{
  "capture": {
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    "dtype": "BF16",
    "dtype_lossless": true,
    "form": "hidden",
    "head_not_separable_reason": null,
    "head_separable": true,
    "hidden_width": 6144,
    "lossy_codec": null,
    "manifest_file": "capture/manifest.json",
    "manifest_file_sha256": "8234e9f154d04812714fb58d8d05d91b200545c16ff1ad7904a96e171221b0bc",
    "records_count": 25,
    "scored_rows_total": 51175,
    "semantic_point": "after_final_rmsnorm_before_lm_head",
    "tensor_key": "hidden_states",
    "total_size_bytes": 628845100,
    "vocab_size": 154880
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  "coverage": {
    "complete": true,
    "declared_records": 25,
    "index_range": [
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    "missing_indices_sample": [],
    "present_records": 25,
    "shard_of": null,
    "subset_detail": null
  },
  "created_utc": "2026-09-05T12:21:34Z",
  "dataset": {
    "author": {
      "handle": null,
      "is_registry_maintainer": false,
      "name": "malaiwah",
      "role": "capture-author",
      "url": null
    },
    "base_capture": null,
    "id": "fidelity--glm53.malaiwah.quant.nvfp4-incoai",
    "license": "mit",
    "name": "fidelity--glm53.malaiwah.quant.nvfp4-incoai",
    "qualification": null,
    "repository": "malaiwah/glm53-fidelity-nvfp4-incoai-v1",
    "revision": null,
    "role": "quant",
    "structural_status": "sealed"
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    "distinct_evidence_hash_count": 1,
    "evidence_hashes": [
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    "evidence_kind": "hidden_state_tensor_sha256",
    "identical_across_runs": null,
    "note": "a single cold capture; cross-run determinism is NOT asserted here. Capture a second cold run and run `fidelity-dataset compare --self-compare` -- an exactly 0.0 result is the SC-1 reproduction confirmation.",
    "repeat_noise": null,
    "repeats": [],
    "run_count": 1
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  "disclosures": [
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      "affects_comparability": false,
      "code": "no_known_deviations",
      "detail": "captured by engines/tools/hf_capture.py",
      "severity": "info"
    },
    {
      "affects_comparability": false,
      "code": "checkpoint_tensors_intentionally_unused",
      "detail": "791 checkpoint tensor(s) were present but intentionally unused by this architecture. Their complete set exactly matched pinned allowlist 969cee605deba10dd82afbaa9b1b7a35d2339fdb122efece14530c9030aa1436 (canonical sorted-name SHA-256 61e5f26aed8bca408c5de5347d8e1668b0c5716237dad1fc98c47bc108f4ae57).",
      "severity": "caveat"
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    {
      "affects_comparability": false,
      "code": "activation_scales_not_applied",
      "detail": "the artifact ships a static per-tensor `input_scale` beside each of its 57600 routed-expert modules (a W4A4 serving kernel would quantize activations with it); this is a weights-only decode-and-run measurement, 57600 such tensors were read and NOT applied (activation_scheme static-nvfp4-not-applied), so activation quantization is not captured by the measured KLD -- the same caveat family as the official FP8 release's dynamic activation scheme.",
      "severity": "caveat"
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    {
      "affects_comparability": false,
      "code": "reduced_run_count",
      "detail": "this dataset is ONE cold capture (run_count 1, the DET-D4 floor is 5). Cross-run determinism is not asserted by the dataset; it is established by a second cold capture plus `fidelity-dataset compare --self-compare`, whose exactly-0.0 result is the SC-1 reproduction confirmation.",
      "severity": "caveat"
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    "enforced": true,
    "entropy_nats": 3.6383363063160545,
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    "expected_first_ids": [
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    "logit_spread": 26.375,
    "note": "one extra window through the same schedule; its hidden state is discarded and never enters the dataset.",
    "prompt": "The capital of France is",
    "schema": "malaiwah.generation-sanity-probe.v1",
    "status": "pass",
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    "top1_text": " Paris",
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        "text": " Paris"
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        "probability": 0.09340419865211294,
        "text": " one"
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        "text": " the"
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      {
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        "probability": 0.03893666289657242,
        "text": " home"
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    "uniform_entropy_nats": 11.950405902510404,
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    "bits": 16,
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    "dtype": "BF16",
    "equality_receipt": null,
    "file": "head/weight.safetensors",
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      "file_sha256": null,
      "shape": null,
      "tensor_content_sha256": null,
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    "head_json": "head/head.json",
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    "compatible_with": [
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    "divergences": [
      {
        "field": "head",
        "id": "D-1",
        "reason": "every capture declares its OWN head identity by tensor content digest; the comparator refuses a hidden-form comparison across differing heads."
      },
      {
        "field": "runtime.lane",
        "id": "D-2",
        "reason": "lane is required and gated at compare time so registry BIAS-006 is checked before a number exists, not at submission."
