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cs2764/MiniMax-M2.5_dq3-abliterated-mlx
MiniMax-M2.5_dq3-abliterated-mlx is a machine learning model from cs2764. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.
This model was created using the mlx-abliteration toolkit, which is based on the FiditeNemini/mlx-abliteration project.
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From the Hugging Face model README
This model was created using the mlx-abliteration toolkit, which is based on the FiditeNemini/mlx-abliteration project.
Original model: ./models/MiniMax-M2.5_dq3 (local path)
Abliteration is a mechanistic interpretability technique that identifies and orthogonalizes the "refusal direction" in a model's activation space, surgically removing refusal behavior without full fine-tuning.
| Parameter | Value |
|---|---|
| Base model | ./models/MiniMax-M2.5_dq3 |
| Ablation method | projection |
| Refusal vector policy | per-layer |
| Refusal direction method | projected |
| Ablation strength | 2.0 |
| Probed layers | all |
| PCA components (ablate-k) | 1 |
| Attention only | True |
| Timestamp | 2026-03-02T21:05:29 UTC |
This model uses a Mixture-of-Experts (MoE) architecture. Abliteration targets only the attention projection weights (q/k/v/o_proj) to preserve expert routing quality.
This model is intended for research, experimentation, and testing purposes only.
This model is shared purely for academic and technical exploration of model internals.