Publish gemma-4-26B-A4B-it distribution-fidelity artifact (part 7)
Browse files- unsloth-gemma-4-26B-A4B-it-NVFP4/attribution.json +98 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4-qdq-manifest.json +113 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json +0 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/compliance.json +974 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/inspect.json +32 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/manifest.json +1682 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/provenance.json +53 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/report.json +0 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/report.md +301 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/strata.json +1780 -0
- unsloth-gemma-4-26B-A4B-it-NVFP4/strata.md +68 -0
unsloth-gemma-4-26B-A4B-it-NVFP4/attribution.json
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{
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"composite_cell": {
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"components": [
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"experts",
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"attention",
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"dense_mlp"
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],
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"match_mode": "per_tensor",
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"mean_kld": 1.4355058325437036,
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"partition": "analysis",
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"qdq_manifest": "attribution/gemma-4-26B-A4B-it-qdq-experts-attention-dense_mlp-nvfp4-qdq-manifest.json",
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"reference_config_sha256": "ed0c1eb3633de771906e9ba004a44cc5635bcc06ee2062077c3d2e88a50707d3",
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"report": "attribution/gemma-4-26B-A4B-it-qdq-experts-attention-dense_mlp-nvfp4.json",
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"routing_excess_mean": null,
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"scheme": "nvfp4",
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"selection_flip_rate": 0.6775011831338544,
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"token_sha256": "81d66409b44937fadcb5361f42b497543b6cfd377d7fb85c7dabdcd7afd57f32",
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"variant": "gemma-4-26B-A4B-it-qdq-experts-attention-dense_mlp-nvfp4"
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},
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"deployed": {
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"candidate": "unsloth-gemma-4-26B-A4B-it-NVFP4",
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"inspection": {
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"coverage": {
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"attention": {
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"quantized": 115,
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"weights": 115
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"dense_mlp": {
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"quantized": 90,
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"weights": 90
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"experts": {
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"quantized": 11520,
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"declared": {},
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"detected_scheme": "nvfp4",
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"model": "/media/fmodels2/unsloth/gemma-4-26B-A4B-it-NVFP4",
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"quant_algorithm": "round_to_nearest",
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"quant_method": "compressed-tensors",
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"quantized_names_sha256": "b1384f9775d673940254e21793af07954ca0b554e849cf4734ccb6712163dfa5",
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"weights_sha256": "35808fd0b33ba55f748ad168724d1ec838a899362bd77aac4a183345b617bd19"
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},
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| 56 |
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"mean_kld": 1.1652832681101484,
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| 57 |
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"report": "report.json",
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| 58 |
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"scheme": "nvfp4"
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},
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"engine_arithmetic": -0.2702225644335552,
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| 61 |
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"expert_cell": {
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| 62 |
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"match_mode": "per_tensor",
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| 63 |
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"mean_kld": 0.6588457176668272,
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| 64 |
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"partition": "analysis",
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| 65 |
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"qdq_manifest": "attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4-qdq-manifest.json",
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| 66 |
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"reference_config_sha256": "ed0c1eb3633de771906e9ba004a44cc5635bcc06ee2062077c3d2e88a50707d3",
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| 67 |
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"report": "attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json",
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| 68 |
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"routing_excess_mean": 0.583112164659827,
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| 69 |
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"scheme": "nvfp4",
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"selection_flip_rate": 0.5052739569763339,
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| 71 |
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"token_sha256": "81d66409b44937fadcb5361f42b497543b6cfd377d7fb85c7dabdcd7afd57f32",
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"variant": "gemma-4-26B-A4B-it-qdq-experts-nvfp4"
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},
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"ladder": [
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{
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"mean_kld": null,
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"scheme": "fp8_block",
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"unavailable": "fp8_block lays scales on a 128-wide grid and model.language_model.layers.0.experts.down_proj is 128x2816x704, whose 704 is not a multiple of 128"
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},
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{
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"mean_kld": 0.46808904126519385,
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"report": "attribution/gemma-4-26B-A4B-it-qdq-experts-mxfp8.json",
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| 83 |
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"scheme": "mxfp8",
