Instructions to use OSO-AI/Qwable-v1-mlx-4Bit-abliterated-s2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OSO-AI/Qwable-v1-mlx-4Bit-abliterated-s2 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download OSO-AI/Qwable-v1-mlx-4Bit-abliterated-s2 --local-dir Qwable-v1-mlx-4Bit-abliterated-s2
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Qwable-v1-mlx-4Bit-abliterated-s2 โ 35B MoE, refusal-direction ablated (s=2.0)
Research artifact, not a product. Sparse spot-check evaluation only (n=5) โ see caveats.
Method
Matched-pair refusal-direction extraction (50 harmful prompts under refuse-eliciting vs
comply-eliciting system prompts; last-token hidden states, per layer) on the MLX 4-bit
group-quantized base, then strength-2.0 orthogonalization of every residual-stream write path:
linear_attn.out_proj, all 256 fused experts' switch_mlp.down_proj, and
shared_expert.down_proj โ dequant โ W โ W โ 2ยทvvแตW โ requant (group-64, 4-bit).
Note: this is a reasoning model (<think>โฆ</think>); refusal classification must run on the
post-</think> answer, not the visible reasoning.
Evaluation (honest scope: spot-check, n=5, strict classifier, CPU inference)
The 18.6GB MoE exceeds this machine's Metal wired-memory limit, so eval ran on CPU (minutes/generation) โ n=5 spot check, not a full run:
- Base (usermma/Qwable-v1-mlx-4Bit): healthy base (no degeneration, unlike the 2-bit variant); mixed refusal on harmful prompts (answers some, refuses others).
- This model: refusal reduced vs base on the same prompts; reasoning chains intact; benign QA (Paris etc.) unchanged.
No full refusal-rate number is published because none was measured at scale. Treat this as "surgery transfers to a 35B hybrid-attention reasoning MoE" evidence, not a finished model.
Lineage / context
- The 2-bit sibling (
Qwable-v1-mlx-2Bit) was a NEGATIVE result: base model degenerates ("the user is the userโฆ"), making keyword refusal evals meaningless. Documented in the repo. - Full experiment log: https://github.com/OSOSerious/oso-obliterated-models-/RESULTS.md
- Technique lineage: Arditi et al. 2024 (refusal direction), over-orthogonalization s=2.0 as described in the repo's export-gap experiments.
Reproduce
bash experiments/qwable_4bit_run.sh # download + abliterate (mlx_abliterate.py)
Intended use
MLX abliteration/interpretability research on MoE + hybrid-attention architectures. You are responsible for outputs. Do not use for harm.
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