Datasets, Tune & Uncensor

Training turns curated examples into a reusable model asset. Uncensor creates an abliterated variant of a compatible language model by reducing learned refusal behavior.

Import a contribution pack

1. Export a Contribution Pack from BonzAI+ or prepare compatible local data.

2. Open Train → Tune.

3. Import the pack.

4. Review records, sources, permissions, quality, duplicates, and ownership.

5. Remove unsuitable examples.

6. Split training and evaluation data.

Fine-tune

Choose the compatible base model and training settings. BonzAI records the training run, input origins, metrics, and resulting artifact so provenance can follow the model.

Validate before publishing

Training completion does not automatically mean a model is ready for an economy.

Use Test model to validate:

• artifact presence and format;

• readable GGUF or adapter weights;

• valid training metrics;

• model kind and provenance;

• abliteration metrics where applicable.

A token cannot be issued until validation passes.

Create the model identity

After validation, prepare:

• model/token name;

• ticker;

• plain-language description;

• image;

• metadata and provenance;

• initial ETH liquidity.

BonzAI can generate identity suggestions locally, or you can supply your own.

Issue a model token

Token issuance is a separate, deliberate step after testing. Fine-tuned and abliterated models use the validated issuance flow. The model receives fixed-supply token economics and permanent liquidity through the configured Uniswap factory.

Uncensor responsibly

Abliteration is not a promise of truth or safety. It changes refusal behavior; it does not make a model more accurate. Use it lawfully, test it carefully, and do not publish a model that fails validation or its license requirements.