GLM MoE DSA: a tiny toolchain fixture

A small, independently initialized random-weight reference model for learning, debugging, and reproducing QFS capture workflows. It is a base/root fixture, not a quantized child.

These weights are untrained, not an assistant, and not a language-quality benchmark. Generated text has no useful semantic quality. No upstream trained weights or training data are implied by an architecture name.

Weight lineage

This checkpoint was initialized directly for an architecture test. It was not fine-tuned from a production model and does not inherit that model's trained weights. Architecture/configuration lineage is documented separately from weight lineage.

Browse the Random Architecture Fixtures collection. A random base may also serve as the common source for an aligned quantization family.

At a glance

Size is serialized artifact size, not runtime RAM or a capacity/performance guarantee. Packed-array element counts are not model parameter counts.

What is actually included

Four text decoder layers (one dense, three MoE), MLA attention, alternating full/shared DSA indexers, 8 routed experts/top-2 plus one shared expert, and a complete untied vocabulary head. Native FP32 router buffers are retained; no MTP.

The repository file inventory includes config.json, generation_config.json, tokenizer.json, tokenizer_config.json, build-manifest.json, requirements-cpu.txt. Exact build/runtime/license inventories are linked below. A file may describe historical provenance without being an executable entry point.

Good community uses—and boundaries

• Learn how to download pinned artifacts, seal a small synthetic token panel, capture hidden states and replay a full vocabulary head.
• Debug model-family adapters, strict tensor loading, storage decoders, or reproducibility tooling without downloading a production-sized checkpoint.
• Reproduce a narrowly scoped result, report an adapter/reader regression, and retain the source, panel and runtime identities needed to explain it.

Not established: trained-model accuracy; useful instruction following; quantizer optimization quality; original production-weight compatibility; GPU/NPU/serving-kernel parity; cross-hardware determinism; long-context behavior outside the recorded panel; throughput or paid-compute admission.

Additional build caveats:

• Random initialization; not trained or distilled.
• Not a quantization or behavioral approximation of a released GLM model.
• MTP intentionally absent (zero next-n-prediction layers); no unmatched MTP tensors.
• This build manifest describes generated bytes, not proof of clean loading or capture; see separately measured verification evidence.

Model family, root dataset and evidence

This model is the base/root of its own random fixture family, not a reproduction of the trained upstream model. The fidelity-root dataset repository is the family reference location. A link is not a claim that registration or publication has completed.

Related child fixtures: ct-mxfp4, ct-nvfp4, modelopt-nvfp4, fp8-ue8m0, modelopt-mixed, q8_0, q4_0, fp8-block32.

The historical CPU evidence bundle retains two independent captures at first/ and repeat/, plus comparison/. Its repository root is a receipt bundle, not a single canonical QFS dataset. Those recorded same-machine/same-stack forced comparisons reported 0.0 nats on 252 synthetic scored positions; that is not a trained-quality result or a transferable hardware floor. The original detailed receipt and caveats remain authoritative.

Runtime requirements and safe local reproduction

Replay the published evidence without loading a model

This uses QFS's existing NumPy FP64 comparator in a Torch-free environment. It reads the stored hidden states and each side's own head. No model forward, remote model code, GPU, upload or registry mutation is involved. Runtime receipts name the actual backend; last-bit differences from another FP64 implementation are not a new quality claim. Synthetic exact controls remain zero.

Set QFS to a reviewed Quant Fidelity Suite checkout and use Bash:

: "${QFS:?Set QFS to your reviewed QFS checkout}"
WORK=$(mktemp -d)
export OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2
python3.12 -m venv "$WORK/replay-env"
"$WORK/replay-env/bin/pip" install 'numpy==2.5.3' 'huggingface-hub==1.30.0'
"$WORK/replay-env/bin/hf" download malaiwah/qfs-fixture-root-captures-v1 --repo-type dataset \
  --revision f53b204091c988ce4a2161af81886f3018745559 --include 'roots/glm_moe_dsa/**' --local-dir "$WORK/evidence"
"$WORK/replay-env/bin/python" "$QFS/bin/fidelity_dataset.py" verify \
  "$WORK/evidence/roots/glm_moe_dsa/first" --verify-tensors
"$WORK/replay-env/bin/python" "$QFS/bin/fidelity_dataset.py" verify \
  "$WORK/evidence/roots/glm_moe_dsa/repeat" --verify-tensors
"$WORK/replay-env/bin/python" "$QFS/bin/fidelity_dataset.py" compare \
  --reference "$WORK/evidence/roots/glm_moe_dsa/first" \
  --candidate "$WORK/evidence/roots/glm_moe_dsa/repeat" \
  --out "$WORK/replayed" --device cpu --replay-device numpy --replay-dtype float32 \
  --vocab-chunk 8192 --verify-tensors --self-compare --force-compute

Reconstructed format comparisons are intentionally advisory and normally return exit code 2 while writing a valid receipt. Inspect that receipt; do not silence refusals or interpret an advisory result as a production-quality ranking.

