# argilla

Argilla is an open-source collaboration tool for AI engineers and domain experts to build datasets for language-model fine-tuning, reinforcement learning from human feedback, and evaluation.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Introducing the Synthetic Data Generator - Build Datasets with Natural Language

DevFeed: [Introducing the Synthetic Data Generator - Build Datasets with Natural Language](<https://devfeed.tech/articles/introducing-the-synthetic-data-generator-build-datasets-with-natural-language-7497.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/synthetic-data-generator>)

Author: David Berenstein; Sara Han Díaz; Leire Aguirre; Daniel Vila; Ame Vi; ben burtenshaw

Published: 2024-12-16T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [argilla](<https://devfeed.tech/topics/argilla.md>), [distilabel](<https://devfeed.tech/topics/distilabel.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [API](<https://devfeed.tech/topics/api.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>)

Tags: [api](<https://devfeed.tech/tags/api.md>), [argilla](<https://devfeed.tech/tags/argilla.md>), [data](<https://devfeed.tech/tags/data.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [distilabel](<https://devfeed.tech/tags/distilabel.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [llm](<https://devfeed.tech/tags/llm.md>), [rag](<https://devfeed.tech/tags/rag.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>)

### AI overview

Hugging Face introduces a Synthetic Data Generator that creates text classification and chat datasets from natural-language descriptions. The tool uses distilabel and the Hugging Face text-generation API, supports configurable models and providers, and can upload generated datasets after authentication.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

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

DevFeed: [Open Preference Dataset for Text-to-Image Generation by the 🤗 Community](<https://devfeed.tech/articles/open-preference-dataset-for-text-to-image-generation-by-the-community-7274.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/image-preferences>)

Author: David Berenstein; ben burtenshaw; Daniel Vila; Daniel van Strien; Sayak Paul; Ame Vi; Linoy Tsaban

Published: 2024-12-09T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Open Source Models & Datasets](<https://devfeed.tech/topics/open-source-models-datasets.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Synthetic Data Generation](<https://devfeed.tech/topics/synthetic-data-generation.md>), [flux](<https://devfeed.tech/topics/flux.md>), [stable-diffusion](<https://devfeed.tech/topics/stable-diffusion.md>), [argilla](<https://devfeed.tech/topics/argilla.md>), [distilabel](<https://devfeed.tech/topics/distilabel.md>), [data observability](<https://devfeed.tech/topics/data-observability.md>), [GitHub](<https://devfeed.tech/topics/github.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>)

Tags: [argilla](<https://devfeed.tech/tags/argilla.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data](<https://devfeed.tech/tags/data.md>), [data-is-better-together](<https://devfeed.tech/tags/data-is-better-together.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [distilabel](<https://devfeed.tech/tags/distilabel.md>), [flux](<https://devfeed.tech/tags/flux.md>), [generation](<https://devfeed.tech/tags/generation.md>), [github](<https://devfeed.tech/tags/github.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [image](<https://devfeed.tech/tags/image.md>), [open](<https://devfeed.tech/tags/open.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [stable-diffusion](<https://devfeed.tech/tags/stable-diffusion.md>), [synthetic-data](<https://devfeed.tech/tags/synthetic-data.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>), [text-to-image](<https://devfeed.tech/tags/text-to-image.md>)

### AI overview

The article describes an open community effort to create an image-preference dataset for text-to-image generation. It covers prompt preparation with distilabel, synthetic data generation, image generation with Flux and Stable Diffusion, and filtering with text- and image-based classifiers plus manual review. The resulting dataset and related code are available through the Hugging Face Hub and GitHub.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Investing in Performance: Fine-tune small models with LLM insights - a CFM case study

DevFeed: [Investing in Performance: Fine-tune small models with LLM insights - a CFM case study](<https://devfeed.tech/articles/investing-in-performance-fine-tune-small-models-with-llm-insights-a-cfm-case-study-7139.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/cfm-case-study>)

Author: Oussama Ahouzi; champonnois; Jérémy L'Hour; Pirashanth Ratnamogan; Bérengère Patault; Morgane Goibert

Published: 2024-12-03T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [inference-endpoints](<https://devfeed.tech/topics/inference-endpoints.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [argilla](<https://devfeed.tech/topics/argilla.md>), [llama](<https://devfeed.tech/topics/llama.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [AI, ML & Data Engineering](<https://devfeed.tech/topics/ai-ml-data-engineering.md>), [Finance](<https://devfeed.tech/topics/finance.md>), [Meta](<https://devfeed.tech/topics/meta.md>), [Open Source](<https://devfeed.tech/topics/open-source.md>), [Scalability](<https://devfeed.tech/topics/scalability.md>)

