glm-5.3
Datasets
All datasets matching “glm-5.3”GLM-5.3-Flash-TR3-partsbin-v1
GLM-5.3-Flash TR3 parts bin v1 — K6 + K8 payload stores under one transform seed
This dataset is the parts bin for the GLM-5.3-Flash TR3 quantization
campaign (2026-08-27/28): the complete per-choice payload stores of the two
published uniform quants, plus the preparation artifacts and provenance
receipts that produced them.
malaiwah/GLM-5.3-Flash-TR3-6bpw (uniform K6)
malaiwah/GLM-5.3-Flash-TR3-8bpw (uniform K8)
What a parts bin is
TR3 (trellis) encoding is… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-TR3-partsbin-v1.GLM-5.3-Flash-BF16-Teacher-Logits
GLM-5.3-Flash BF16 teacher logits
This dataset contains full-vocabulary float32 teacher logits from the immutable
zai-org/GLM-5.3-Flash-BF16 revision a6c167b62691b2bac901344b65cb651a70f53e43.
It keeps the sealed final KLD panel qualification-only and publishes the
separate non-final calibration panel under role-specific paths.
Qualification-only final windows: 25
Qualification-only final prediction positions: 51175
Vocabulary size: 154880
Teacher receipt:… See the full description on the dataset page: https://huggingface.co/datasets/brandonmusic/GLM-5.3-Flash-BF16-Teacher-Logits.GLM-5.3-Flash-fidelity-suite-v1
GLM-5.3-Flash Fidelity Suite v1
Historical distribution-fidelity evidence for GLM-5.3-Flash (released 2026-08-26):
BF16-reference and FP8-as-served hidden-state captures, a shared LM head, and
receipts from the declared capture/replay path. Compatible candidate captures
can be compared on matching published positions without holding the 643 GB
reference; this is not a universal native-serving or task-quality score. Protocol: the Qwen3.8-27B fidelity-suite v5 methodology… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-fidelity-suite-v1.osworld-glm-5.3-flash-trajThese are the trajectory results from our GLM-5.3-Flash evaluation on OSWorld.
For detailed evaluation results, configuration, and additional information, please refer to the following GitHub issue:
https://github.com/xlang-ai/OSWorld/issues/591
GLM-5.3-BF16-full-logitsGLM-5.3-Flash-calibration-activations-v1
GLM-5.3-Flash calibration activations v1 (BF16, natural routing)
Per-layer block-input activations of zai-org/GLM-5.3-Flash-BF16 @ b1967181 over 92x2048
tokens of the exllamav3 standard_cal_data corpus (pinned): per context, layer_NNN.attn_in
and layer_NNN.mlp_in (bf16, post-norm linear inputs; mlp_in is the router + expert gate/up
input) and layer_NNN.router_logits (fp32, natural top-8 routing ground truth).
Per-expert Hessians E[xx^T], routing statistics and down-proj inputs… See the full description on the dataset page: https://huggingface.co/datasets/malaiwah/GLM-5.3-Flash-calibration-activations-v1.