚖️ Scale however you need. 🔐 Use them privately or share them with the world! 🧑‍💻 GitHub: https://buff.ly/49IDSmd

With 80K agent builders joining the agents course, it is time to make agents explorable on the Hub! You can now search and find the perfect agents and tools for your needs! Powered by @Gradio! Start searching:

smolagents and tools gallery - a Hugging Face Space by davidberenstein1957

Discover amazing ML apps made by the community

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Image Generation has landed in Arena form 🎨🤖! 1. Describe your desired image🎨 2. Two anonymous models output images 3. Vote for the winner! Images have been sourced from our Open Image Preference dataset! Dataset: https://buff.ly/4il0du9 Arena: https://buff.ly/4142NwH

Are you, the top of the Agents class?! We just released a bonus unit on function calling (FC). You will learn: ⑴ What is FC? ⑵ Thought → Act → Observe Cycle in FC ⑶ lightweight and efficient fine-tuning Course: https://buff.ly/3Qn1DHB

📹 In case you've missed the hype around smolagents, here is a presentation I gave yesterday at an MLOps community event! library: https://buff.ly/4hj6PrJ slides: https://buff.ly/3WUzZ8D video:

Smol Agents and Hugging Face - Anote AI Day Summit 2025

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Slides for my MLOps community talk on smolagents! Slides: https://buff.ly/3WUzZ8D

from bells and whistles to agents and tools

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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: https://buff.ly/41cYctx

🔥 Come and get those AI agents certificates! Join the cohort of 66K students: https://buff.ly/4hxb6rK

Documents or images to structured data using Vision Language Models Outlines has an integration with transformers, which facilitates structured generation based on limiting token sampling probabilities. Blog: https://buff.ly/4jFHMkr

Local docker deployments for the synthetic data generator 🫱🏾‍🫲🏼 We would love to hear your thoughts! PR: https://buff.ly/4hRMny6

Curious about "Why 🚀", you may wonder? smolagents effortlessness combined with the power of 400,000 AI tools available on the Hub! library: https://buff.ly/4hj6PrJ

WOW, this will rock the world! Hibiki is a model for simultaneous speech2speech translation. And it actually works. Available in French-English but super excited to see what the community will do. Hub: https://buff.ly/3EtmM0f Paper: https://buff.ly/4jIXNGd

Agentic RAG: Applied, visual, and step-by-step! 🐾 Get familiar with the Agents and tools, not the bells and whistles! Retrieve - Augment and now GENERATE. Parts: 1: https://buff.ly/40XNIxM 2: https://buff.ly/40HkB0x 3:

Agentic RAG Stack (3/5) - Generate responses using a SmolLM

A Blog post by David Berenstein on Hugging Face

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🤯 Bring your own AI data, even if you have none! Describe your dataset for RAG, LLMs or Text Classification Bring your own context! Press play and wait Space: https://buff.ly/3Y1S99z GitHub: https://buff.ly/49IDSmd

Anyone can create free hosted tools for their AI agents! 🔥 Agentic RAG stack part 2 - augment Augment retrieval results by reranking optimises content without increasing time too much part2: https://buff.ly/40HkB0x part1: https://buff.ly/40XNIxM code: https://buff.ly/4hEajpj

🔥 How to find and install the latest AI apps from the AI app store 1. go to https://buff.ly/42CnUbU 2. search the app you like 3. go to the bottom settings 4. open the URL 5. press the search bar to install More info: https://buff.ly/3Csqc2J

Retrievers and rankers are a crucial part of optimising RAG. Easier to fine-tune than LLMs. More predictable than prompts. Training data is hard to find, so we offer private and free synthetic data on your own documents! Blog:

Fine-tune ModernBERT for RAG with Synthetic Data

A Blog post by Sara Han Díaz on Hugging Face

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Creating an agentic RAG stack on the Hugging Face Hub - part 1 - retrieval (1/5). 🚀 Web apps and microservices included! Chunk, embed and index documents at a huge scale without overhead. Blog:

Index and retrieve documents for vector search using Sentence Transformers and DuckDB

A Blog post by David Berenstein on Hugging Face

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Shit! 24B is the new small. Mistral drops their new model on Hugging Face! Great performance, and low latency. Model: https://buff.ly/4hwAzBa Code: https://buff.ly/3CEohrF

Deploy a DeepSeek Web App with minimal code! AI Gradio is a Python package that makes it easy for developers to create AI apps powered by various AI providers. Code: https://buff.ly/40BDsde Library: https://buff.ly/3CvOQ2n

No data for fine-tuning retrieval models? We help you generate it! - Load from Hub - Upload your own files - Generate from a prompt Space: https://buff.ly/3Y1S99z Code: https://buff.ly/3PRg4TX