---

license: apache-2.0

license_link: https://huggingface.co/Qwen/Qwen3.6-35B-A3B/blob/main/LICENSE

library_name: gguf

base_model: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF

base_model_relation: quantized

model_name: Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF

model_creator: Qwen

model_type: qwen3

quantized_by: deucebucket

pipeline_tag: image-text-to-text

tags:

- GGUF

- qwen3

- qwen

- quantized

- cerebellum

- imatrix

- moe

- mixed-precision

- 3-bit

- heretic

- uncensored

- abliterated

model-index:

• name: Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF

results:

- task:

name: Text Generation

type: text-generation

dataset:

name: AI2 Reasoning Challenge

type: ai2_arc

config: ARC-Challenge

split: test

metrics:

- name: normalized accuracy

type: acc_norm

value: 0.9548

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: HellaSwag

type: hellaswag

split: validation

metrics:

- name: normalized accuracy

type: acc_norm

value: 0.9178

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: MMLU-Redux

type: cais/mmlu

config: all

split: test

metrics:

- name: accuracy

type: acc

value: 0.7542

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: HumanEval+ (pass@1)

type: openai_humaneval

split: test

metrics:

- name: pass@1

type: pass@1

value: 0.6463

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF/tree/main/benchmark_results

- task:

name: Text Generation

type: text-generation

dataset:

name: WikiText-2 Perplexity

type: wikitext

config: wikitext-2-raw-v1

split: test

metrics:

- name: perplexity

type: perplexity

value: 7.157

source:

name: Local audited benchmark run (RTX 3090, llama.cpp)

url: https://huggingface.co/deucebucket/Qwen3.6-35B-A3B-Heretic-Cerebellum-GGUF/tree/main/benchmark_results

---

<p align="center">

<img src="cerebellum_banner.png" alt="Cerebellum" width="640">

</p>

Qwen 3.6 35B-A3B Heretic — Cerebellum GGUF

Sensitivity-guided mixed-precision quantization of

llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF,

which is itself a decensored variant of

Qwen/Qwen3.6-35B-A3B

produced by llmfan46 using Heretic v1.2.0.

All future Heretic versions of this build will live in this repository.

Version identifiers appear only in filenames, not in the repo name.

Files

| File | Size | Description |

|------|------|-------------|

| Qwen3.6-35B-A3B-Heretic-Cerebellum-v1-Q3_K_M.gguf | 11.96 GB (11,955,468,384 bytes) | Cerebellum v3 recipe — recommended |

| Qwen3.6-35B-A3B-uncensored-heretic-mmproj-BF16.gguf | ~858 MB | Vision projector, passed through unmodified from llmfan46's repo |

The vision projector is required for multimodal (image/video) use.

It is identical to the file distributed by llmfan46 and is included here

for single-repo convenience only.

Provenance

1. Base architecture: Qwen/Qwen3.6-35B-A3B — Qwen Team (Apache-2.0)
2. Heretic variant: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF — llmfan46.

The BF16 GGUF from that repository was used as the direct quantization source.

llmfan46 applied Heretic v1.2.0 with the Magnitude-Preserving Orthogonal

Ablation (MPOA) method, targeting attn.o_proj, attn.out_proj, and

mlp.down_proj. Their reported result: 0.0015 KL divergence from base,

10/100 refusals vs 83/100 on the original model.

1. Quantization: Cerebellum v3 recipe transferred verbatim from the stock

deucebucket/Qwen3.6-35B-A3B-Cerebellum-GGUF

build — same 360-entry tensor-type override file, same Unsloth coder imatrix.

Benchmarks

Benchmarks run on these GGUF files directly using llama.cpp on RTX 3090.

All numbers are audited; every failed answer was manually verified as a genuine

model error — audit reports are in benchmark_results/AUDIT_*.md.

Full per-question detail (summary JSON, samples JSONL, EvalPlus eval JSON,

adversarial audit reports) is in benchmark_results/ in this repository.

