base_model: orcarouter/GLM-5.3-Flash-Uncensored-FP8
base_model_relation: quantized
library_name: transformers
pipeline_tag: image-text-to-text
license: other
license_name: shapleymcg-license-1.0
license_link: https://github.com/brandonmmusic-max/shapleymcg/blob/main/LICENSE
tags:

• glm
• glm5_next
• exl3
• tr3
• vllm
• sm120
• dflash2
• multimodal
• uncensored
• abliterated
• moe
• shapleymcg
  quantized_by: neko-legends (Depths)

4× DGX Spark TP4 — our EXL3 quant of the uncensored GLM-5.3-Flash — August 29, 2026

176 GB EXL3 TR3 4bpw · 1M context · ~97 tok/s single-stream · DFlash2 k=7 spec decode · parity with aligned base

Encoded on 4 DGX Sparks in 4h22m (37,152 expert tensors) from orcarouter uncensored FP8 weights. Abliteration survives quantization — verified by discriminator probe.

I only post models I actually use. — neko-legends

GLM-5.3-Flash Uncensored — EXL3 TR3 4bpw

Licensing and attribution

This work includes or was produced using ShapleyMcg, created by Brandon M. Music
(https://github.com/brandonmmusic-max/shapleymcg). ShapleyMcg is licensed under the
ShapleyMcg License v1.0, an attribution-required license that grants no rights to
the person known as "0xSero." Use of ShapleyMcg without this attribution is unlicensed.

This checkpoint is a mixed-license artifact:

• Model weights chain — zai-org/GLM-5.3-Flash (MIT) → orcarouter uncensored
  FP8 derivative (MIT). The uncensored weights are redistributable under MIT terms.
• Calibration artifacts — the per-expert suh/svh/mcg scale tensors were
  inherited from the ShapleyMcg-calibrated checkpoint
  (brandonmusic/GLM-5.3-Flash-tr3-4bpw,
  mirrored by Mia-AiLab), and the trellis codes were produced by running the published
  R10 encoder closure. Those portions are licensed under the
  ShapleyMcg License v1.0,
  included in this repository as ShapleyMcg-LICENSE.
• Our contributions (the uncensored-weight re-encode, non-expert dequantization,
  verification, benchmarks, this card) may be used under either license above.

@misc{music2026shapleymcg,
  author = {Music, Brandon M.},
  title  = {ShapleyMCG: An Auditable Calibration-to-Encoding Pipeline for
            Low-Bit Mixture-of-Experts Models},
  year   = {2026},
  url    = {https://github.com/brandonmmusic-max/shapleymcg},
  note   = {Licensed under the ShapleyMcg License v1.0}
}

Intended use and status

This is an uncensored (abliterated) model: upstream refusal alignment has been
removed from the weights. It exists for the work where that matters — and that work
is overwhelmingly legitimate:

• Defending your own systems. Auditing, hardening, and penetration-testing
  infrastructure you own or are authorized to test — your home lab, homelab network,
  self-hosted services, and the software you run. Real adversarial testing needs a
  model that does not refuse to think like an attacker.
• Red-team and purple-team exercises under authorization, security research,
  adversarial evaluation, and defensive analysis of malicious content (phishing
  triage, malware analysis, social-engineering resistance training).
• Self-hosted deployment where a household or individual wants a model that
  behaves like a capable colleague, not a compliance department — on their own
  hardware, for their own purposes.

Please use responsibly. This model will help with things it should not be
used for. The operators of this repository publish it for authorized security
research and personal self-hosted use; users are responsible for complying with
applicable laws and the rules of any system they point it at. Do not use it
against systems you do not own or lack explicit permission to test.

No capability, safety, or bias evaluations beyond the benchmarks in this card
have been performed. Access is gated; by requesting access you accept these terms.

Format

Same format as Mia-AiLab/GLM-5.3-Flash-EXL3-TR3-4bpw
(quantization_config: bits 4, codebook mcg, scope glm53_routed_experts_only, exllamav3 0.0.43):

• Routed experts (288 × 42 MoE layers): EXL3/K4 trellis codes + fp16 suh/svh block scales
• Everything else (attention, NoPE MLA, shared experts, dense layers, embeddings): BF16,
  dequantized from the FP8 source (official-source-native policy)
• lm_head stays fp16/bf16

Quantization recipe

The 4-bit encode was produced with brandonmusic's published R10 encoder closure
(reproducibility/r10 from brandonmusic/GLM-5.3-Flash-tr3-4bpw):

• Scale vectors (suh/svh/mcg) inherited per-tensor from the Mia-AiLab base EXL3
  checkpoint — reusing its calibrated scale search (the uncensored weights are a small
  perturbation of base)
• Trellis codes re-encoded from the FP8-dequantized uncensored weights
• Identity covariance (no activation capture this run); pilot round-trip rel-err ≈ 6.75%
  per expert tensor
• Encoded in parallel across 4 Sparks: 37,152 tensors in 4h22m

Files

92 safetensors shards (175.6 GB) + config.json / quantization_config.json /
exl3-mcg-storage-abi.json / tokenizer files carried from the base EXL3 checkpoint
(tensor inventory verified identical: 150,226 tensors, 0 missing, 0 extra, 0 shape mismatches).

Credits

• zai-org — GLM-5.3-Flash
• orcarouter / dealignai lineage — the uncensored (abliterated) weights
• brandonmusic — EXL3/TR3 format + published R10 encoder closure
• MiaAI-Lab — the DGX Spark serving kit and base checkpoint this is built on
• Quantization + benchmarks by neko-legends (Depths agent), 2026-08-29