Qwen3.8-27B-Uncensored-MLX

An abliterated (refusal-removed) MLX build of Qwen's Qwen3.8-27B — 2 / 4 / 6 / 8-bit for Apple Silicon

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An abliterated (refusal-removed) build of Qwen/Qwen3.8-27B — a 27B-parameter dense, hybrid-attention (Gated DeltaNet linear + full attention) native vision-language model with thinking control, tool-calling and an MTP head — quantized to MLX format for Apple Silicon. Four precisions are provided as subfolders: 2 / 4 / 6 / 8-bit (affine, group size 64). The vision tower, norms and conv layers are kept in BF16; only the language-model linear weights (including embed_tokens / lm_head) are quantized. Browse all models in the OrcaRouter Model Catalog. This model is deployed as API here.

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⚠️ Disclaimer & risks — read before use

This model has had its safety alignment substantially removed via abliteration (orthogonalizing the refusal direction out of the residual stream). As a direct consequence:

• It will comply with harmful, unethical, offensive, or illegal requests that the original Qwen3.8-27B would refuse. It has no meaningful built-in guardrails.
• It is released strictly for legitimate research — interpretability, AI-safety and refusal-mechanism study, red-teaming, robustness evaluation, and controlled experiments.
• You assume full responsibility and liability for how you use it and for everything it generates. Add your own safety, moderation and abuse-prevention layers before any deployment.
• Use must comply with the Apache 2.0 License inherited from the base model, and all laws and regulations that apply to you.
• The authors and uploaders accept no liability for any misuse or harm. Outputs do not reflect the views of the uploaders or of Qwen / Alibaba.

Specific risks

• Harmful content on demand — it will produce instructions for malware, exploits, weapons, fraud and other illegal or dangerous activity when asked.
• No refusals — jailbreak / safety probes "succeed" trivially; do not mistake this for a passing safety evaluation.
• Confident falsehoods & bias — it can generate false, defamatory, biased or offensive text and present it authoritatively.
• Expanded attack surface — preserved vision, tool-calling and 262K context mean these risks extend to image understanding and autonomous / agentic use.
• Quantization noise — lower-bit builds (esp. 2-bit) add instability on top of the above; outputs can be degraded or nonsensical.

Intended use vs out of scope

• Intended: AI-safety and interpretability research, refusal-mechanism study, red-teaming, guardrail and robustness evaluation, controlled academic experiments.
• Out of scope: any deployment to end users, minors, or production without your own moderation / safety layer; any unlawful, harmful, or rights-infringing use.

By downloading or using this model you acknowledge and accept the above.

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Available quantizations

Folder	Bits/weight	Size	Shards	Min Mac RAM	Quality vs BF16 source
8-bit/	8.627	~27.5 GB	6	32 GB	Near-lossless — recommended for quality
6-bit/	6.661	~22 GB	5	24–32 GB	Excellent — strong quality/size balance
4-bit/	4.695	~15 GB	3	24 GB	Very good — recommended default
2-bit/	2.729	~8.7 GB	2	16 GB	⚠️ Severely degraded — archival only

2-bit warning: at 27B, 2-bit quantization collapses generation quality (repetition loops, garbled output). It is included only as an extreme-compression archive; do not use it for real work — prefer 4-bit or higher.

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Verification & test results

All builds were quantized from the same abliterated BF16 source and verified numerically (dequantized weights vs. source) plus tested by generation on GPU.

Precision	Numerical fidelity (cosine)	Text / Chinese / Code	Refusal probes	Vision
8-bit	cos 0.9997	✅	✅ 0 refusals	✅
6-bit	cos 0.9996	✅	✅ 0 refusals	✅
4-bit	cos 0.996	✅	✅ 0 refusals	✅
2-bit	cos 0.92	⚠️ breaks down	⚠️ garbled (not refusal)	partial

• Uncensored preserved: red-team probes (exploit walkthrough, controversial argument) return substantive content with zero refusals on 4 / 6 / 8-bit.
• Multimodal preserved: shapes, colors, position, background and text in a probe image are described correctly on 4 / 6 / 8-bit.
• Speed: ~32–37 tok/s steady-state on a single H200 (MLX CUDA backend). MLX's native target is Apple Silicon (Metal).

Note: on 6-bit, mlx's offline mx.dequantize mis-unpacks these weights (a library edge case), so correctness is verified by clean generation — inference is unaffected.

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Usage (mlx-vlm, Apple Silicon)

pip install -U mlx-vlm    # needs mlx-vlm >= 0.6.13, mlx >= 0.32

# download one precision (e.g. 4-bit) from the subfolder
hf download orcarouter/Qwen3.8-27B-Uncensored-MLX --include "4-bit/*" \
    --local-dir ./Qwen3.8-27B-Uncensored-MLX

# text
python -m mlx_vlm generate \
    --model ./Qwen3.8-27B-Uncensored-MLX/4-bit \
    --prompt "Explain quantum entanglement in one sentence." --max-tokens 256

# vision (image + text)
python -m mlx_vlm generate \
    --model ./Qwen3.8-27B-Uncensored-MLX/4-bit \
    --image path/to/image.png \
    --prompt "Describe this image." --max-tokens 256

# OpenAI-compatible server
python -m mlx_vlm server --model ./Qwen3.8-27B-Uncensored-MLX/4-bit --port 8080

On Apple Silicon the Metal backend is used automatically — no CUDA setup needed. (On a Linux CUDA backend, vision requires MLX_CUDA_USE_CUDNN_SDPA=0; this does not apply on macOS.)

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Model details

Base model	Qwen/Qwen3.8-27B
Architecture	Qwen3_5ForConditionalGeneration — 64 layers, hidden 5120, hybrid Gated DeltaNet (48 linear + 16 full attention, interval 4), native VL tower
Modification	Abliteration (refusal-direction removal), then MLX affine quantization
Quantization	MLX affine, group size 64, per-precision 2 / 4 / 6 / 8-bit
Kept in BF16	vision tower, all norms, linear-attention conv1d
Quantized	language-model linear layers incl. embed_tokens and lm_head
Context	262,144 tokens