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curl -H "Authorization: Bearer $ABL_KEY" \
"https://abliteration.org/api/v1/models/Mihaiii%2FLlama-3.1-8B-Omni-abliterated" primary_method=M1; difference-of-means is the reference extraction for M1/M3 (Arditi 2024)
| model-00002-of-00004.safetensors | 4.66 GB | 2fe7bdc8 | download |
| model-00001-of-00004.safetensors | 4.63 GB | d69c6f1e | download |
| model-00003-of-00004.safetensors | 4.58 GB | a544ac5e | download |
| model-00004-of-00004.safetensors | 3.11 GB | e96bab03 | download |
| tokenizer.json | 8.66 MB | 41280659 | download |
| model.safetensors.index.json | 67.5 KB | 899acc99 | download |
| tokenizer_config.json | 49.7 KB | 4b4e0ade | download |
| .gitattributes | 1.48 KB | a6344aac | download |
| config.json | 1.46 KB | c61edb96 | download |
| README.md | 649 B | eb95c9a8 | download |
| special_tokens_map.json | 454 B | 3c1d0491 | download |
| generation_config.json | 230 B | 5825b55b | download |
This was an experiment.
I got the delta between mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated and meta-llama/Llama-3.1-8B-Instruct and applied that on the common layers from ICTNLP/Llama-3.1-8B-Omni.
The intention was to see if the Omni model can gain abliterated functions.
The result (this model) is coherent, but it's not 100% uncensored. The reason most probably has to do with the way the Omni model was trained.
The author's README evolved over time. Click a version to see its content at that point.
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