distilabel-internal-testing (distilabel-internal-testing)

burtenshaw

posted an update 1 day ago

Post

363

Hey 👋

I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.

OSAIResearchCommunity/README#2

burtenshaw

posted an update 1 day ago

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1013

📣 Teachers and Students! Here's a handy quiz app if you're preparing your own study material.

TLDR, It's a quiz that uses a dataset to make questions and save answers

Here's how it works:

- make a dataset of multiple choice questions
- duplicate the space add set the dataset repo
- log in and do the quiz
- submit the questions to create a new dataset

I made this to get ready for the agents course, but I hope it's useful for you projects too!

quiz app burtenshaw/dataset_quiz

dataset with questions burtenshaw/exam_questions

agents course we're working on https://huggingface.co./agents-course

burtenshaw

posted an update 2 days ago

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1845

AI was built on side projects!

gabrielmbmb

updated a dataset 3 days ago

distilabel-internal-testing/instructions

Viewer • Updated 3 days ago • 10 • 41

gabrielmbmb

published a dataset 3 days ago

distilabel-internal-testing/instructions

Viewer • Updated 3 days ago • 10 • 41

burtenshaw

posted an update 4 days ago

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3313

🚧 Work in Progress! 🚧

👷‍♀️ We're working hard on getting the official agents course ready for the 50,000 students that have signed up.

If you want to contribute to the discussion, I started these community posts. Looking forward to hearing from you:

- smolagents unit in the agents course - agents-course/README#7
- LlamaIndex Unit in the agents course - agents-course/README#6
- LangChain and LangGraph unit in the agents course - agents-course/README#5
- Real world use cases in the agents course - agents-course/README#8

davidberenstein1957

posted an update 5 days ago

Post

1804

The RAG's in the bag!

You can now use the Synthetic Data Generator with your own domain-specific seed data to generate a dataset for fine-tuning retrieval or reranking models.

GitHub: https://buff.ly/49IDSmd
Spaces: https://buff.ly/3Y1S99z
Blog: https://huggingface.co./blog/sdiazlor/fine-tune-modernbert-for-rag-with-synthetic-data

1 reply

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davidberenstein1957

posted an update 9 days ago

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1213

You can now use the "Synthetic Data Generator" at a much larger scale with your preferred inference engine: Ollama, vLLM, TGI, and serverless inference! 🔥

Install, configure, launch!

Space: argilla/synthetic-data-generator
Examples: https://github.com/argilla-io/synthetic-data-generator/tree/main/examples

burtenshaw

posted an update 9 days ago

Post

1282

Playing with agents, and I reckon Gradio spaces make the perfect agent tools! So I wrote this guide using Gradio and smolagents:

https://huggingface.co./blog/burtenshaw/gradio-spaces-agent-tools

burtenshaw

posted an update 10 days ago

Post

37513

We’re launching a FREE and CERTIFIED course on Agents!

We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.

Here's what you'll learn:

- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience

This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.

Enroll today and start building the next generation of AI agent applications!

https://bit.ly/hf-learn-agents

22 replies

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davidberenstein1957

posted an update 11 days ago

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2093

🔦 What? The Hub as a vector search backend!

code: https://gist.github.com/davidberenstein1957/f0157a471ec59d9dd44ae6957f1d52ec
build on DuckDB: https://huggingface.co./docs/hub/en/datasets-duckdb

davidberenstein1957

updated a model 16 days ago

distilabel-internal-testing/tiny-random-mistral

Text Generation • Updated 16 days ago • 2.37k

davidberenstein1957

posted an update 22 days ago

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1938

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

1 reply

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davidberenstein1957

posted an update 27 days ago

Post

1999

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

burtenshaw

posted an update about 1 month ago

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2965

People are flexing their end of year stats, so I made this app to show hub stats in a tidy design!

Thanks @Ameeeee and @jfcalvo for the feature from Argilla!
burtenshaw/recap

1 reply

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davidberenstein1957

posted an update about 1 month ago

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1359

🐇 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

davidberenstein1957

posted an update about 1 month ago

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4204

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

4 replies

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burtenshaw

posted an update about 2 months ago

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2470

Quick update from week 1 of smol course. The community is taking the driving seat and using the material for their own projects. If you want to do the same, join in!

- we have ongoing translation projects in Korean, Vietnamese, Portuguese, and Spanish
- 3 chapters are ready for students. On topics like, instruction tuning, preference alignment, and parameter efficient fine tuning
- 3 chapters are in progress on evaluation, vision language models, and synthetic data.
- around 780 people have forked the repo to use it for learning, teaching, sharing.

⏭️ Next step is to support people that want to use the course for teaching, content creation, internal knowledge sharing, or anything. If you're into this. Drop an issue or PR

REPO: https://buff.ly/3ZCMKX2
discord channel: https://buff.ly/4f9F8jA

davidberenstein1957

posted an update about 2 months ago

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2082

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

dvilasuero

authored a paper about 2 months ago

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

Paper • 2412.03304 • Published Dec 4, 2024 • 17

distilabel-internal-testing

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distilabel-internal-testing/instructions

distilabel-internal-testing/instructions

distilabel-internal-testing/tiny-random-mistral

Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation

AI & ML interests

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