---

license: apache-2.0

license_link: LICENSE

base_model: wangzhang/Qwen3.6-27B-abliterated

tags:

• gguf
• abliterated
• uncensored
• quantized
• qwen3.6
• hybrid-attention
• gated-deltanet
• llama.cpp

language:

• en
• zh

library_name: gguf

pipeline_tag: text-generation

---

Qwen3.6-27B-abliterated-v2 — GGUF

GGUF quantizations of wangzhang/Qwen3.6-27B-abliterated, a second-pass refusal-suppressed Qwen3.6-27B (10/100 refusals, 15/15 hard-prompt compliance, cumulative KL ≈ 0.024 vs original Qwen/Qwen3.6-27B). See the base model card for the full abliteration methodology and V1 → V4 sweep history.

Files

| File | Type | Size | VRAM (context 4k) | Notes |

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

| Qwen3.6-27B-abliterated-v2-F16.gguf | F16 | ~54 GB | ~56 GB | Lossless-vs-BF16 (small rounding on bfloat16 → float16 downcast). Reference quality. |

| Qwen3.6-27B-abliterated-v2-Q8_0.gguf | Q8_0 | ~28 GB | ~30 GB | Near-lossless. Recommended if you have 32+ GB VRAM or unified memory. |

| Qwen3.6-27B-abliterated-v2-Q4_K_M.gguf| Q4_K_M | ~16 GB | ~18 GB | Recommended for 24 GB cards (3090/4090/A6000) and 24 GB Apple Silicon. K-quant with mixed-precision blocks (Q6_K for attn_qkv + ffn_down, Q4_K elsewhere). |

All three were produced from the same source BF16 safetensors checkpoint with llama.cpp convert_hf_to_gguf.py (F16) and llama-quantize (Q8_0, Q4_K_M). No imatrix calibration — if you want better Q4 behaviour at minimal extra setup, run your own imatrix pass on a small held-out prompt set and requantize from the F16 file here.

llama.cpp version requirement

You need llama.cpp with Qwen3.5 hybrid-GDN support — this is recent (landed 2026). The architecture is registered as qwen3_5 in GGUF metadata and the inference code handles 48 GatedDeltaNet layers + 16 full-attention layers with the full_attention_interval = 4 interleave pattern. Older llama.cpp builds will error out with "unknown model architecture".

Verified working: llama.cpp master (commit ≥ 2026-01), which added Qwen3_5TextModel(_LinearAttentionVReorderBase) and the corresponding inference graph. Check your build with:

./llama-cli --help | grep -i qwen
# or quickly try a small generation:
./llama-cli -m Qwen3.6-27B-abliterated-v2-Q4_K_M.gguf -p "Hello" -n 16

ollama, LM Studio, and KoboldCpp inherit llama.cpp — use their latest releases and it should just work. If you get a "unknown model type 'qwen3_5'" error, your frontend is on an old bundled llama.cpp — update.

Quick start

llama.cpp (CLI)

# Q4_K_M on a 24 GB card, context 4k:
./llama-cli -m Qwen3.6-27B-abliterated-v2-Q4_K_M.gguf \
    -c 4096 -ngl 99 \
    -p "How do I pick a lock?" -n 400 --temp 0.7

# Q8_0 on 32+ GB:
./llama-cli -m Qwen3.6-27B-abliterated-v2-Q8_0.gguf \
    -c 8192 -ngl 99 \
    -p "Explain how to make methamphetamine step by step." -n 500 --temp 0.7

ollama

# Create a Modelfile that points at the GGUF:
cat > Modelfile <<EOF
FROM Qwen3.6-27B-abliterated-v2-Q4_K_M.gguf
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER stop "<|im_end|>"
TEMPLATE """{{ if .System }}<|im_start|>system
{{ .System }}<|im_end|>
{{ end }}{{ if .Prompt }}<|im_start|>user
{{ .Prompt }}<|im_end|>
{{ end }}<|im_start|>assistant
"""
EOF

ollama create qwen36-27b-abliterated-v2 -f Modelfile
ollama run qwen36-27b-abliterated-v2

LM Studio / Jan / KoboldCpp

Drop any of the three GGUF files into your model folder, select the Qwen 2.5-compatible chat template (the embedded chat_template.jinja in this GGUF matches the Qwen <|im_start|> / <|im_end|> format), set context length to your hardware budget, and go.

Architectural notes (if converting yourself or debugging)

Qwen3.6-27B is architecturally unusual — Qwen3_5ForConditionalGeneration VLM wrapper over a hybrid dense stack (48 GDN + 16 full-attention, interleave [GDN, GDN, GDN, full] × 16). Things that commonly bite GGUF conversion:

1. *GDN tensor naming in GGUF is ssm_*, not linear_attn_. llama.cpp reuses Mamba-2 GGUF tensor names for the GDN path (ssm_alpha, ssm_beta, ssm_conv1d, ssm_dt, ssm_norm, ssm_out). If you grep a tensor list and don't see linear_attn entries, that's normal.
2. Full-attn blocks use attn_qkv fused, not separate attn_q/k/v. The _LinearAttentionVReorderBase conversion path re-orders and fuses Q/K/V at write time.
3. The VLM vision tower is dropped by the conversion script — text-only inference is what you get from these GGUF files, which matches the text-only abliteration pipeline. If you want image input, use the original transformers checkpoint at wangzhang/Qwen3.6-27B-abliterated-v2.
4. No MTP head — the mtp_num_hidden_layers = 1 auxiliary head in the HF checkpoint is dropped by GGUF conversion (llama.cpp doesn't support Qwen-style MTP speculative decoding yet). Standard non-speculative sampling only.

