orcarouter/Qwen3.8-27B-Uncensored-MLX
huggingface.co/orcarouter/Qwen3.8-27B-Uncensored-MLX
Model card
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 — 2 / 4 / 6 / 8-bit (affine, group
- size 64) — each as a subfolder, with the 4-bit build also mirrored at the repo root so
- that
orcarouter/Qwen3.8-27B-Uncensored-MLXloads directly in LM Studio and other tools - that treat a repo as a single model. 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.
⚠️ 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.
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.
- Repo root =
4-bit/. The root of this repo holds a copy of the 4-bit build, so --model orcarouter/Qwen3.8-27B-Uncensored-MLX(no subfolder) resolves to 4-bit. Use the- subfolder paths to pick any other precision.
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.dequantizemis-unpacks these weights (a library edge - case), so correctness is verified by clean generation — inference is unaffected.
Usage (mlx-vlm, Apple Silicon)
```bash 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.)
Multi-Token Prediction (MTP) — speculative decoding
This model has a native MTP head. In MLX, MTP is loaded as a separate drafter for
speculative decoding: the main model is loaded with the MTP weights stripped, and the drafter
is passed explicitly. The drafter lives in the mtp/ subfolder of this repo
(model_type: qwen3_5_mtp) and works with any main-model precision (4 / 6 / 8-bit).
- Setting an
mtp_enabledflag on the main model alone does nothing — MLX needs the - separate drafter passed via
--draft-model … --draft-kind mtp.
```bash # fetch a main-model precision (e.g. 6-bit) plus the MTP drafter hf download orcarouter/Qwen3.8-27B-Uncensored-MLX --include "6-bit/" "mtp/" \ --local-dir ./Qwen3.8-27B-Uncensored-MLX
generate with MTP speculative decoding
python -m mlx_vlm generate \ --model ./Qwen3.8-27B-Uncensored-MLX/6-bit \ --draft-model ./Qwen3.8-27B-Uncensored-MLX/mtp \ --draft-kind mtp --draft-block-size 4 \ --prompt "Explain quantum entanglement in one sentence." --max-tokens 256
OpenAI-compatible server with MTP
python -m mlx_vlm server \ --model ./Qwen3.8-27B-Uncensored-MLX/6-bit \ --draft-model ./Qwen3.8-27B-Uncensored-MLX/mtp \ --draft-kind mtp --draft-block-size 4 --port 8080 ```
Requirements: an mlx-vlm build with the qwen3_5_mtp drafter and --draft-kind mtp
(available on mlx-vlm main). MTP acceptance is lossless — with greedy decoding the output is
identical to running without the drafter, just fewer forward passes on accepted tokens. The
speedup is realized on Apple Silicon (Metal); one drafter serves all precisions.
Usage (LM Studio)
Search for orcarouter/Qwen3.8-27B-Uncensored-MLX in LM Studio and download it — the repo
root is the 4-bit build, and the other precisions appear as separate download options.
Three things to get right:
1. This repo is gated. LM Studio downloads anonymously by default and will get an HTTP 401. Accept the terms on the model page once, then paste a Hugging Face read token into LM Studio under Settings → Integrations → Hugging Face. 2. Turn off KV cache quantization. MLX vision models do not support it on this architecture, and loading fails during initialization if it is enabled (mlx-engine#286). 3. Pick a quant that fits. 8-bit is ~29.5 GB on disk and wants a 64 GB Mac; 6-bit suits 48 GB; 4-bit (~16 GB) is the right choice on a 32 GB Mac. LM Studio's "Likely too large" badge is a RAM warning, not an error.
If you are on an older LM Studio MLX runtime, update it (Settings → Runtime): qwen3_5
support landed in mlx-vlm 0.6.x, and older runtimes cannot load this architecture at all.
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 |