license: other
license_name: qwen-community-1.0
license_link: LICENSE
base_model:
- orcarouter/Qwen3.8-Flash-Next-Uncensored
base_model_relation: quantized
pipeline_tag: image-text-to-text
library_name: mlx
quantized_by: Navin-Models
language: - en
- zh
tags: - qwen
- qwen3.8
- qwen4-exp
- mlx
- uncensored
- abliterated
- mixture-of-experts
- vision-language
- function-calling
- reasoning
- mtp
- imatrix
- research
extra_gated_prompt: >-
This is an abliterated research model with substantially reduced safety alignment.
It may produce harmful, illegal, offensive, biased, or otherwise unsafe content.
Access is provided for legitimate research and controlled evaluation. You are responsible
for lawful use, downstream safeguards, and compliance with the Qwen Community License 1.0.
extra_gated_fields:
I have read and accept the included Qwen Community License: checkbox
I understand that safety alignment has been substantially removed: checkbox
I will use this release lawfully and take responsibility for appropriate safeguards: checkbox
Qwen3.8-Flash-Next-Uncensored AD-3.87bpw MLX
[!CAUTION]
Research artifact with substantially reduced safety alignment. This model is derived from an abliterated checkpoint and may comply with harmful, unethical, illegal, offensive, biased, or otherwise unsafe requests that the aligned model would refuse. It has no dependable built-in guardrails. Do not expose it to end users or production traffic without independently designed safety, moderation, access-control, logging, and abuse-prevention measures. You are responsible for how you use it and for compliance with applicable law.
A tensor-specific mixed MLX artifact for 64GB Macs, derived from orcarouter/Qwen3.8-Flash-Next-Uncensored at revision 8336e613ea508b13c2159bd0f68965d97a606b95.
The build preserves the 262,144-token model configuration, separate F16 vision tensors, a separate MTP section, and an external mmap-backed Q5_1 PLE table. Its allocation adapts AtomicChat's published Atomic Dynamic tensor-allocation philosophy to a custom MLX representation. The 3.87bpw name is this release's measured encoded model-payload average excluding the separately loaded F16 vision section; it is not an AtomicChat release identity.
This is an independent community build. It is not produced, endorsed, or warranted by Qwen, Alibaba, OrcaRouter, AtomicChat, MLX, or mlx-lm.
Important runtime status
This repository is a verified packed-weight artifact, not a drop-in mlx-lm model. The included runtime/admlx code provides the manifest-bound loader, canonical decoders, mmap PLE access, and direct MLX/Metal packed-linear kernel seam used during validation. It does not currently provide a turnkey end-to-end generation CLI for the full model.
Static memory accounting passes all four 262K scenarios—text, text with MTP, vision, and vision with MTP—for 64GB Macs. The heaviest projected Metal working set is 56,880,334,312 bytes, with a projected 10,765,400,600 bytes of physical memory remaining after the stated buffer and touched-PLE assumptions. A real full-model 262K runtime trial has not yet been performed, so the release verdict intentionally remains:
STATIC_PROVISIONAL_RUNTIME_PENDING
Do not interpret the static fit calculation as measured generation performance or a guarantee against memory pressure.
Contents
| Section | Tensors | Payload bytes | Format / role |
|---|---|---|---|
| Resident body | 1,166 | 47,578,244,504 | Mixed IQ2_XS, IQ2_S, IQ3_S, Q8_0 and exact structural tensors |
| External PLE | 128 | 38,400,184,320 | Canonical Q5_1 shards, mmap-backed |
| MTP | 31 | 902,321,776 | Separate draft section; target importance matrix not applied |
| Vision | 333 | 897,862,112 | Separate F16 section |
| Total | 1,658 | 87,778,612,712 | Complete encoded payload |
Measured effective average:
- model payload excluding separate F16 vision: 3.8710216579 bpw → release name 3.87bpw
- complete package including separate F16 vision: 3.9012720779 bpw
Tensor allocation
- routed gate/up: IQ2_XS, IQ2_S, or IQ3_S selected per layer by measured importance-weighted reconstruction error
- routed down: IQ2_S or IQ3_S selected per layer
- sensitive tensors: Q8_0
- PLE n-gram table: external canonical Q5_1 shards
- vision: separate F16
- MTP: separate mixed section; no target-only importance-matrix reuse
- structural integer tensors: exact I64 preservation
Four consecutive 640-wide routed-down logical rows are packed as one 2,560-element canonical encoder row. Consumers must honor logical_rows_per_canonical_row in artifact-manifest.json.
