# Qwen3.8 27B Uncensored MLX

URL: https://interfaze.ai/models/orcarouterqwen38-27b-uncensored-mlx

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Qwen3.8 27B Uncensored MLX by orcarouter, a image-text-to-text model with multimodal capabilities. Understand and compare multimodal features, benchmarks, and capabilities.

## Comparison

| Feature | Qwen3.8 27B Uncensored MLX | Interfaze |
| --- | --- | --- |
| Input Modalities | text, image, video | image, text, audio, video, document |
| Native OCR | No | Yes |
| Long Document Processing | No | Yes |
| Language Support | 201 partial | 162+ |
| Native Speech-to-Text | No | Yes |
| Native Object Detection | No | Yes |
| Guardrail Controls | No | Yes |
| Context Input Size | 262.1K | 1M |
| Tool Calling | Yes | Tool calling supported + built in browser, code execution and web search |

### Scaling

| Feature | Qwen3.8 27B Uncensored MLX | Interfaze |
| --- | --- | --- |
| Scaling | Self-hosted/Provider-hosted with quantization | Unlimited |

[Try Interfaze](https://interfaze.ai/dashboard)[Read the Docs](https://interfaze.ai/docs)

View model card on [Hugging Face](https://huggingface.co/orcarouter/Qwen3.8-27B-Uncensored-MLX)

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> An **abliterated** (refusal-removed) build of [`Qwen/Qwen3.8-27B`](https://huggingface.co/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-MLX` loads 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](https://www.orcarouter.ai/models). This model is deployed as API [here](https://www.orcarouter.ai/models/qwen/qwen3.8-27b).

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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](https://www.apache.org/licenses/LICENSE-2.0)** 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.

> **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.

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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


hf download orcarouter/Qwen3.8-27B-Uncensored-MLX --include "4-bit/*" \
    --local-dir ./Qwen3.8-27B-Uncensored-MLX


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


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


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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## 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](https://github.com/lmstudio-ai/mlx-engine/issues/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.

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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 |

## Want more deterministic results?

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