      },
      {
        "field": "panel.scoring_window",
        "id": "D-3",
        "reason": "scoring_window is part of panel identity, not a comparator flag: a score_from=1024 number is a different panel, not a variant."
      },
      {
        "field": "comparison_kind",
        "id": "D-4",
        "reason": "self-compare is a first-class mode asserting exactly 0.0."
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      {
        "field": "estimator.accumulation_dtype",
        "id": "D-5",
        "reason": "required, fp64 default; an fp32 estimator lands in a different comparability class on the same data and that must be visible."
      },
      {
        "field": "coverage",
        "id": "D-6",
        "reason": "declared vs present record counts with shard_of; fixes our own manifest that claimed 5,120 captures in a 512-file repository."
      },
      {
        "field": "panel",
        "id": "D-7",
        "reason": "the panel is a separately referenceable object, which is what makes a candidate capture publishable standalone."
      },
      {
        "field": "capture.lossy_codec",
        "id": "D-8",
        "reason": "lets a llama.cpp .kld be ingested without laundering a lossy capture as exact."
      },
      {
        "field": "capture.records[].attention_mask_sha256",
        "id": "D-9",
        "reason": "our packed and streaming lanes vary mask construction; a single-request capture path makes it invariant and has no equivalent field."
      },
      {
        "field": "capture.head_separable",
        "id": "D-10",
        "reason": "hidden form is the default; logit form must declare head_separable:false with a reason."
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    "k3_compat_emitted": false,
    "note": null
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      "checked": false,
      "hits": null,
      "method": "not established",
      "receipt": null
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    "context_length": 2048,
    "contexts": 25,
    "panel_file": "panel/panel.json",
    "panel_file_sha256": "a71d495140a7fc53387547d4cc87d610232c57303a059b57a62fd4f9333f4a4f",
    "panel_id": "panel--glm53.malaiwah.corpus5x5-v1",
    "panel_receipt_file": "panel/panel-receipt.json",
    "panel_receipt_sha256": "9c3bc4f59e8825ac78b366ca0e2988a48ecdbcaf26fdaeaedd3251edb6f9a828",
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    "repository": null,
    "revision": null,
    "scored_positions_total": 51175,
    "scoring_window": {
      "dropped_positions_total": 0,
      "min_left_context_tokens": 1,
      "policy": "every causal prediction position of every window is scored; nothing is dropped",
      "score_from": 0,
      "windowed": false
    },
    "suite_token_hash_sha256": "f09ee395f635225a077695ed193d61f7d1e70650cebe1b68f82b16f59d399f86",
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        {
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        {
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          "name": "generation_config.json",
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        {
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      "files_verified": true,
      "id": "zai-org/GLM-5.3-BF16",
      "identity_sha256": "b18e4d378be7e75fd2b323f7c81cc65640f6a08e1842d3bf4e025d88a9d78bf9",
      "maximum_token_id_exclusive": 154820,
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      "repository": "zai-org/GLM-5.3-BF16",
      "revision": "304b8051cfb2b260b61ce0cbe330e02a98e73639",
      "vocab_size": 154820
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  },
  "runtime": {
    "backend_identity_sha256": null,
    "file": "runtime/capture-runtime.json",
    "file_sha256": "6bbba771d9a1a75d3b4ca262a3fdd9fe61f6f317de65351d3d16c397f58404b8",
    "lane": "streaming",
    "lane_identity_sha256": "025ae83c1cb0e2c518baa8bd0c46965f0c17b6aaca5b1b875ca5fcea71f565d5",
    "lane_inferred": false,
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    "source": "native",
    "stack_fingerprint_sha256": "e7ddf6b280474c1709836e227b55dda1247b32148313bc87fd88a2ada704f0e0"
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  "schema": "malaiwah.fidelity-dataset.v1",
  "scope": {
    "assignments": [
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "78 tensors: model.layers.N.self_attn.o_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "attn.o",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "261 tensors: model.layers.N.self_attn.indexer.k_norm.bias, model.layers.N.self_attn.indexer.k_norm.weight, model.layers.N.self_attn.indexer.weights_proj.weight, model.layers.N.self_attn.indexer.wk.weight, model.layers.N.self_attn.indexer.wq_b.weight, model.layers.N.self_attn.kv_a_layernorm.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "attn.other",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "312 tensors: model.layers.N.self_attn.kv_a_proj_with_mqa.weight, model.layers.N.self_attn.kv_b_proj.weight, model.layers.N.self_attn.q_a_proj.weight, model.layers.N.self_attn.q_b_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "attn.qkv",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "1 tensors: model.embed_tokens.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "embed_tokens",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "1 tensors: lm_head.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "lm_head",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "3 tensors: model.layers.N.mlp.down_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "mlp.down",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "3 tensors: model.layers.N.mlp.gate_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "mlp.gate",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "3 tensors: model.layers.N.mlp.up_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "mlp.up",
