This repository contains the full sweep of Unsloth Dynamic v2 (UD) GGUF quantizations for the pre-ablated gorbatjovy/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-heretic model.

The base model was sourced from gorbatjovy, who previously ran it through the Heretic ablation pipeline to mathematically isolate and project out the refusal vectors.

📂 Provided Files

This repository provides multiple quantization formats to suit your hardware. We provide both standard variants (with the MTP heads stripped) and -MTP- variants that retain the Multi-Token Prediction heads for environments that support them.

We highly recommend the IQ4_XS variant for the absolute best balance of quality, speed, and memory usage.

Standard & MTP Quants

Name	Quant method	Size (GB)	Max RAM required	Use case
Q8_0	Q8_0	~29.1 GB	~31.6 GB	Lossless cache-friendly format. Extremely high quality, large footprint.
Q6_K	Q6_K	~22.3 GB	~24.8 GB	Very high quality, near perfectly lossless.
Q5_K_M	Q5_K_M	~19.5 GB	~22.0 GB	High quality, minimal loss.
IQ4_NL	IQ4_NL	~16.8 GB	~19.3 GB	Excellent quality, uses non-linear quantization.
Q4_K_XL	Q4_K_XL	~16.5 GB	~19.0 GB	Large vocabulary Q4 variant. Very high quality.
Q4_K_M	Q4_K_M	~16.2 GB	~18.7 GB	Solid balance of quality and size.
IQ4_XS	IQ4_XS	~15.5 GB	~18.0 GB	Recommended. Importance quantization provides the smartest logic for the size.
Q3_K_M	Q3_K_M	~13.1 GB	~15.6 GB	Very high compression, noticeable quality loss.
IQ3_S	IQ3_S	~12.5 GB	~15.0 GB	Extreme compression with importance quantization.
IQ3_XXS	IQ3_XXS	~11.2 GB	~13.7 GB	Max compression in the 3-bit range.
Q2_K	Q2_K	~10.4 GB	~12.9 GB	Extreme compression, heavy quality loss.
IQ2_XS	IQ2_XS	~9.8 GB	~12.3 GB	Max compression in the 2-bit range.
IQ2_XXS	IQ2_XXS	~9.0 GB	~11.5 GB	Absolute maximum compression. Not recommended.

💡 About Unsloth Dynamic (UD) Quantization

All files were quantized using Unsloth's native v2 dynamic quantization logic rather than static llama.cpp quantize tools. This process applies dynamic activation quantization and importance weighting (especially for IQ quants) during the compression, resulting in significantly higher fidelity at identical file sizes.

🛠️ MTP IQ Quantization & Dummy Block Patch

Because the llama-quantize tool strictly requires importance matrix data for all layers (including the 64th Multi-Token Prediction layers which are absent in standard models), the IQ variants of the -MTP- GGUFs failed during standard compilation. To bypass this limitation and retain the MTP heads in highly compressed IQ formats, we custom-patched the importance matrix. A synthesized, neutral dummy block (10,240 elements) was dynamically injected into the imatrix for the blk.64.nextn.eh_proj.weight tensor, allowing the IQ algorithms to perfectly compress the model while fully preserving MTP capabilities.

📊 Speculative Decoding (MTP) Benchmarks

Below are the speedup benchmarks recorded on a single RTX 3090. To ensure complete transparency, all MTP tests were run with the following generation parameters (mirroring a real-world uncensored production setup):

temperature: 0.6
top_p: 1.0
top_k: 0
min_p: 0.05
presence_penalty: 0.0
repeat_penalty: 1.0

The benchmark tasked the models with solving two custom riddles: ("I have no voice, yet I tell tales of ancient light...") and ("I am a city without streets, a library with no pages, and a vault that contains nothing but pathways..."). The Solved? column confirms whether the model correctly deduced the answers (e.g., Space/Universe/Telescope for the first, and RAM/SSD for the second) without hallucinating, proving that the aggressive quantization bitrates didn't destroy its reasoning capabilities.

