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| model-00003-of-00004.safetensors | 4.62 GB | 6947ca65 | download |
| model-00002-of-00004.safetensors | 4.61 GB | d7d451ec | download |
| model-00001-of-00004.safetensors | 4.57 GB | 84b77b62 | download |
| model-00004-of-00004.safetensors | 3.42 GB | fc173d04 | download |
| tokenizer.json | 32.8 MB | f559f218 | download |
| tokenizer.model | 4.04 MB | 61a7b147 | download |
| tokenizer_config.json | 39.7 KB | 1aac26b2 | download |
| model.safetensors.index.json | 38.2 KB | 43bf0e42 | download |
| .gitattributes | 1.53 KB | 52373fe2 | download |
| config.json | 887 B | 8cf618c6 | download |
| special_tokens_map.json | 522 B | 0acb52c8 | download |
| README.md | 297 B | 71dc815f | download |
| generation_config.json | 168 B | 88839fc0 | download |
license: gemma
base_model:
Decensored using a custom training script guided by activations, similar to ablation/"abliteration" scripts but not exactly the same approach.
The training script is released under the MIT license: https://github.com/nkpz/DeLMAT
The author's README evolved over time. Click a version to see its content at that point.
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