        "treatment": "native"
      },
      {
        "bits_per_weight": 4.0,
        "format": "nvfp4",
        "layer_range": "all",
        "note": "57600 tensors: model.layers.N.mlp.experts.E.down_proj.weight, model.layers.N.mlp.experts.E.gate_proj.weight, model.layers.N.mlp.experts.E.up_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "moe.experts",
        "treatment": "quantized"
      },
      {
        "bits_per_weight": null,
        "format": "mixed",
        "layer_range": "3-77",
        "note": "class mixes formats on the same layers (SCOPE-004 admits one row per class and layer_range): 75 x native:bf16@16 (model.layers.N.mlp.gate.weight); 75 x native:fp32@32 (model.layers.N.mlp.gate.e_score_correction_bias). read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "moe.router",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "225 tensors: model.layers.N.mlp.shared_experts.down_proj.weight, model.layers.N.mlp.shared_experts.gate_proj.weight, model.layers.N.mlp.shared_experts.up_proj.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "moe.shared_expert",
        "treatment": "native"
      },
      {
        "bits_per_weight": null,
        "format": "mixed",
        "layer_range": "78",
        "note": "class mixes formats on the same layers (SCOPE-004 admits one row per class and layer_range): 790 x native:bf16@16 (model.layers.N.eh_proj.weight, model.layers.N.enorm.weight, model.layers.N.hnorm.weight); 1 x native:fp32@32 (model.layers.N.mlp.gate.e_score_correction_bias). read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "mtp",
        "treatment": "native"
      },
      {
        "bits_per_weight": 16,
        "format": "bf16",
        "layer_range": "all",
        "note": "157 tensors: model.layers.N.input_layernorm.weight, model.layers.N.post_attention_layernorm.weight, model.norm.weight. read from incoai/GLM-5.3-NVFP4@54e52520606f96b3d9fc84088ad22882a61648ac model.safetensors.index.json + every shard header (ranged HTTP): a routed expert projection is nvfp4 (e2m1, group 16 along the input axis, weight U8 [out, in/2] + weight_scale F8_E4M3 [out, in/16] + weight_scale_2 F32 scalar; 57600 such modules) when it ships the modelopt component set, native at its stored dtype when it ships whole; every other tensor native at its stored dtype. Declared by quantization_config: quant_method modelopt, quant_algo NVFP4, config_groups.group_0.weights num_bits=4 group_size=16, producer modelopt 0.45.0. 57600 modules also ship a per-tensor F32 input_scale (a static activation quantity: recorded, never applied by the weights-only decode).",
        "tensor_class": "norm",
        "treatment": "native"
      }
    ],
    "head_policy": "native",
    "kv_cache_dtype": "bf16",
    "policy": "mixed",
    "scope_digest": "attn.o=native:bf16@16|attn.other=native:bf16@16|attn.qkv=native:bf16@16|embed_tokens=native:bf16@16|lm_head=native:bf16@16|mlp.down=native:bf16@16|mlp.gate=native:bf16@16|mlp.up=native:bf16@16|moe.experts=quantized:nvfp4@4|moe.router=native:mixed|moe.shared_expert=native:bf16@16|mtp=native:mixed|norm=native:bf16@16|head=native|kv=bf16"
  },
  "seal": {
    "checksums_file": "checksums.txt",
    "checksums_format": "sha256sum",
    "checksums_sha256": "3ad756cfd2d44b2fe30b621ce02c95d8b97c2a763b0567034b90207aa99f88f8",
    "covers": [
      "every published file except checksums.txt and fidelity-dataset.json, via checksums.txt",
      "fidelity-dataset.json itself, via the self-blanked seal",
      "every capture tensor a second time by CONTENT, via capture.capture_content_digest"
    ],
    "excludes": [
      "checksums.txt",
      "fidelity-dataset.json"
    ],
    "external_anchor": null,
    "method": "self_blanked_canonical_json_sha256",
    "seal_field": "dataset_sha256"
  },
  "upstream_receipts": [],
  "weights": {
    "artifact_ref": null,
    "checkpoint_identity_sha256": "2bafa83a937934c04da9cbc20540babe9dddf945aa3926392497d25f1a8c58b6",
    "codec": "nvfp4",
    "config_sha256": "48025a5f559f7a747734be8499810a22d34330577e1566a28810466319f57ef4",
    "declared_bits": 4.0,
    "declared_head_bits": 16,
    "index_sha256": null,
    "model_ref": null,
    "model_revision": "54e52520606f96b3d9fc84088ad22882a61648ac",
    "quantized": true,
    "repository": "incoai/GLM-5.3-NVFP4",
    "revision": "54e52520606f96b3d9fc84088ad22882a61648ac"
  }
}