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sahilchachra/orcarouter-Qwen3.8-27B-Uncensored-MXFP4

🤗 Hugging Face sourceimage-text-to-textapache-2.027.4B params55 GBsafetensors✓ 14 checksumsupdated today
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orcarouter-Qwen3.8-27B-Uncensored — MLX MXFP4

MLX MXFP4 quantization of orcarouter/Qwen3.8-27B-Uncensored, an uncensored fine-tune of Qwen3.8-27B. Qwen3.8-27B is a qwen3_5 vision-language model with a hybrid GatedDeltaNet linear-attention + full-attention text backbone (64 layers, full attention every 4th) and a Qwen3-VL vision tower. Runs on Apple Silicon via mlx-vlm. Stays image-text-to-text — the vision tower is kept in bf16; only the text backbone is quantized.
Precision MXFP4 (E2M1 + E8M0 shared scale, group size 32)
Bits per weight 4.449 bpw
On-disk size 14 GB (13 shards)
Quantized text backbone (incl. the ~1.27B lm_head)
Kept in bf16 Qwen3-VL vision tower

Quantizations

Variant Bits Size
orcarouter-Qwen3.8-27B-Uncensored-MXFP4 4.449 bpw 14 GB ← this repo
orcarouter-Qwen3.8-27B-Uncensored-MXFP8 8.381 bpw 27 GB higher fidelity / for 32 GB+

Verification

Smoke-tested on Apple Silicon via mlx-vlm with deterministic greedy decoding, inspecting raw token IDs (not just detokenized text):

Text — coherent and correct:

  • "What is the capital of France?" → "The capital of France is Paris."
  • "What is 25 + 17?" → coherent step-by-step arithmetic.

Vision (the bf16 vision tower, through the quantized text backbone):

Image Question Answer
solid red main color? Red ✅
solid blue main color? Blue ✅
green circle shape and color? "green circle" ✅

Color and shape are read correctly — the vision path is live.

Usage (mlx-vlm)

pip install -U mlx-vlm   # needs the qwen3_5 architecture (>= 0.6.12)

from mlx_vlm import load, generate
from mlx_vlm.prompt_utils import apply_chat_template

model, processor = load("sahilchachra/orcarouter-Qwen3.8-27B-Uncensored-MXFP4")
config = model.config

prompt = apply_chat_template(processor, config, "What is the capital of France?")
print(generate(model, processor, prompt, max_tokens=256, verbose=True))

This is a reasoning model; it produces a <think> channel before its answer.

Run in LM Studio

Loads and runs in LM Studio (tested on 0.4.20, mlx-llm runtime): the qwen3_5 architecture is recognized, the model indexes cleanly (format: mlx present), and the ChatML template runs as-is. This is a reasoning model — it emits a thinking channel (reasoning_content) before the final content, so give it enough max_tokens (e.g. 200+) or the answer can be empty while it is still thinking. Verified: "capital of France" → reasoning + content = "Paris".

Loads on a 24 GB Mac (14.2 GiB weights + small context).

Notes & limitations

  • Uncensored model. This is a deliberately uncensored/abliterated derivative and will not refuse requests the way the original might. Use responsibly and in line with the base model's license and your local laws.
  • MTP head dropped. The base model's multi-token-prediction (speculative decoding) head is not included — mlx-vlm strips the mtp.* weights on load. Text and vision are unaffected; only spec-decode is not available.
  • tie_word_embeddings=false, so the ~1.27B lm_head is a separate matrix and is quantized. Verified correct on the MXFP4 build above.
  • Inherits all capabilities and limitations of the base model. See the original model card.
  • Quantized by @sahilchachra with MLX. Apache-2.0.