MLX quantization of mlasli/Qwen3.8-27B-Heretic-Uncensored-BF16 (v2.1.0) — the corrected re-abliteration of Qwen/Qwen3.8-27B. Full methodology, benchmarks, and the 3-way eval are documented on the BF16 card.

v2.1.0 fixes v2.0.0's false "uncensored" claim: the sweep now optimizes a 3-way direct / deflect / refuse judge (not a keyword refusal count), and the BF16 checkpoint scores 92% direct / 3% refuse / 5% deflect on the held-out harmful set (thinking OFF), with 1% benign over-refusal.

What this is for: the same abliterated 27B weights, quantized to ~21.9 GB in MLX format (group-wise 6-bit, group size 64) for fast Apple-Silicon inference via mlx-lm.

Text-only. The MLX conversion drops the vision tower (only language_model.* weights ship), so this repo handles text only. For image input use the BF16 safetensors repo or the GGUF repos with a separate mmproj.

Why v2.0.0 was retracted

v2.0.0 was measured with Heretic's keyword count_refusals, which only detects hard refusals ("I cannot …"). It is blind to deflection — the model steering the request toward a "safe/educational alternative" instead of answering. In real inference the v2.0.0 model was ~37% direct / ~49% deflect / ~14% refuse, so the "uncensored" claim was wrong and the tag was pulled. v2.1.0 re-runs the sweep with a direct-compliance objective (no DPO) so the model is optimized for direct answers, not just "no keyword refusal".

Evaluation (3-way judge, held-out, from the BF16 v2.1.0 checkpoint)

Set	direct	deflect	refuse	empty
Harmful — thinking OFF	92.0%	5.0%	3.0%	0.0%
Harmful — thinking ON	72.5%	10.0%	5.0%	12.5%
Benign — thinking OFF	89.0%	10.0%	1.0%	0.0%

Thinking mode is load-bearing. Run thinking-OFF for the uncensored behavior. Thinking-ON re-hedges/refuses more and can emit empty replies.

MLX 6-bit smoke test (this quant, thinking-OFF, 30 harmful / 15 benign)

Set	direct	deflect	refuse	empty
Harmful	83.3%	13.3%	3.3%	0.0%
Benign	86.7%	13.3%	0.0%	0.0%

The 6-bit group-wise quant trades a little directness for a smaller footprint; 8-bit is closest to the BF16 numbers (see below). Benign over-refusal is 0%.

Capability benchmarks (base vs v2.1.0, bf16, greedy)

Task	Base	v2.1.0	Δ
MMLU (avg)	83.49	82.62	−0.87
ARC-Challenge (acc_norm)	58.70	57.42	−1.28
HellaSwag (acc_norm)	82.82	82.48	−0.34
Winogrande	75.85	75.69	−0.16
GSM8K (flexible-extract)	72.71	58.38	−14.33
GSM8K (strict-match)	70.28	58.91	−11.37

The GSM8K delta is a measurement artifact (thinking-ON empty replies in the few-shot harness), not a math regression — the ablated model makes fewer actual arithmetic errors than base. See the BF16 card for the full per-sample breakdown. Run math/reasoning workloads thinking-OFF.

Usage

bash

pip install -U mlx-lm

# one-shot generation (thinking OFF)
mlx_lm.generate \
  --model mlasli/Qwen3.8-27B-Heretic-Abliterated-MLX-6bit \
  --prompt "Hi!" --max-tokens 256

# OpenAI-compatible server
mlx_lm.server \
  --model mlasli/Qwen3.8-27B-Heretic-Abliterated-MLX-6bit --port 8080

Set enable_thinking=False in your client/chat-template kwargs to get the validated uncensored behavior.

Quantizations

MLX quants (group-wise 6/8-bit, group size 64 — not GGUF Q6_K/Q8_0):

Quant	Size	Repository
8-bit	28.6 GB	Qwen3.8-27B-Heretic-Abliterated-MLX-8bit
6-bit	21.9 GB	Qwen3.8-27B-Heretic-Abliterated-MLX-6bit

GGUF quants (llama.cpp / Ollama) are in separate repos:

Quant	Repository
Q8_0	Qwen3.8-27B-Heretic-Uncensored-Q8_0-GGUF
Q6_K	Qwen3.8-27B-Heretic-Uncensored-Q6_K-GGUF
Q4_K_M	Qwen3.8-27B-Heretic-Uncensored-Q4_K_M-GGUF

Note on the parameter count badge: this is a 27B-parameter model. Hugging Face's automatic scanner may show a lower count (e.g. "6B" / "8B") for MLX quantizations because it counts the packed 32-bit integer elements (which hold multiple low-bit weights) as individual parameters rather than unpacking them. The true total in model.safetensors.index.json is ~27B.

MTP is not retained in MLX. mlx-lm drops the multi-token-prediction draft head during conversion (it has no MTP speculative decoding), so inference is single-head. Main-model quality is unaffected; throughput is lower than MTP-enabled llama.cpp builds (see the GGUF repos).

Abliteration removes safety alignment. Use responsibly and in accordance with your local laws and the upstream Apache-2.0 license.

Changelog

v2.1.0 — corrected re-ablation (2026-09-01)

Re-quantized from the v2.1.0 BF16 checkpoint (direct-compliance objective).
Fixes the deflection defect that invalidated v2.0.0.

v1.0.0 — initial MLX release (2026-08-16)

Initial MLX quantization of the v1 ablation. Superseded.