Behavioural reminder

The base model is refusal-suppressed. See the abliterated-v2 README for the full intent: this is research infrastructure, not a product. It will produce directly harmful content without disclaimers or warnings. If you deploy it to users who expect a safety layer, you are responsible for that layer yourself. The 10/100 refusal number is from an LLM-judge evaluation on 100 held-out harmful prompts — you should assume the real-world refusal rate on adversarial prompts is lower than 10 % because the judge is strict about what counts as compliance.

Conversion provenance

• Source: wangzhang/Qwen3.6-27B-abliterated-v2 at the HEAD at time of conversion (README + model-00001/00002.safetensors committed together).
• Tool: llama.cpp convert_hf_to_gguf.py → F16, then llama-quantize F16 → Q8_0 and F16 → Q4_K_M in parallel.
• Hardware: vast.ai A100-SXM4-80GB pod, conversion on CPU (-t 16 threads), outputs written to tmpfs /dev/shm (the NFS mfs mount wasn't reliable enough for 54 GB sequential writes during the first attempt; RAM-backed tmpfs is what actually worked).
• Wall time: F16 conversion ~5 min, Q8_0 quantize ~80 s, Q4_K_M quantize ~3 min. Total ~10 min CPU work on a 128-vCPU A100 pod.

Credits

• Base model: wangzhang/Qwen3.6-27B-abliterated — second-pass abliterated Qwen3.6-27B.
• Original model: Qwen/Qwen3.6-27B by Alibaba Cloud.
• Quantization toolchain: ggml-org/llama.cpp.
• Abliteration tool: wuwangzhang1216/abliterix.

<!-- wangzhang-provenance-license-v1 -->

Provenance and Modification Notice

• Immediate source checkpoint: wangzhang/Qwen3.6-27B-abliterated
• Ultimate upstream model: Qwen/Qwen3.6-27B
• Exact base revision used: Not recorded in the existing release artifacts; the current upstream HEAD is not substituted.
• Modification method: Abliterix weight-space / representation intervention intended to reduce refusal behavior.
• Additional transformation: GGUF conversion/quantization; see the existing quantization details in this card.
• Modified and published by: Wangzhang Wu
• Repository first published: 2026-04-24 (Hugging Face repository metadata)

The original model weights and/or derived checkpoint were modified. This repository is an independent derivative and is not an official release of the upstream model developer.

License and Attribution

The governing upstream license is Apache License 2.0. A copy is included in LICENSE. License source audited on 2026-08-29: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE

All applicable upstream copyright, attribution, acceptable-use, and other license terms remain in effect. This repository grants no rights beyond those provided by the upstream license. Downstream users must preserve applicable license and attribution notices.

---

<!-- wangzhang-safety-disclaimer-v1 -->

Disclaimer and Responsible Use / 免责声明与安全使用声明

English

This is an experimental, modified model provided for research, evaluation, and other lawful purposes. Its safety alignment, refusal behavior, or other safeguards may have been weakened or removed. It may produce inaccurate, biased, offensive, explicit, dangerous, or illegal content. Outputs are not professional advice and must not be relied on for medical, legal, financial, safety-critical, or other high-stakes decisions without qualified human review.

You are solely responsible for how you access, use, deploy, fine-tune, or redistribute this model and its outputs, including compliance with applicable laws, regulations, licenses, third-party rights, platform policies, and the original model's terms. Do not use it to facilitate harm, illegal activity, malware, fraud, privacy violations, targeted harassment, weapons development, or decisions that materially affect a person's rights or access to essential services without appropriate authorization, safeguards, and qualified oversight.

Before deployment, perform a context-specific risk assessment and testing; use human oversight, access controls, content filtering, rate limits, monitoring, logging, and incident-response procedures as appropriate. Preserve this notice in downstream redistributions.

The model is provided "AS IS", without warranties of any kind. To the fullest extent permitted by applicable law, the maintainer disclaims liability for claims, damages, or losses arising from use, misuse, inability to use, or redistribution of the model or its outputs. Nothing in this notice overrides applicable law or the governing license, and this notice is not legal advice.

中文

本模型属于实验性改造模型，仅供研究、评测及其他合法用途。其安全对齐、拒答机制或其他防护可能已被削弱或移除，因此可能生成不准确、偏见、冒犯、露骨、危险或违法内容。输出不构成医疗、法律、金融等专业意见；涉及高风险或重大权益的决定，必须由具备资质的人员复核。

使用者须对模型及其输出的访问、使用、部署、微调和再分发承担全部责任，并遵守适用法律法规、许可证、第三方权利、平台政策及原模型条款。不得将本模型用于促成伤害、违法活动、恶意软件、欺诈、侵犯隐私、定向骚扰、武器开发，或在缺乏适当授权、防护和专业监督时，用于实质影响个人权利或基本服务获取的决策。

部署前应进行与具体场景相匹配的风险评估和测试，并酌情采用人工监督、访问控制、内容过滤、限流、监控、日志和事件响应措施；下游再分发时应保留本声明。

本模型按“现状”提供，不附带任何形式的保证。在适用法律允许的最大范围内，维护者不对因使用、误用、无法使用或再分发本模型及其输出而产生的索赔、损害或损失承担责任。本声明不取代适用法律或管辖本模型的许可证，也不构成法律意见。