Heretic Cerebellum v1 (11.96 GB) vs baselines

| Benchmark | Heretic Cerebellum v1 (11.96 GB) | Stock Cerebellum v3 (11.1 GB) | Uniform Q3_K_M baseline (15.6 GB) | Notes |

|-----------|:---:|:---:|:---:|---|

| Wiki PPL (ctx 2048, 32 chunks) | 7.157 ± 0.103 | 7.099 ± 0.102 | — | RTX 3090, identical invocation |

| ARC-Challenge | 95.48% (1172 q) | 95.82% | 96.10% | 25-shot |

| HellaSwag | 91.78% (10042 q) | 92.28% | 91.50% | 10-shot |

| MMLU-Redux | 75.42% (2400 q) | 75.00% | 74.12% | 5-shot |

| HumanEval base | 68.29% (164 problems) | 70.73% | — | pass@1, evalplus |

| HumanEval+ | 64.63% | 65.24% | 56.71% | pass@1, evalplus |

| Vision smoke | 100% (24/24) | 100% (36 images) | — | basic image description |

| RealWorldQA | 76.0% (n=50) | ~78% | — | single-question granularity ±2% |

Stock Cerebellum v3 is the same tensor allocation applied to the non-heretic base.

Uniform Q3_K_M baseline is the stock (non-heretic) model at 15.6 GB — the

standard comparison point for showing what mixed-precision buys at reduced size.

Head-to-head: same weights, uniform quant

llmfan46's own uniform Q3_K_M of the identical heretic weights (16.87 GB) was

benchmarked on the identical harness, same night, same protocol.

| Metric | Heretic Cerebellum v1 (11.96 GB) | Uniform Q3_K_M (16.87 GB) |

|--------|:---:|:---:|

| Wiki PPL (ctx 2048, 32 chunks) | 7.157 ± 0.103 | 7.220 ± 0.106 |

| ARC-Challenge | 95.48% | 95.56% |

| HellaSwag | 91.78% | 91.92% |

| MMLU-Redux | 75.42% | 74.88% |

| HumanEval base | 68.29% | 65.24% |

| HumanEval+ | 64.63% | 57.93% |

The Cerebellum allocation is 29% smaller and scores equal-or-better on PPL,

MMLU and HumanEval+ (both runs' per-question artifacts in benchmark_results_uniform/).

Heretic Abliteration Details (from llmfan46)

The following parameters are as reported in llmfan46's model card and are

reproduced here for downstream reference.

| Parameter | Value |

|-----------|-------|

| direction_index | 19.93 |

| attn.out_proj.max_weight | 1.49 |

| attn.out_proj.max_weight_position | 23.45 |

| attn.out_proj.min_weight | 1.08 |

| attn.out_proj.min_weight_distance | 16.54 |

| mlp.down_proj.max_weight | 1.46 |

| mlp.down_proj.max_weight_position | 28.05 |

| mlp.down_proj.min_weight | 1.27 |

| mlp.down_proj.min_weight_distance | 18.79 |

| attn.o_proj.max_weight | 1.47 |

| attn.o_proj.max_weight_position | 24.35 |

| attn.o_proj.min_weight | 0.07 |

| attn.o_proj.min_weight_distance | 22.58 |

Targeted components: attn.o_proj, attn.out_proj, mlp.down_proj.

Tool: Heretic v1.2.0,

method: Magnitude-Preserving Orthogonal Ablation (MPOA)

(reference).

Cerebellum v3 Tensor Allocation

Same allocation as the stock build. Listed here for reference.

| Group | Precision | Rationale |

|-------|-----------|-----------|

| attn_qkv | Q3_K_M | Critical for vision and attention routing |

| ssm_out | Q3_K_M | Most sensitive tensor per ablation (+0.24 PPL) |

| ffn_gate_exps | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| ffn_up_exps | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| ffn_down_exps | Q2_K | Acceptable loss for size savings |

| ffn_gate_shexp | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| ffn_up_shexp | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| ffn_down_shexp | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| attn_gate | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

| ssm_alpha, ssm_beta | Q2_K | Q2_K regularization outperforms Q3_K_M in reverse ablation |

Protected: all norms (F32), SSM state parameters (F32), router tensors (default).

6 of 10 groups perform at least as well at Q2_K as at Q3_K_M in reverse

ablation — imatrix-guided Q2_K acts as regularization on gate, mixing, and

shared-expert weights for this architecture.