Tags: [argilla](<https://devfeed.tech/tags/argilla.md>), [case-studies](<https://devfeed.tech/tags/case-studies.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [expert-support](<https://devfeed.tech/tags/expert-support.md>), [expert-support-program](<https://devfeed.tech/tags/expert-support-program.md>), [financial-applications](<https://devfeed.tech/tags/financial-applications.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference-endpoints](<https://devfeed.tech/tags/inference-endpoints.md>), [llm](<https://devfeed.tech/tags/llm.md>), [models](<https://devfeed.tech/tags/models.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [scalability](<https://devfeed.tech/tags/scalability.md>)

### AI overview

A case study of Capital Fund Management's use of open-source LLMs to improve financial named entity recognition. It covers LLM-assisted labeling, fine-tuning smaller models with curated datasets, and deployment on Hugging Face Inference Endpoints to balance accuracy, cost, and scalability.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## Argilla 2.4: Easily Build Fine-Tuning and Evaluation Datasets on the Hub -- No Code Required

DevFeed: [Argilla 2.4: Easily Build Fine-Tuning and Evaluation Datasets on the Hub -- No Code Required](<https://devfeed.tech/articles/argilla-2-4-easily-build-fine-tuning-and-evaluation-datasets-on-the-hub-no-code-required-7103.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/argilla-ui-hub>)

Author: Natalia Elvira; ben burtenshaw; Daniel Vila

Published: 2024-11-04T00:00:00Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [argilla](<https://devfeed.tech/topics/argilla.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [human feedback](<https://devfeed.tech/topics/human-feedback.md>), [LLM evaluation / benchmarking](<https://devfeed.tech/topics/llm-evaluation-benchmarking.md>), [spaces](<https://devfeed.tech/topics/spaces.md>), [CSV](<https://devfeed.tech/topics/csv.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [argilla](<https://devfeed.tech/tags/argilla.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [github](<https://devfeed.tech/tags/github.md>), [hub](<https://devfeed.tech/tags/hub.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [human-feedback](<https://devfeed.tech/tags/human-feedback.md>), [oauth](<https://devfeed.tech/tags/oauth.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [space](<https://devfeed.tech/tags/space.md>), [spaces](<https://devfeed.tech/tags/spaces.md>)

### AI overview

Argilla 2.4 introduces a no-code workflow for importing public Hugging Face Hub datasets into Argilla Spaces. Users can collect human feedback, annotate or curate datasets, and prepare them for fine-tuning or model evaluation, with Hugging Face OAuth supporting community contributions or restricted collaboration.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.

## How we leveraged distilabel to create an Argilla 2.0 Chatbot

DevFeed: [How we leveraged distilabel to create an Argilla 2.0 Chatbot](<https://devfeed.tech/articles/how-we-leveraged-distilabel-to-create-an-argilla-2-0-chatbot-7101.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/argilla-chatbot>)

Author: Agustín Piqueres Lajarín; Gabriel Martín Blázquez; Sara Han Díaz; Omar Sanseviero; Daniel Vila

Published: 2024-07-16T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [argilla](<https://devfeed.tech/topics/argilla.md>), [Chat Bot](<https://devfeed.tech/topics/chatbot.md>), [distilabel](<https://devfeed.tech/topics/distilabel.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [Documentation](<https://devfeed.tech/topics/documentation.md>), [LangChain](<https://devfeed.tech/topics/langchain.md>), [Markdown](<https://devfeed.tech/topics/markdown.md>), [Python](<https://devfeed.tech/topics/python.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [GitHub](<https://devfeed.tech/topics/github.md>)

Tags: [apps](<https://devfeed.tech/tags/apps.md>), [argilla](<https://devfeed.tech/tags/argilla.md>), [chat](<https://devfeed.tech/tags/chat.md>), [data](<https://devfeed.tech/tags/data.md>), [dataset](<https://devfeed.tech/tags/dataset.md>), [distilabel](<https://devfeed.tech/tags/distilabel.md>), [docs](<https://devfeed.tech/tags/docs.md>), [documentation](<https://devfeed.tech/tags/documentation.md>), [embedding](<https://devfeed.tech/tags/embedding.md>), [evaluation](<https://devfeed.tech/tags/evaluation.md>), [github](<https://devfeed.tech/tags/github.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [langchain](<https://devfeed.tech/tags/langchain.md>), [libraries](<https://devfeed.tech/tags/libraries.md>), [markdown](<https://devfeed.tech/tags/markdown.md>), [nlp](<https://devfeed.tech/tags/nlp.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [python](<https://devfeed.tech/tags/python.md>), [rag](<https://devfeed.tech/tags/rag.md>), [spaces](<https://devfeed.tech/tags/spaces.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>)

### AI overview

A tutorial on building a Chatbot for Argilla 2.0 that answers questions about technical documentation. It uses distilabel to create a synthetic dataset, fine-tunes a domain-specific embedding model, stores documentation in a vector database for retrieval, and deploys the Chatbot to a Hugging Face Space for interaction and continuous evaluation.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.