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"variant": "gemma-4-26B-A4B-it-qdq-experts-mxfp8"
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},
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{
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"mean_kld": 0.6588457176668272,
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| 88 |
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"report": "attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json",
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| 89 |
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"scheme": "nvfp4",
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"variant": "gemma-4-26B-A4B-it-qdq-experts-nvfp4"
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}
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],
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| 93 |
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"quant_algorithm": "round_to_nearest",
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"router_cell": {
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"evidence": "qdq.py --inspect gemma-4-26B-A4B-it-NVFP4: router 0 of 30 weights quantized",
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"status": "not_applicable"
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}
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}
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unsloth-gemma-4-26B-A4B-it-NVFP4/attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4-qdq-manifest.json
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{
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"block_size": 16,
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"components": [
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"experts"
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],
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| 6 |
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"components_detail": {
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| 7 |
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"experts": {
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| 8 |
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"max_abs_err": 0.15234375,
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| 9 |
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"names": [
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| 10 |
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"model.language_model.layers.0.experts.down_proj",
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| 11 |
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"model.language_model.layers.0.experts.gate_up_proj",
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| 12 |
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"model.language_model.layers.1.experts.down_proj",
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| 13 |
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"model.language_model.layers.1.experts.gate_up_proj",
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| 14 |
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"model.language_model.layers.10.experts.down_proj",
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| 15 |
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"model.language_model.layers.10.experts.gate_up_proj",
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| 16 |
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"model.language_model.layers.11.experts.down_proj",
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| 17 |
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"model.language_model.layers.11.experts.gate_up_proj",
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| 18 |
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"model.language_model.layers.12.experts.down_proj",
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| 19 |
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"model.language_model.layers.12.experts.gate_up_proj",
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| 20 |
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"model.language_model.layers.13.experts.down_proj",
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| 21 |
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"model.language_model.layers.13.experts.gate_up_proj",
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| 22 |
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"model.language_model.layers.14.experts.down_proj",
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| 23 |
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"model.language_model.layers.14.experts.gate_up_proj",
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| 24 |
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"model.language_model.layers.15.experts.down_proj",
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| 25 |
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"model.language_model.layers.15.experts.gate_up_proj",
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| 26 |
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"model.language_model.layers.16.experts.down_proj",
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| 27 |
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"model.language_model.layers.16.experts.gate_up_proj",
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| 28 |
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"model.language_model.layers.17.experts.gate_up_proj",
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| 29 |
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"model.language_model.layers.18.experts.down_proj",
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| 30 |
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"model.language_model.layers.18.experts.gate_up_proj",
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| 31 |
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"model.language_model.layers.19.experts.down_proj",
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| 32 |
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"model.language_model.layers.19.experts.gate_up_proj",
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| 33 |
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"model.language_model.layers.2.experts.down_proj",
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| 34 |
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"model.language_model.layers.2.experts.gate_up_proj",
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| 35 |
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"model.language_model.layers.20.experts.down_proj",
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| 36 |
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"model.language_model.layers.20.experts.gate_up_proj",
|
| 37 |
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"model.language_model.layers.21.experts.down_proj",
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| 38 |
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"model.language_model.layers.21.experts.gate_up_proj",
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| 39 |
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"model.language_model.layers.22.experts.down_proj",
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| 40 |
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"model.language_model.layers.22.experts.gate_up_proj",
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| 41 |
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"model.language_model.layers.23.experts.gate_up_proj",
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| 42 |