Perplexity Note

Wiki PPL for the Heretic build (7.157) is 0.058 higher than the stock

Cerebellum v3 (7.099). The difference is within the measurement uncertainty

(overlapping ±0.1 error bars) and reflects the small distributional shift

introduced by abliteration rather than quantization quality. Both builds

used the same wikitext-test.txt corpus, ctx 2048, 32 chunks, RTX 3090.

Measured launch (RTX 3090, llama.cpp)

Measured 2026-06-13 on a single RTX 3090 (24 GB), one llama-server, KV cache q8_0:

| metric | measured |

|---|---|

| decode speed | 149 tok/s |

| peak VRAM (4-slot serving) | 14.2 GB |

| max measured context (q8_0 KV) | 131,072 |

llama-server -m Qwen3.6-35B-A3B-Heretic-Cerebellum-v1-Q3_K_M.gguf \
  -ngl 99 --parallel 4 -c 24576 --jinja

_This rig's measurements; no quality claims beyond them._

Runtime — Casual Deployment

llama-server \
  --model Qwen3.6-35B-A3B-Heretic-Cerebellum-v1-Q3_K_M.gguf \
  --mmproj Qwen3.6-35B-A3B-uncensored-heretic-mmproj-BF16.gguf \
  --n-gpu-layers 99 \
  --ctx-size 8192 \
  --jinja

--jinja is required for Qwen3.6. The enable_thinking chat-template flag

only takes effect when the Jinja template path is active; without it, the

model defaults to thinking mode on every request.

Non-thinking requests require an explicit flag at the API level:

{"chat_template_kwargs": {"enable_thinking": false}}

Qwen3.6 does not support the /think and /nothink soft-switch tokens

used by Qwen3.5. Thinking mode is on by default.

Recommended Sampling Parameters

From the official Qwen3.6-35B-A3B documentation.

| Mode | temperature | top_p | top_k | min_p | presence_penalty | repetition_penalty |

|------|-------------|-------|-------|-------|------------------|--------------------|

| Thinking — general | 1.0 | 0.95 | 20 | 0.0 | 1.5 | 1.0 |

| Thinking — precise coding (WebDev) | 0.6 | 0.95 | 20 | 0.0 | 0.0 | 1.0 |

| Non-thinking (instruct) | 0.7 | 0.80 | 20 | 0.0 | 1.5 | 1.0 |

presence_penalty can be adjusted between 0 and 2 to reduce repetition loops;

higher values may occasionally cause language mixing.

Reproduction

Standard Cerebellum recipe. The tensor-type override file and ablation logs

from the stock v3 build apply directly.

# 1. imatrix (constant ~300 MB RAM)
python -m osmosis.imatrix_stream \
    --model Qwen3.6-35B-A3B-uncensored-heretic-BF16.gguf \
    --output imatrix.dat

# 2. quantize with stock llama-quantize
llama-quantize \
    --imatrix imatrix.dat \
    --tensor-type-file cerebellum_v3_overrides.txt \
    Qwen3.6-35B-A3B-uncensored-heretic-BF16.gguf \
    Qwen3.6-35B-A3B-Heretic-Cerebellum-v1-Q3_K_M.gguf \
    Q3_K_M

The imatrix used for this build was generated from the Unsloth coder corpus

(same corpus as the stock Cerebellum v3 build).

The 360-line tensor override file (cerebellum_v3_overrides.txt) is included

in this repository alongside the ablation logs.

Benchmark Artifacts

Summary JSONs, per-question JSONL samples, EvalPlus eval JSON files, and

adversarial audit reports (AUDIT_*.md) are in benchmark_results/ in this

repository per project policy.

Credits

• Base model: Qwen/Qwen3.6-35B-A3B — Qwen Team
• Heretic variant and BF16 source: llmfan46/Qwen3.6-35B-A3B-uncensored-heretic-GGUF — llmfan46
• Abliteration tool: Heretic v1.2.0 by p-e-w
• GGUF runtime: llama.cpp
• Quantization method and workflow: Cerebellum — deucebucket