orcarouter/Qwen3.8-27B-Uncensored-MLX

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

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.

• 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.

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.)

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_enabled flag on the main model alone does nothing — MLX needs the separate drafter passed via --draft-model … --draft-kind mtp.

# 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