
Unsloth for memory-conscious model tuning
Fine-tune supported model architectures using optimized training paths and documented recipes.
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Fine-tune supported model architectures using optimized training paths and documented recipes.

Describe model fine-tuning runs in configuration files and reuse the recipes across experiments.

Train supported language models with configurable datasets and tuning methods.

Build model evaluations with datasets, solvers, tools and scorers.

Study attacks and defenses against machine-learning models using controlled experiments.

Identify likely personal information in text and apply redaction or replacement rules.
Each page introduces the project, explains its capabilities and highlights practical takeaways. Follow the repository link for the latest documentation and setup instructions.
Sign in, choose Add Repo and paste the main URL of a public AI repository, not a file or issue link. Franklin AI uses the project’s README to build and publish a page and links your contribution to your profile. Each account can add up to 20 repositories a day.
Private repositories cannot be added. If the README is too short for an automatic page, or the project is already being reviewed, the repository is saved in your submissions with the reason for an admin to complete, and you are credited when it is published.
Its existing page stays as it is. Adding it again links that page to your profile instead of creating a second one.
Use the Category filter. Each card shows its categories, such as AI Agents or AI Development, and the repository’s owner and name.