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🗺️ Practical guides overview

This guide will help you with all the practical aspects of setting up an annotation project for training and fine-tuning LLMs using Argilla’s Feedback Task Datasets. It covers everything from defining your task to collecting, organizing, and using the feedback effectively.

🧐 Choose a dataset type

Find the dataset type needed for your project.

🧑‍💻 Create and update a dataset

Methods to create and configure a dataset.

🗂️ Assign records to your team

Workflows to organize your annotation team.

🔎 Filter and query datasets

Obtain a filtered version of your dataset.

✍️ Annotate a dataset

Learn to use Argilla’s UI and its features.

🌊 Simplify annotation with machine feedback workflow

Use things like active learning, weak supervision, semantic search, and job scheduling.

📊 Collect responses and metrics

Collect responses, get metrics, and solve disagreements.

📥 Export a dataset

Export your dataset and save it in the Hugging Face Hub or locally.

🦾 Fine-tune LLMs and other models

Fine-tune an LLM or other models with the feedback collected from Argilla.