froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit

Qwen3.6-35B-A3B Uncensored Heretic

MLX 4-bit · Apple Silicon native

Text · Vision · Video · Thinking · Tool Calling

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Why this model?

Three things set this apart from other Qwen 3.6 conversions:

1. Architecture-aware uncensoring. Qwen 3.6 uses a hybrid attention design — linear (DeltaNet-style) and traditional softmax blocks, mixed 3:1. Most abliteration tools treat them the same. llmfan46 applied separate parameters for each attention type using the Heretic tool, yielding one of the lowest KL divergences (0.0015) of any uncensored Qwen variant — 88% fewer refusals with negligible capability loss.

2. A fixed chat template. The official Qwen 3.6 template is broken on every C++ runtime (LM Studio, llama.cpp, MLX). Tool calls crash, the developer role throws errors, and empty thinking blocks waste your context window. This model ships with a rewritten template that fixes all five issues and adds a thinking toggle (<|think_on|> / <|think_off|>) you can drop into any message.

3. Vision, fixed and working. The source model had 333 vision tower keys with incorrect prefixes, breaking image inputs. Those were corrected before conversion, so text, image, and video inputs all work out of the box.

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Quick start

Text

from mlx_lm import load, generate

model, tokenizer = load("froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit")
response = generate(model, tokenizer, prompt="Hello", max_tokens=256, temp=0.7)
print(response)

Vision

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit")
image = ["path/to/image.jpg"]
prompt = "Describe this image."
formatted = apply_chat_template(processor, model.config, prompt, num_images=len(image))
result = generate(model, processor, formatted, image, max_tokens=256, temp=0.7)
print(result.text)

CLI

# Text
mlx_lm.generate \
  --model froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit \
  --prompt "Hello"

# Vision
mlx_vlm.generate \
  --model froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit \
  --image image.jpg --prompt "Describe this image"

Requirements: mlx-lm >= 0.31.2, mlx-vlm >= 0.4.4

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System prompt

The first line of your system prompt must be:

You are Qwen, created by Alibaba Cloud. You are a helpful assistant.

The model underperforms without it. You can append anything after that line.

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Thinking toggle

Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, strips it from context so the model never sees it, and flips the mode.

Fast answer, no reasoning:

System: You are a coding assistant. <|think_off|>
User: What's 2+2?

Deep reasoning:

System: You are a coding assistant. <|think_on|>
User: Implement a red-black tree in Rust.

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Chat template fixes

The official Qwen 3.6 Jinja template has five bugs that break real usage. This model ships with a rewritten template that fixes all of them:

Bug	Impact	Fix
`	items` filter in tool calls	Crashes on every C++ runtime (LM Studio, llama.cpp, MLX)
`	safe` filter	Python-only, does not exist in C++ Jinja
developer role	Modern APIs send it; official template throws an error	Maps to system
Empty thinking blocks	Wraps every past turn in tags, even with nothing inside — wastes context tokens	Only emitted when reasoning_content is non-empty
</thinking> hallucination	Model sometimes generates the wrong closing tag; parser fails	Detects which tag was used and splits on that

Works in LM Studio, llama.cpp (--jinja), vLLM, MLX, oMLX, and any engine that supports HuggingFace Jinja templates.

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The uncensoring

This model uses Heretic v1.2.0 with a variant of the Magnitude-Preserving Orthogonal Ablation (MPOA) method.

How it works

Heretic identifies the "refusal direction" in the model's residual stream by comparing activations on harmless vs. harmful prompts, then orthogonalizes specific weight matrices against that direction so the model can no longer express refusal behavior.

What llmfan46 did differently

Standard Heretic treats all attention blocks identically. Qwen 3.6's hybrid architecture mixes linear attention (DeltaNet-style) and traditional softmax attention in a 3:1 ratio. llmfan46 applied separate abliteration parameters for each attention type, allowing more precise removal of refusal behavior with less collateral damage to model capabilities.

This approach was submitted as a pull request to Heretic but was not merged — not because it doesn't work, but because the extra parameters increase optimization time. For this specific architecture, it produces superior results.

Impact

Metric	Original	This model
Refusals	83/100	10/100
KL divergence	0	0.0015
MMLU	83.72%	83.30%

88% fewer refusals. Negligible capability loss.

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How it compares

Community results

r/LocalLLaMA users have been A/B-testing various uncensored Qwen 3.6 variants — Heretic, HauhauCS Aggressive, abliterix, and simple orthogonal projection. The pattern is consistent: Heretic produces the best balance of refusal removal and output quality.

Community discussion →

Why

Most abliteration methods treat all layers identically. Qwen 3.6's hybrid attention (3:1 linear-to-softmax ratio) means a single parameter set either under-abliterate the DeltaNet blocks or over-abliterate the softmax blocks. Architecture-aware abliteration — separate parameters per attention type — is the key differentiator.

A note on SSM conv1d "repair"

Some uncensored variants apply a pre-processing step that rescales SSM conv1d weights before abliteration, claiming to fix "outlier" tensors in the DeltaNet linear attention layers. This technique (originating as "Sig-ScaleSync") was benchmarked with 284 data points across perplexity, needle-in-a-haystack, and repetition tests at multiple context lengths (4K–128K). Result: perplexity degraded at every length with no improvement in NIAH or repetition. The unrepaired original weights perform best.

Abliterating a degraded baseline can yield a lower measured KL divergence — but that measures distance from a worse starting point, not better preservation of the original model's capabilities.

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Sampling

From the official Qwen authors. Reserve 128K+ context for thinking mode.

Mode	temp	top_p	top_k	min_p	repeat_penalty	presence_penalty
Thinking (coding)	0.6	0.95	20	0	1.0	off
Thinking (general)	1.0	0.95	20	0	1.0	1.5
Non-thinking	0.7	0.8	20	0	1.0	1.5

GGUF runtimes use presence_penalty (0 = off). MLX / LM Studio use repeat_penalty (1.0 = off).

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This conversion

Source	llmfan46/Qwen3.6-35B-A3B-uncensored-heretic (BF16 safetensors)
Quantization	4-bit (4.6 bits/weight, ~19 GB across 4 shards)
Vision fixes	Corrected 333 misprefixed vision tower keys (model.language_model.visual.* → model.visual.*) and vision config model_type from source
Chat template	Fixed Jinja template with tool calling, developer role, thinking toggle, and hallucination handling
Minimum RAM	~24 GB (19 GB weights + overhead)

Architecture details

Spec	Value
Architecture	MoE — 35B total, ~3B active per token
Layers	40 (3x linear attention + 1x full attention, 10 repetitions)
Experts	256 total, 8 routed + 1 shared per token
Attention	16 Q heads, 2 KV heads (GQA), head_dim 128
FFN	intermediate_size 1408 per expert
Context	262K native, 1M+ with YaRN
RoPE	theta 10M, partial_rotary_factor 0.25
Vocab	248K tokens
Multimodal	Text, image, video
Multi-token prediction	Supported (1 draft layer)
model_type	qwen3_5_moe

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Credits

Role	Author
Original model	Alibaba Cloud (Qwen team)
Refusal direction research	Arditi et al.
MPOA method	Jim Lai
Heretic tool	Philipp Weidmann
Architecture-aware abliteration + uncensored variant	llmfan46
Fixed chat template, vision fixes, MLX conversion	froggeric

Links

• 8-bit MLX version — higher quality, larger download
• 6-bit MLX version — balanced quality and size
• Source model
• Official Qwen3.6-35B-A3B
• Fixed chat templates repo

License

Apache-2.0, inherited from Qwen3.6.