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Cataloged from froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit
All models by froggeric →MLX 4-bit · Apple Silicon native
Text · Vision · Video · Thinking · Tool Calling
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 [blocked] 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.
Requirements: mlx-lm >= 0.31.2, mlx-vlm >= 0.4.4
The first line of your system prompt must be:
The model underperforms without it. You can append anything after that line.
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:
Deep reasoning:
The official Qwen 3.6 Jinja template has five bugs that break real usage. This model ships with a rewritten template [blocked] that fixes all of them:
Works in LM Studio, llama.cpp (--jinja), vLLM, MLX, oMLX, and any engine that supports HuggingFace Jinja templates.
This model uses Heretic v1.2.0 with a variant of the Magnitude-Preserving Orthogonal Ablation (MPOA) method.
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.
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.
88% fewer refusals. Negligible capability loss.
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.
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.
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.
From the official Qwen authors. Reserve 128K+ context for thinking mode.
GGUF runtimes use presence_penalty (0 = off). MLX / LM Studio use repeat_penalty (1.0 = off).
Apache-2.0, inherited from Qwen3.6.
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)from mlx_vlm import load, generatefrom 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)# Textmlx_lm.generate \ --model froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit \ --prompt "Hello"
# Visionmlx_vlm.generate \ --model froggeric/Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit \ --image image.jpg --prompt "Describe this image"You are Qwen, created by Alibaba Cloud. You are a helpful assistant.System: You are a coding assistant. <|think_off|>User: What's 2+2?System: You are a coding assistant. <|think_on|>User: Implement a red-black tree in Rust.