Qwen3.6-27B Abliterated + MTP GGUF

The first publicly available Qwen3.6-27B uncensored GGUF with native MTP speculative decoding.
Refusal-free at the weight level · Full MTP block grafted · ~70 t/s on RTX 3090 · No custom fork required

Published by Gastón Parravicini

Why this exists

When Qwen3.6-27B dropped, two things were true at the same time:

• The only abliterated versions available had no MTP support — the draft heads were stripped during the merge, killing speculative decoding speed
• The only MTP-enabled GGUFs were fully censored — original refusal behavior intact

Nobody had combined both. Doing it required writing custom patches to handle Qwen3.6's MTP tensor naming conventions and avoid GGUF metadata corruption. This release is the result of that work.

This was the first. It still has the most complete quant coverage.

What this release adds

Refusal suppression

Removed at the weight level using two-pass orthogonal-projection abliteration (abliterix + Optuna TPE). KL divergence of 0.024 vs the base model — well below the 0.05 threshold where quality degradation becomes measurable. General intelligence, reasoning, and tool use are fully intact.

Full MTP speculative decoding

The complete blk.64 MTP block — all 15 tensors — grafted from the official Unsloth release. MTP is baked into Qwen3.6-27B at training time, so the draft heads are native, not bolted on.

~55% speedup. No quality loss. No custom fork required — MTP support is now in llama.cpp mainline.

Quants

Mac users: memory is unified — M2 Max 32GB fits Q4_K_M comfortably.

All quants include the full MTP draft head block. Speculative decoding works across all tiers.

Requirements

MTP support is now in llama.cpp mainline. No custom fork needed.

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build -DGGML_CUDA=ON -DCMAKE_BUILD_TYPE=Release
cmake --build build -j$(nproc) --target llama-server llama-cli

For Mac (Metal), replace -DGGML_CUDA=ON with -DGGML_METAL=ON.

Usage

Standard server (OpenAI-compatible API)

./llama-server \
  -m Qwen3.6-27B-abliterated-Gaston-MTP-Q4_K_M.gguf \
  -ngl 99 \
  --flash-attn on \
  --ctx-size 32768 \
  --spec-type draft-mtp \
  --spec-draft-p-min 0.75 \
  --spec-draft-n-max 3 \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --host 0.0.0.0 \
  --port 8080 \
  --jinja \
  -n 16384

Agentic and coding workloads (thinking visible)

./llama-server \
  -m Qwen3.6-27B-abliterated-Gaston-MTP-Q4_K_M.gguf \
  -ngl 99 \
  --flash-attn on \
  --ctx-size 80000 \
  -b 2048 -ub 1024 \
  --temp 0.6 \
  --top-p 0.95 \
  --top-k 20 \
  --min-p 0.0 \
  --spec-type draft-mtp \
  --spec-draft-p-min 0.75 \
  --spec-draft-n-max 3 \
  --cache-type-k q4_0 \
  --cache-type-v q4_0 \
  --host 0.0.0.0 \
  --port 8080 \
  --jinja \
  -n 16384 \
  --chat-template-kwargs '{"preserve_thinking":true}'

Recommended sampling

temperature: 0.6
top_p: 0.95
top_k: 20
min_p: 0.0

Validated use cases

This model has been tested in production with:

• PentAGI — autonomous multi-agent penetration testing, full tool call loop
• Qwen Code — agentic coding with MCP servers (Context7, SearXNG)
• OpenClaw — local agent framework with OpenAI-compatible API
• Standard OpenAI function calling via --jinja chat template

The abliteration does not affect tool call behavior, function calling, or structured output generation.

Abliteration quality

Refusal suppression was done with orthogonal projection + Optuna TPE optimization. The result is one of the cleanest abliterations publicly available for this model:

Lower KL divergence = closer to the original model's output distribution = less quality loss. At 0.024, the abliteration is essentially invisible to downstream tasks.

Base model benchmarks

Capabilities are fully preserved. Abliteration removes refusal directions only — not reasoning, coding, or general intelligence.

Coding & Agentic

Knowledge & Reasoning

Full benchmark details: official Qwen3.6-27B model card.

How this was built

Standard abliteration pipelines strip MTP tensors during the merge — there was no existing tool that preserved them. This required a custom multi-step process:

1. Download wangzhang/Qwen3.6-27B-abliterated BF16 safetensors — abliteration via two-pass orthogonal projection with Optuna TPE optimization (KL divergence 0.024)
2. Convert to GGUF Q8_0 using the llama.cpp converter
3. Graft the full blk.64 MTP block (15 tensors) from unsloth/Qwen3.6-27B-MTP-GGUF using a patched version of havenoammo's graft script
4. Three custom patches written to handle Qwen3.6's MTP tensor naming conventions and prevent duplicate KV metadata errors in the output GGUF
5. Quantize all tiers from the grafted Q8_0 using llama-quantize
6. Validate MTP activation in llama.cpp — confirmed draft acceptance rates above 90% across all context lengths tested

The three patches were the non-trivial part. Qwen3.6's tensor naming diverges from prior Qwen versions in ways that broke existing graft tooling. No existing public script handled this correctly at the time of release.

Credits

License

Apache 2.0, inherited from Qwen/Qwen3.6-27B.

Disclaimer

This model has had its safety guardrails removed at the weight level. It is intended for research, security testing, and unrestricted local inference. You are solely responsible for how you use it.