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"model.language_model.layers.24.experts.down_proj",
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| 43 |
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"model.language_model.layers.24.experts.gate_up_proj",
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| 44 |
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"model.language_model.layers.25.experts.down_proj",
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| 45 |
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"model.language_model.layers.25.experts.gate_up_proj",
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| 46 |
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"model.language_model.layers.26.experts.down_proj",
|
| 47 |
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"model.language_model.layers.26.experts.gate_up_proj",
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| 48 |
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"model.language_model.layers.27.experts.down_proj",
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| 49 |
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"model.language_model.layers.27.experts.gate_up_proj",
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| 50 |
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"model.language_model.layers.28.experts.down_proj",
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| 51 |
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"model.language_model.layers.28.experts.gate_up_proj",
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| 52 |
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"model.language_model.layers.29.experts.gate_up_proj",
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| 53 |
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"model.language_model.layers.3.experts.down_proj",
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| 54 |
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"model.language_model.layers.3.experts.gate_up_proj",
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| 55 |
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"model.language_model.layers.4.experts.down_proj",
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| 56 |
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"model.language_model.layers.4.experts.gate_up_proj",
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"model.language_model.layers.5.experts.down_proj",
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| 58 |
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"model.language_model.layers.5.experts.gate_up_proj",
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| 59 |
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"model.language_model.layers.6.experts.down_proj",
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| 60 |
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"model.language_model.layers.6.experts.gate_up_proj",
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| 61 |
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"model.language_model.layers.7.experts.down_proj",
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| 62 |
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"model.language_model.layers.7.experts.gate_up_proj",
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| 63 |
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"model.language_model.layers.8.experts.down_proj",
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| 64 |
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"model.language_model.layers.8.experts.gate_up_proj",
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| 65 |
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"model.language_model.layers.9.experts.down_proj",
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| 66 |
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"model.language_model.layers.9.experts.gate_up_proj",
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"model.language_model.layers.17.experts.down_proj",
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| 68 |
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"model.language_model.layers.23.experts.down_proj",
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| 69 |
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"model.language_model.layers.29.experts.down_proj"
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| 70 |
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],
|
| 71 |
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"parameters": 22837985280,
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| 72 |
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"tensors": 60,
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| 74 |
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}
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| 75 |
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},
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"device": "cuda",
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| 77 |
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"match_mode": "per_component",
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| 78 |
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"matched": {
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| 79 |
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|
| 83 |
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},
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| 84 |
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|
| 86 |
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"weights": 90
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|
| 88 |
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"experts": {
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| 89 |
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"quantized": 11520,
|
| 90 |
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"weights": 11520
|
| 91 |
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},
|
| 92 |
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"router": {
|
| 93 |
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"quantized": 0,
|
| 94 |
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"weights": 30
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| 95 |
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},
|
| 96 |
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"shared_expert": {
|
| 97 |
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"quantized": 0,
|
| 98 |
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"weights": 0
|
| 99 |
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}
|
| 100 |
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},
|
| 101 |
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"detected_block": null,
|
| 102 |
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"detected_scheme": "fp8_per_channel",
|
| 103 |
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"model": "/media/fmodels2/RedHatAI/gemma-4-26B-A4B-it-FP8-dynamic",
|
| 104 |
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"quant_algorithm": null
|
| 105 |
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},
|
| 106 |
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"note": "Quantize-dequantize variant stored in the source dtype. Weights are numerically what the quantized model would use; arithmetic is not. Comparing this against the real quantized checkpoint isolates the kernel's contribution.",
|
| 107 |
+
"quantized_names_sha256": null,
|
| 108 |
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"scheme": "nvfp4",
