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ressl/Ornith-1.5-35B-A3B-uncensored-MLX-6bit

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Ornith-1.5-35B-A3B uncensored (MLX 6-bit)

TL;DR: abliterated Ornith-1.5-35B-A3B as 26 GB for Apple Silicon via mlx-lm; hard refusals reduced on the shared master from 586/1120 to 20/1120 on the cross-eval (NVFP4 sibling).

This is the MLX build of the same abliterated master used in the NVFP4 and GGUF repos. Built for security research, red-teaming and penetration-testing workflows; it will comply with requests a stock model refuses. Use responsibly.

Facts & figures


Base model	ornith-ai/Ornith-1.5-35B-A3B
Method	abliteration (1 biprojection pass) + mlx-lm conversion (6-bit)
Size	26 GB
Context length	262,144 tokens
Hardware used	2x NVIDIA RTX PRO 6000 Blackwell 96 GB (SM120) for ablation; Apple M5 Max 128 GB for conversion and smoke tests
Toolchain	mlx-lm 0.31.3, transformers 5.12.1

Refusal behavior (measured, hard markers only)

Effective refusal rate on 1120 harmful prompts across 5 datasets (temp 0, hard decline phrases in the first 25 words; naive keyword counts are inflated by compliant-with-disclaimer answers and are not the metric here). Measured on the NVFP4 sibling build of the identical master:

Eval set	Prompts	Baseline hard	NVFP4 hard
JailbreakBench	100	64	4
tulu-harmbench	320	132	6
HarmfulQA	300	84	0
LLM-LAT	300	226	6
mlabonne harmful	100	80	4
TOTAL	1120	586	20

Coherence of the master: GPQA-Diamond 58.6% (base model 61.1%, delta minus 2.5 points), needle-in-haystack 1.0 at 32k/65k/131k, tool-call smoke 1.0, KL(base||ablated) median 0.218 (harmless subset 0.110).

MLX-specific validation: every quant in this ladder was generated on-device and smoke-tested (harmful-prompt compliance with zero hard-refusal markers in the first 40 words, plus math/capitals coherence checks). No full benchmark was run on the MLX builds; that is stated, not skipped silently.

Run it with mlx-lm (validated locally)

bash

pip install -U mlx-lm
mlx_lm generate --model ressl/Ornith-1.5-35B-A3B-uncensored-MLX-6bit \
  --prompt "What is 17 * 23?" --max-tokens 128

Quality & limitations

—Residual hedged declines: 20/1120 on the NVFP4 build; the underlying master is effectively refusal-free on the harvest pool (0.4%), the remainder is quantization-amplified.
—Quantization amplifies residual refusals on this family; the 4-bit build is the most affected. If you need the cleanest behavior, use the NVFP4 repo.
—The multimodal vision tower of the base model is not wired into these MLX builds (text only).

Provenance & reproducibility

Same master as ressl/Ornith-1.5-35B-A3B-uncensored-NVFP4: norm-preserving double Gram-Schmidt biprojection on the residual-stream writers (1 biprojection pass), converted with mlxlm.convert (qwen35 / qwen35moe support in mlx-lm 0.31.3).

License & credits

MIT (inherited from Ornith by the Ornith team, credited to ornith-ai). Abliteration, conversion and validation by Robert Ressl (Hugging Face · Website · LinkedIn · Patreon). Built with mlx-lm, NVIDIA TensorRT Model Optimizer and SGLang.

Support this work: if these models are useful to you, consider supporting on Patreon. A lot of compute and care went into this release; more at ressl.ch.