Capture the actual checkpoint

Capture uses a separate pinned Torch CPU environment. The input below is the original token-panel format, not a sealed capture's internal panel/ folder. The native source, tokenizer and model revisions remain explicit. Set AUTHOR to your own HF handle; the dataset repository argument is attribution only and nothing is uploaded by these commands.

: "${AUTHOR:?Set AUTHOR to your Hugging Face handle}"
"$WORK/replay-env/bin/hf" download malaiwah/glm-moe-dsa-tiny-random-bf16 --revision a1a973330e4e912678adbd8ea41e8b86b1c9dccb --local-dir "$WORK/model"
"$WORK/replay-env/bin/hf" download malaiwah/glm-moe-dsa-tiny-random-bf16 --revision a1a973330e4e912678adbd8ea41e8b86b1c9dccb --local-dir "$WORK/source"
"$WORK/replay-env/bin/hf" download malaiwah/qfs-fixture-root-captures-v1 --repo-type dataset \
  --revision f53b204091c988ce4a2161af81886f3018745559 --include 'requirements-capture.txt' --include 'roots/glm_moe_dsa/input-panel/**' \
  --local-dir "$WORK/inputs"
python3.12 -m venv "$WORK/capture-env"
"$WORK/capture-env/bin/pip" install -r "$WORK/inputs/requirements-capture.txt"
"$WORK/capture-env/bin/python" "$QFS/bin/fidelity_dataset.py" architectures prepare \
  --architecture glm_moe_dsa --model-dir "$WORK/model" \
  --model-repository malaiwah/glm-moe-dsa-tiny-random-bf16 --model-revision a1a973330e4e912678adbd8ea41e8b86b1c9dccb \
  --panel "$WORK/inputs/roots/glm_moe_dsa/input-panel" --tokenizer-root "$WORK/source" \
  --author "$AUTHOR" --dataset-repository "$AUTHOR/glm-moe-dsa-tiny-random-bf16-capture" \
  --dataset-id "fidelity--$AUTHOR.glm-moe-dsa-tiny-random-bf16" --out "$WORK/workflow"

Inspect workflow.json. This launcher executes its exact capture/verification commands and selects the Torch-free interpreter only for the final comparison:

export OMP_NUM_THREADS=2 MKL_NUM_THREADS=2 OPENBLAS_NUM_THREADS=2
"$WORK/replay-env/bin/python" - "$WORK/workflow/workflow.json" <<'PY'
import json, subprocess, sys
workflow = json.load(open(sys.argv[1]))
for step in workflow["commands"]:
    argv = list(step["argv"])
    if step["step"] == "compare":
        argv[0] = sys.executable
    result = subprocess.run(argv)
    if result.returncode:
        raise SystemExit(result.returncode)
PY

Use the actual root at malaiwah/glm-moe-dsa-tiny-fidelity-root-v1@b8ed427805abdfd6b8b20db10ca5107e840d0497. The community collection groups models and captures. Custom code, where required above, is explicitly pinned and executed only after your consent; hash verification is provenance, not a sandbox.

Licensing and detailed provenance

The fixture repository retains its mit license in LICENSE. This covers the fixture only according to that exact text; architecture inspiration is not relicensing of upstream weights, cards, code, or configuration. Dependencies retain their own licenses (native Transformers implementation: Apache-2.0). No upstream trained tensors are claimed to be copied.

Immutable provenance, historical cards, source inventories and full caveats

• Model/card snapshot used while authoring: malaiwah/glm-moe-dsa-tiny-random-bf16@a1a973330e4e912678adbd8ea41e8b86b1c9dccb.
• Full preserved earlier model card, SHA-256 7921e3c1d32b3fc7b9541320459c5803627778aa0458733ab13ae46729c37c6b. This is an immutable historical record, including original build pins, measured resource tables, command logs, limitations and failed-attempt provenance. Historical publisher commands are not part of the local-use recipe above.
• build-manifest.json.
• qfs-source.json.
• cpu-reproduction-summary.json.

Selected original provenance fields (full tensor/component evidence remains in the linked inventories):

{
  "architecture_inspiration": "GLM-5.2-SIQ-Fruit-bf16 geometry and dense/MoE plus full/shared DSA schedules; no Fruit or GLM model weights reused.",
  "generation": "Native GlmMoeDsaForCausalLM random initialization in FP32, rounded to the published BF16 artifact; native router correction buffers retained FP32.",
  "generator_sha256": "19c3489f1bc7e8e9323cb26afbead562816a6918d6823af252eb6d04dff8e81e",
  "limitations": [
    "Random initialization; not trained or distilled.",
    "Not a quantization or behavioral approximation of a released GLM model.",
    "MTP intentionally absent (zero next-n-prediction layers); no unmatched MTP tensors.",
    "This build manifest describes generated bytes, not proof of clean loading or capture; see separately measured verification evidence."
  ],
  "parameter_count": 277824,
  "seed": 20260907,
  "versions": {
    "safetensors": "0.8.0",
    "tokenizers": "0.23.2",
    "torch": "2.11.0+cpu",
    "transformers": "5.16.1"
  }
}