|
| 109 |
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"source_model": "/media/fmodels2/google/gemma-4-26B-A4B-it",
|
| 110 |
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"tensors_rounded": 60,
|
| 111 |
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"tensors_untouched": 953,
|
| 112 |
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"vllm_version": "0.1.dev20446+gb2bc9171d"
|
| 113 |
+
}
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unsloth-gemma-4-26B-A4B-it-NVFP4/attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json
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unsloth-gemma-4-26B-A4B-it-NVFP4/compliance.json
ADDED
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"NVIDIA RTX PRO 6000 Blackwell Workstation Edition",
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"NVIDIA RTX PRO 6000 Blackwell Workstation Edition"
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],
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},
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{
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"detail": "eager enforced; prefix caching off, max_num_seqs=1",
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},
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{
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},
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},
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{
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"detail": "hidden storage with bitwise-exact replay",
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| 174 |
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},
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{
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"detail": "mean 1.16528327, median 0.29886094, max 42.91187286, 4 depth buckets",
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"title": "Tail and depth disclosure"
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| 180 |
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},
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| 181 |
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{
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"detail": "comparability key fully resolved",
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| 183 |
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"title": "Comparability"
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},
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{
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"detail": "partition is 'analysis'",
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| 191 |
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| 192 |
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},
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| 193 |
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{
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"detail": "suite, reference, head, and checksums present; published reference matches the scored capture on every bound field",
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"status": "pass",
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"law": 14,
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"title": "Component attribution"
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},
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{
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"detail": "10 domains disclosed; weakest other_multilingual at 1.48858881, strongest code_docs_issues at 0.88324234, spread 1.7x",
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"status": "pass",
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"title": "Domain disclosure"
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},
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{
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"approver": "Andy Kitzke",
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| 215 |
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"timestamp": "2026-09-02T05:15:00Z"
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| 216 |
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},
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"detail": "the report is bound to weights 35808fd0b33ba55f but no inspection of the candidate was supplied to check it against",
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| 218 |
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"law": 16,
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| 219 |
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"status": "override",
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| 220 |
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"title": "Candidate weight binding"
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| 221 |
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{
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"detail": "1 override(s), each fully attributed",
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| 226 |
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"title": "Recorded deviation"
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| 956 |
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| 957 |
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| 958 |
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| 960 |
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| 961 |
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| 965 |
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| 966 |
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| 967 |
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| 968 |
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| 969 |
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| 970 |
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| 971 |
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| 972 |
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| 973 |
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| 974 |
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|
unsloth-gemma-4-26B-A4B-it-NVFP4/inspect.json
ADDED
|
@@ -0,0 +1,32 @@
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| 1 |
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{
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"coverage": {
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| 3 |
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| 4 |
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| 5 |
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| 6 |
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| 7 |
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| 8 |
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|
| 9 |
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| 11 |
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| 12 |
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"quantized": 0,
|
| 13 |
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|
| 14 |
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| 15 |
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"router": {
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| 16 |
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"quantized": 0,
|
| 17 |
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"weights": 0
|
| 18 |
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},
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| 19 |
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"shared_expert": {
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| 20 |