Model / Size	Base (No MTP)	MTP (Draft 2)	MTP (Draft 3)	MTP (Draft 4)	MTP (Draft 5)
IQ4_NL 16.8 GB	43.1 t/s ❌ / ✅	68.8 t/s +59.5% • Acc: 68.7% ✅ / ✅	67.2 t/s +55.8% • Acc: 58.9% ✅ / ✅	72.0 t/s +66.8% • Acc: 61.4% ✅ / ✅	59.2 t/s +37.2% • Acc: 45.8% ✅ / ✅
Q4_K_XL 16.5 GB	40.7 t/s ✅ / ✅	59.0 t/s +44.9% • Acc: 71.5% ✅ / ✅	56.2 t/s +37.9% • Acc: 62.4% ✅ / ✅	53.5 t/s +31.3% • Acc: 54.6% ✅ / ✅	47.0 t/s +15.4% • Acc: 45.5% ✅ / ✅
Q4_K_M 16.2 GB	40.7 t/s ✅ / ✅	59.8 t/s +46.9% • Acc: 73.0% ✅ / ✅	58.1 t/s +42.8% • Acc: 65.6% ✅ / ✅	53.0 t/s +30.1% • Acc: 53.6% ✅ / ✅	48.5 t/s +19.1% • Acc: 47.7% ✅ / ✅
IQ4_XS 15.5 GB	44.6 t/s ✅ / ✅	72.1 t/s +61.5% • Acc: 75.4% ❌ / ✅	71.6 t/s +60.5% • Acc: 64.2% ✅ / ✅	68.2 t/s +52.8% • Acc: 53.8% ✅ / ✅	64.4 t/s +44.4% • Acc: 53.5% ✅ / ✅
IQ3_S 12.5 GB	43.5 t/s ✅ / ✅	56.7 t/s +30.6% • Acc: 63.4% ✅ / ✅	58.7 t/s +35.0% • Acc: 59.3% ✅ / ✅	58.3 t/s +34.2% • Acc: 52.9% ✅ / ✅	52.1 t/s +19.9% • Acc: 42.6% ✅ / ✅
IQ3_XXS 11.2 GB	46.9 t/s ✅ / ✅	59.3 t/s +26.5% • Acc: 62.0% ✅ / ✅	60.3 t/s +28.6% • Acc: 56.8% ✅ / ✅	54.4 t/s +16.0% • Acc: 40.8% ✅ / ✅	51.9 t/s +10.6% • Acc: 38.5% ✅ / ✅
IQ2_XS 9.8 GB	49.4 t/s ✅ / ✅	54.8 t/s +11.0% • Acc: 51.8% ✅ / ✅	49.9 t/s +1.1% • Acc: 40.2% ✅ / ✅	49.4 t/s +0.0% • Acc: 34.8% ✅ / ✅	41.3 t/s -16.3% • Acc: 26.5% ✅ / ✅
IQ2_XXS 9.0 GB	52.0 t/s ✅ / ✅	51.7 t/s -0.6% • Acc: 36.1% ✅ / ✅	44.6 t/s -14.2% • Acc: 29.1% ✅ / ✅	39.5 t/s -24.1% • Acc: 20.8% ✅ / ✅	32.4 t/s -37.8% • Acc: 15.4% ✅ / ✅
Your mileage may vary depending on your specific hardware architecture and whether your inference is compute-bound or memory bandwidth-bound.

🚀 Downloading and Running

Downloading using huggingface-cli

You can download your preferred quant using the official huggingface-cli:

huggingface-cli download mcgonzo/Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-heretic-GGUF Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-heretic-UD-IQ4_XS.gguf --local-dir . --local-dir-use-symlinks False

Running the model

llama.cpp / llama-server If you are running the llama-server directly, we recommend the following parameters for the best uncensored performance:

bash

llama-server -m Qwen3.6-27B-Architect-Polaris2-Fable-B-F451-heretic-UD-IQ4_XS.gguf \
  --port 8080 \
  -c 32768 \
  -ngl 99 \
  --flash-attn on \
  --min-p 0.05 \
  --top-k 0 \
  --top-p 1.0 \
  --repeat-penalty 1.0