license: mit
base_model: orcarouter/GLM-5.3-Flash-Uncensored-FP8
base_model_relation: quantized
tags:

• gguf
• vector-quantization
• glm5-next
• q4_k
• dgx-spark
• abliterated
• uncensored
• experimental
  pipeline_tag: image-text-to-text

GLM-5.3-Flash Uncensored VQ GGUF (Q4_K nonexpert v3)

Experimental custom GVQ1 / GGUF v3 conversion of
orcarouter/GLM-5.3-Flash-Uncensored-FP8
(150bd5e3a957db747db1224cc4f8891bcfa7a7f0), the abliterated (refusal-removed)
build of Z.AI GLM-5.3-Flash in the official block-FP8 layout, for decode on
DGX Spark (GB10). A reader and runtime implementing FORMAT.md
and the q4_k_nonexpert_v3 recipe are required:
ds4-dfm-rs main from c5384b5e
(#86) on.
Standard llama.cpp/Ollama/Transformers loading is not claimed.

This artifact has the structure of
Baekpica/GLM-5.3-Flash-VQ-GGUF
at its final revision 4da527cd: the same 56 part names and 39,862 tensors
in the same order, with the same types, parts and payload sizes, built with
the same two recipes. Only the source weights differ. Because the part names
are identical, keep the two releases in separate directories.

Contents

• Routed experts are VQ: 36,288 matrices of layers 3–44 use
  eight-dimensional VQ, K8192, 12+1-bit indices, one E4M3 codebook per layer
  and F16 row gains (1.625 index bits per weight), recipe
  GLM-R1-uniform8192-native (RECIPE.md). All 42 codebooks
  were trained fresh on these weights: abliteration rewrote every routed
  down_proj, so the official codebooks were not reused.
• 316 nonexpert matrices are Q4_K (256-element / 144-byte blocks, no
  importance matrix), each derived from the source weights (FP8 times its
  block scale rounded to BF16, or BF16): KDA q/k/v/o (136), DSA
  q_a/q_b/kv_a/o (44), dense FFN of layers 0–2 (9), shared experts of
  layers 3–44 (126) and the output head (1).
  q4k-selection.json lists them; the manifest keeps
  every parent payload hash.
• Kept in source precision: embedding, DSA kv_b (BF16 latent attention),
  router and its bias, mHC maps, KDA gates, indexer, norms, vision and the
  MTP layer 45. Their bytes equal the source safetensors bytes.
• Tokenizer, chat template, processor, configuration and generation assets
  are byte-for-byte copies from the source revision. The chat template is
  therefore the Uncensored repository's (10,644 bytes), not the official
  template shipped with the base VQ release.

The 56 parts total 77,272,028,544 bytes. Decode reads about 7.5 GB of
weights per token, as in the base VQ release.

How it was built

1. All 72 source files were checked against the pinned Hub revision's
   SHA256 and Git blob hashes.
2. The GVQ1 converter used for the base VQ release, changed only in its
   source path and revision, trained and encoded the 42 routed-expert layers
   on an H100 and copied the other 3,532 tensors byte for byte into the
   native, MTP and vision parts. The release audit accounts for all 76,108
   source tensors: 36,288 FP8 scales consumed by VQ, every other tensor
   preserved.
3. ds4-dfm-rs tools/glm_vq_q4k.py and tools/glm_vq_select_q4.py
   (7ef77272) derived Q4_K as in the base release: a KDA-only parent (136
   matrices), a donor with all 316, and the selection of the other 180 into
   the parent.

Validation

• ds4-dfm-rs host validation (dump_validate, the --validate path,
  at 7ef77272) returns ok for the release directory. It identifies
  GLM-5.3-Flash-VQ with 56 parts and checks every part hash, GVQ1 header,
  codebook and FP8-scale link, Q4_K role and the full tensor layout. The
  source tokenizer and chat template load.
• Routed-expert VQ: full-matrix relative MSE per layer is
  0.143756–0.144532 (the official conversion: 0.143720–0.144518). Packed CPU
  and CUDA reconstructions are exactly equal on boundary, representative,
  worst-error and worst-peak rows of every VQ matrix.
• Q4_K: relative MSE over the 316 matrices is 0.00516 on average
  (0.00502–0.00814; the largest is a shared-expert down_proj).

These are weight-reconstruction errors, not model-quality scores.

Speed on DGX Spark (GB10)

Measured on the base release
Baekpica/GLM-5.3-Flash-VQ-GGUF
(4da527cd), not on this artifact, with ds4-dfm-rs main (3dd964ba):
one warm session per process, 2,048-token incremental prefill and 128 forced
greedy tokens per frontier from 2K to 64K. Curves are per-frontier medians;
bands are the observed min–max of three fresh runs with two embedded MTP
drafts (1,163.5 prefill / 35.08 decode tok/s).

This release has the same parts, tensor types and payload sizes, so it reads
the same weights per token. MTP gains still depend on draft acceptance on the
generated text, which can differ for this model.

Not measured on this artifact

No DGX Spark run was made on this artifact. The base release's quality
notes apply in kind and were not re-measured here: routed-expert VQ
measurably degrades quality (its held-out prose PPL rose from 4.8555 to
8.0797), and its Q4_K screen saw a Korean meeting-summary case degenerate
under greedy decoding. This is not a quality-equivalent replacement of the
source model.

Disclaimer

The source model had its safety alignment substantially removed by
abliteration; see its card. It can comply with harmful requests that the
original model refuses. It is meant for research such as refusal-mechanism
study, red-teaming and robustness evaluation. You are responsible for how you
use it and for everything it generates; add your own safety and moderation
layers before any deployment.

Integrity and licensing

files.json and SHA256SUMS give file sizes and
hashes. tensor-manifest.json gives per-tensor
precision, shapes, source-scale accounting and payload hashes;
source-files.json the verified source files and
native-tensors.json the byte-exact native tensors.
Verify them before loading.

Weights and assets: MIT, see LICENSE (Z.AI GLM-5.3-Flash, and the
MIT-licensed abliterated derivative). YoungAi algorithm attribution: see
LICENSE-YoungAi.