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"quantized": 0,
|
| 21 |
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"weights": 0
|
| 22 |
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}
|
| 23 |
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},
|
| 24 |
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"declared": {},
|
| 25 |
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"detected_block": null,
|
| 26 |
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"detected_scheme": "fp8_per_channel",
|
| 27 |
+
"model": "/media/fmodels2/unsloth/gemma-4-26B-A4B-it-NVFP4",
|
| 28 |
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"quant_method": "compressed-tensors",
|
| 29 |
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"unloadable_reason": null,
|
| 30 |
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"weights_bytes": 16909852932,
|
| 31 |
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"weights_bytes_source": "hub"
|
| 32 |
+
}
|
unsloth-gemma-4-26B-A4B-it-NVFP4/manifest.json
ADDED
|
@@ -0,0 +1,1682 @@
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|
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}
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},
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"class": "GemmaTokenizer",
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| 1654 |
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| 1657 |
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| 1658 |
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| 1661 |
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| 1662 |
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"eos_token": "<eos>",
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"eot_token": "<turn|>",
|
| 1664 |
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"escape_token": "<|\"|>",
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| 1667 |
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| 1668 |
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"image_token": "<|image|>",
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| 1669 |
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"mask_token": "<mask>",
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"soc_token": "<|channel>",
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| 1674 |
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| 1675 |
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| 1676 |
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"think_token": "<|think|>",
|
| 1677 |
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"unk_token": "<unk>"
|
| 1678 |
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},
|
| 1679 |
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"vocab_sha256": "2f396f1b9092280fd3138ba9f9ec8173650fdab98608975e1c13b1f5fc4f1227",
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| 1680 |
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"vocab_size": 262144
|
| 1681 |
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}
|
| 1682 |
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}
|
unsloth-gemma-4-26B-A4B-it-NVFP4/provenance.json
ADDED
|
@@ -0,0 +1,53 @@
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
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|
|
|
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|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
| 1 |
+
{
|
| 2 |
+
"reference": "/media/fmodels2/google/gemma-4-26B-A4B-it",
|
| 3 |
+
"candidate": "/media/fmodels2/unsloth/gemma-4-26B-A4B-it-NVFP4",
|
| 4 |
+
"ok": true,
|
| 5 |
+
"identity": {
|
| 6 |
+
"architectures": [
|
| 7 |
+
"Gemma4ForConditionalGeneration"
|
| 8 |
+
],
|
| 9 |
+
"hidden_size": 2816,
|
| 10 |
+
"num_hidden_layers": 30,
|
| 11 |
+
"layer_types": [
|
| 12 |
+
"sliding_attention",
|
| 13 |
+
"sliding_attention",
|
| 14 |
+
"sliding_attention",
|
| 15 |
+
"sliding_attention",
|
| 16 |
+
"sliding_attention",
|
| 17 |
+
"full_attention",
|
| 18 |
+
"sliding_attention",
|
| 19 |
+
"sliding_attention",
|
| 20 |
+
"sliding_attention",
|
| 21 |
+
"sliding_attention",
|
| 22 |
+
"sliding_attention",
|
| 23 |
+
"full_attention",
|
| 24 |
+
"sliding_attention",
|
| 25 |
+
"sliding_attention",
|
| 26 |
+
"sliding_attention",
|
| 27 |
+
"sliding_attention",
|
| 28 |
+
"sliding_attention",
|
| 29 |
+
"full_attention",
|
| 30 |
+
"sliding_attention",
|
| 31 |
+
"sliding_attention",
|
| 32 |
+
"sliding_attention",
|
| 33 |
+
"sliding_attention",
|
| 34 |
+
"sliding_attention",
|
| 35 |
+
"full_attention",
|
| 36 |
+
"sliding_attention",
|
| 37 |
+
"sliding_attention",
|
| 38 |
+
"sliding_attention",
|
| 39 |
+
"sliding_attention",
|
| 40 |
+
"sliding_attention",
|
| 41 |
+
"full_attention"
|
| 42 |
+
],
|
| 43 |
+
"vocab_size": 262144,
|
| 44 |
+
"intermediate_size": 2112,
|
| 45 |
+
"head_dim": 256
|
| 46 |
+
},
|
| 47 |
+
"differing": [],
|
| 48 |
+
"tokenizer_sha256": {
|
| 49 |
+
"reference": "cc8d3a0ce36466ccc1278bf987df5f71db1719b9ca6b4118264f45cb627bfe0f",
|
| 50 |
+
"candidate": "adbaa8175acf7609b4359724f40eff359ec4fac1a8647eeb99d4422be708e1cf",
|
| 51 |
+
"match": false
|
| 52 |
+
}
|
| 53 |
+
}
|
unsloth-gemma-4-26B-A4B-it-NVFP4/report.json
ADDED
|
The diff for this file is too large to render.
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|
|
|
unsloth-gemma-4-26B-A4B-it-NVFP4/report.md
ADDED
|
@@ -0,0 +1,301 @@
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|
| 1 |
+
# gemma-4-26B-A4B-it / unsloth-gemma-4-26B-A4B-it-NVFP4: distribution fidelity
|
| 2 |
+
|
| 3 |
+
**Mean KLD(reference || candidate) = 1.16528327** over 1572096 scored positions.
|
| 4 |
+
|
| 5 |
+
| Mean | Median | p90 | p99 | Max | Top-1 agreement |
|
| 6 |
+
|---|---|---|---|---|---|
|
| 7 |
+
| 1.16528327 | 0.29886094 | 3.32146120 | 11.06814857 | 42.91187286 | 67.2572% |
|
| 8 |
+
|
| 9 |
+
Reverse direction, KLD(candidate || reference): 1.21587174.
|
| 10 |
+
|
| 11 |
+
## Identity
|
| 12 |
+
|
| 13 |
+
| Item | Value |
|
| 14 |
+
|---|---|
|
| 15 |
+
| Reference checkpoint | /media/fmodels2/google/gemma-4-26B-A4B-it |
|
| 16 |
+
| Reference config SHA-256 | ed0c1eb3633d |
|
| 17 |
+
| Candidate checkpoint | /media/fmodels2/unsloth/gemma-4-26B-A4B-it-NVFP4 |
|
| 18 |
+
| Candidate weights SHA-256 | 35808fd0b33ba55f |
|
| 19 |
+
| Suite | gemma4-31b-it-fidelity-1024x2048-v1 |
|
| 20 |
+
| Suite token SHA-256 | 81d66409b44937fa |
|
| 21 |
+
| Capture manifest SHA-256 | 395c569193357bb7 |
|
| 22 |
+
| Tokenizer | None |
|
| 23 |
+
| Scored vocabulary | 262144 |
|
| 24 |
+
| Declared vocabulary | 262144 |
|
| 25 |
+
| Geometry | 768 rows x 2048 tokens, stride 2048, score_from 0 |
|
| 26 |
+
| Reference storage | hidden |
|
| 27 |
+
| Model runner | V1 |
|
| 28 |
+
| Tensor parallel | 1 |
|
| 29 |
+
| Eager enforced | True |
|
| 30 |
+
| Prefix caching | False |
|
| 31 |
+
| max_num_seqs | 1 |
|
| 32 |
+
| vLLM | 0.1.dev20446+gb2bc9171d |
|
| 33 |
+
| vLLM commit | f4cee453bfd3 |
|
| 34 |
+
| torch | 2.13.0+cu132 |
|
| 35 |
+
| Driver | 580.173.02 |
|
| 36 |
+
| GPUs | NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition |
|
| 37 |
+
| Laws version | 8 |
|
| 38 |
+
| Partition | analysis |
|
| 39 |
+
|
| 40 |
+
## Component attribution
|
| 41 |
+
|
| 42 |
+
This reference routes tokens to experts, so the deployed mean is not a single effect. Each cell is the reference with one component rounded through the deployed scheme and run on BF16 kernels, scored on the same tokens against the same capture. The cells do not sum to the deployed mean and are not meant to: once a token is routed elsewhere, degrading the expert it no longer uses costs nothing.