Verification
The terminal verifier passed with no reported problems:
- exactly 1,658 unique tensors
- missing tensors: 0
- extra tensors: 0
- section overlap: 0
- exhaustive distributed-file SHA-256 inventory
- decode checks for IQ2_XS, IQ2_S, IQ3_S, Q8_0, Q5_1, F16, and I64
- exact source equality for all three structural I64 tensors
- static 262K budget checks for all four text/vision and MTP combinations
See:
artifact-manifest.json— tensor names, sections, shapes, paths, formats, and payload checksumsSHA256SUMS— release-file SHA-256 inventoryevidence/CONVERSION_VERIFICATION.json— terminal conversion verifier resultevidence/FINAL-V7-TERMINAL-INVENTORY.json— archived final inventory summaryRELEASE_COMPLETE.json— release-tree identity and counts
Verify after download:
shasum -a 256 -c SHA256SUMS
Runtime integration
The files under runtime/admlx are the exact runtime primitives distributed with this release. Minimal manifest loading:
from pathlib import Path
from runtime.admlx.runtime_loader import ArtifactLoader
root = Path("Qwen3.8-Flash-Next-Uncensored-AD-3.87bpw-MLX")
artifact = ArtifactLoader(root / "artifact-manifest.json")
linear = artifact.load_qlinear("lm_head.weight")
This demonstrates manifest-bound tensor loading; it is not a full generation example. A full consumer must assemble the complete Qwen4Exp architecture, attach the vision and MTP paths, implement the cache policy, and preserve mmap behavior for the external PLE table.
Suggested Python dependencies for the included primitives:
mlx==0.32.2
numpy
Provenance and method
- source checkpoint:
orcarouter/Qwen3.8-Flash-Next-Uncensored - pinned source revision:
8336e613ea508b13c2159bd0f68965d97a606b95 - source inventory: 131 weight shards, 360,000,192,888 bytes, 1,658 tensors
- llama.cpp revision used for the importance-matrix and canonical-format toolchain:
d1a92352cbd417fd840b4e765c0b82f5fe3d1d89 - MLX version:
0.32.2 - mlx-lm revision used during development:
c62d7957064058b3f00cac9a3e1fe00cbc23ab68 - importance-matrix SHA-256:
5591ce3dc3bf0b73d3c074bc588c90b6c4f7b3c273de6b10e50d111b75f05487
The conversion was resumable and collision-safe across 18 workers. Worker-local transaction journals, independent atomic tensor writes, SHA-256-verified merge, existing-output reuse, and corruption recovery were tested before production conversion. Of 1,658 tensors, 1,192 were generated by the parallel workers and 466 verified tensors were reused from earlier valid progress.
Attribution
The AD label acknowledges adaptation of AtomicChat's Atomic Dynamic tensor-allocation philosophy and its published 64GB Mac memory guidance. This repository uses a different custom MLX representation and its own measured 3.87bpw allocation; it is not AtomicChat's AD-3.84 artifact.
- AtomicChat reference:
AtomicChat/Qwen3.8-Flash-Next-GGUF - uncensored source checkpoint:
orcarouter/Qwen3.8-Flash-Next-Uncensored - original model family:
Qwen/Qwen3.8-Flash-Next
License
The model and its derivatives are distributed under the included Qwen Community License 1.0. Review LICENSE before use. The source checkpoint's model card previously displayed Apache-2.0 metadata while its distributed license file contained the Qwen Community License; this release follows the actual included license text and uses license: other metadata.
This summary is not legal advice. Users are responsible for determining whether their intended use complies with the license and applicable law.