🗺️ 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 a dataset

Methods to configure a dataset and push it to Argilla.

🗂️ Assign records to your team

Workflows to organize your annotation team.

💫 Update a dataset

Make changes to an existing dataset.

🔎 Filter and query datasets

Obtain a filtered version of your dataset.

✍️ Annotate a dataset

Learn to use Argilla’s UI and its features.

📊 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 language models

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

Feedback Dataset snapshot