---
license: apache-2.0
base_model:
  - huihui-ai/Huihui-Qwen3.6-27B-abliterated
base_model_relation: quantized
tags:
  - hipfire
  - mq4
  - awq
  - rdna
  - rocm
  - qwen3.6
  - abliterated
  - uncensored
library_name: hipfire
pipeline_tag: text-generation
---

# Qwen3.6-27B-Uncensored — hipfire MQ4 (AWQ)

A 4-bit [hipfire](https://github.com/Kaden-Schutt/hipfire) **MQ4** quantization of an
abliterated (uncensored) Qwen3.6-27B, built for Rust-native inference on AMD RDNA / CDNA GPUs.

- **Base model:** [huihui-ai/Huihui-Qwen3.6-27B-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3.6-27B-abliterated) (BF16 safetensors)
- **Architecture:** Qwen3.6-27B dense — 64 layers (48 Gated-DeltaNet linear-attention + 16 full gated-attention), hidden 5120, FFN 17408, vocab 248320
- **Format:** hipfire `MQ4G256` (~4.4 bpw), single self-contained `.mq4` file
- **Text only:** the base model's vision tower (`mmproj`) is **not** included

## ⚠️ This model ships AWQ scales

This is **not** a plain MQ4 quant. It carries **AWQ (Activation-aware Weight
Quantization) pre-scaling** baked in:

- Each quantized linear weight has a companion **`<weight_name>.awq_scale.weight`**
  tensor (1-D F16, length = input dim K) embedded in the same `.mq4` file.
- During quantization, weights were pre-scaled per input channel by
  `s[j] = (RMS_act[j])^α`, `α = 0.5` (paper formula, geo-mean-normalized to 1).
- At inference the hipfire runtime divides the activation by `awq_scale`
  (`x /= awq_scale` before the rotation kernel), exactly compensating the
  pre-scale so the math is equivalent to the unscaled product — while the 4-bit
  grid is spent where activations are largest.

**Compatibility:** the hipfire runtime auto-detects and applies these scales by
**tensor name** (see `hipfire-runtime::hfq::load_awq_scale`), not by file
extension — so the plain `.mq4` filename is correct and the AWQ behavior is
preserved. A loader that ignores `*.awq_scale.weight` would read these weights as
if un-prescaled and produce **corrupt logits**, so use a hipfire build that
supports AWQ.

The `lm_head` is plain MQ4 with **no** AWQ scale (the safe default — avoids the
mismatched-head logit-corruption failure mode).

## Importance matrix (imatrix)

AWQ per-channel scales were derived from a hipfire-native imatrix generated with
`llama-imatrix` over an **agentic** calibration corpus (hermes agent-reasoning
traces, ChatML-flattened — **not** wikitext), 285 chunks × 2048 tokens.

## Usage (hipfire)

```bash
hipfire serve --model qwen3.6-27b-uncensored.mq4
# or a one-shot eval / coherence probe:
coherence_probe --model qwen3.6-27b-uncensored.mq4 \
  --prompt-file prompt.txt --max-tokens 200 --temperature 0.0
```

Recommended KV mode: `fwht3` (the asym3 default is lower quality).

## Provenance / caveats

- Quantized on an MI300x (gfx942). Coherence-gate: **0 hard / 0 soft** across all
  detectors (attractor / n-gram / loop-guard / special-leak / think-empty / eos).
- This is a different abliteration recipe than the HauhauCS "Balanced" GGUFs; it
  derives from the huihui-ai abliterated weights.
- Uncensored model: alignment guardrails have been ablated from the base weights.
  Use responsibly and in accordance with the Apache-2.0 license and the Qwen terms.