ddark-il/Qwen3.8-27B-Uncensored

image-text-to-text model by ddark-il

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A mixed-precision MLX affine quantization of orcarouter/Qwen3.8-27B-Uncensored (27.8 B dense, 64 layers, hybrid GatedDeltaNet + full attention, 256 K context, MTP head, vision tower), built to replicate the recipe of True2456/Qwen3.8-27B-AWQ-5.0bpw and tuned for oMLX on M5 (NAX tensor unit). 17.38 GB · 5.006 bpw · 3.2× smaller than bf16 map and its rationale, the group-size-64 argument for oMLX's NAX prefill path, the no-3-bit constraint (oMLX's qwen35prefill kernels ship q4/q5/q6/q8 only), and the MTP and norm-convention findings. Built on their mlx-compress. Qwen3.8-27B-Uncensored (abliterated fine-tune, revision 11a200330368fd04fa7b2f0b1e7f20bd819cf621). No 3-bit anywhere, and gs64 on everything that can route — gs128 is refused by oMLX's NAX path when the tensor unit is available. reldamp 1e-2, Hessians from real prefill activations of this checkpoint. Measured on the stored bytes: 48/48 attention modules improved, median −32 % H-weighted output error, +31.2 % aggregate. It re-rounds on the grid the export already chose, so only packed…

ddark-il/Qwen3.8-27B-Uncensored on Hugging Face A short preview, not the full document.

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  1. Qwen3.8-27B-Uncensored — MLX 5.0 bpw (gs64, NAX-first)
  2. Credits
  3. Per-module widths
  4. How this differs from the recipe it replicates
  5. Running it
  6. Measured quality
  7. License

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Model overview

ddark-il/Qwen3.8-27B-Uncensored is a image-text-to-text model repository published by ddark-il on Hugging Face. The source reports the mlx library.

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Model facts

Task
image-text-to-text
Library
mlx
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
27,781,427,952
Architecture
Qwen3_5ForConditionalGeneration
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-10-25T06:13:39.000Z
Last modified
2026-10-25T06:30:59.000Z

Compatibility and lineage

Language coverage was not reported.

Reported base models: orcarouter/Qwen3.8-27B-Uncensored

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Source: Hugging Face Hub. Metadata observed 2026-10-25T06:46:38.979Z. Daily imports are snapshots, not real-time monitoring.

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