|
| 43 |
+
|
| 44 |
+
No cell here holds expert selection fixed. Rounding any weight changes the residual stream, and every router downstream of that change sees different inputs, so each cell carries some routing movement of its own. What routing costs is measured directly under [Routing divergence](#routing-divergence), not inferred from these cells.
|
| 45 |
+
|
| 46 |
+
### What the deployed checkpoint quantizes
|
| 47 |
+
|
| 48 |
+
| Property | Value |
|
| 49 |
+
|---|---|
|
| 50 |
+
| Scheme | nvfp4 |
|
| 51 |
+
| Block | 16 |
|
| 52 |
+
| Weights on disk | n/a |
|
| 53 |
+
| Declared method | compressed-tensors |
|
| 54 |
+
| Algorithm | round_to_nearest |
|
| 55 |
+
| Activation scheme | n/a |
|
| 56 |
+
|
| 57 |
+
| Component | Quantized weights | Coverage |
|
| 58 |
+
|---|---|---|
|
| 59 |
+
| attention | 115 / 115 | all |
|
| 60 |
+
| dense_mlp | 90 / 90 | all |
|
| 61 |
+
| experts | 11520 / 11520 | all |
|
| 62 |
+
| router | 0 / 30 | none |
|
| 63 |
+
|
| 64 |
+
### Component cells
|
| 65 |
+
|
| 66 |
+
| Cell | What it isolates | Mean KLD | Selections changed | Support |
|
| 67 |
+
|---|---|---|---|---|
|
| 68 |
+
| Experts only | expert weight precision | 0.65884572 | 50.5274% | [report](attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json) · [manifest](attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4-qdq-manifest.json) |
|
| 69 |
+
| Router weights only | router weight precision, not the routing term | n/a — router ships unquantized | n/a | [routing divergence](#routing-divergence) |
|
| 70 |
+
| Every quantized component | experts, attention, dense_mlp, BF16 kernels | 1.43550583 | 67.7501% | [report](attribution/gemma-4-26B-A4B-it-qdq-experts-attention-dense_mlp-nvfp4.json) · [manifest](attribution/gemma-4-26B-A4B-it-qdq-experts-attention-dense_mlp-nvfp4-qdq-manifest.json) |
|
| 71 |
+
| Deployed | as shipped, quantized kernels | 1.16528327 | 66.5133% | [report.json](report.json) |
|
| 72 |
+
|
| 73 |
+
Two terms sit outside every cell above, because no cell can hold them. They are the reason the deployed mean is not a precision measurement:
|
| 74 |
+
|
| 75 |
+
| Term | Value | Share | What it is | Derived in |
|
| 76 |
+
|---|---|---|---|---|
|
| 77 |
+
| Beyond weight rounding | -0.27022256 | 23% of the deployed mean | activation quantization and kernel arithmetic | [Beyond weight rounding](#beyond-weight-rounding) |
|
| 78 |
+
| Routing, measured | saturated | n/a | expert selection changed at 66.5133% of choices and every scored position rerouted, so no held-routing population remains to measure the excess against | [Routing divergence](#routing-divergence) |
|
| 79 |
+
|
| 80 |
+
The selections column is why none of these cells is routing-free: each one reroutes some tokens purely as a consequence of rounding weights upstream of a router it never touched.
|
| 81 |
+
|
| 82 |
+
### Beyond weight rounding
|
| 83 |
+
|
| 84 |
+
**-0.27022256** (23% of the deployed mean). Every cell above rounds weights and runs on BF16 kernels. The deployed checkpoint differs from the composite cell by this much. The checkpoint quantizes weights only, so this term is kernel arithmetic. A weight-only analysis, which is what any QDQ cell is, cannot see it.
|
| 85 |
+
|
| 86 |
+
The deployed checkpoint leaves the router in BF16, so router weight precision costs exactly nothing here. That is not the same as routing costing nothing: an identical router fed perturbed activations selects different experts, which is measured under [Routing divergence](#routing-divergence).
|
| 87 |
+
|
| 88 |
+
Evidence: qdq.py --inspect gemma-4-26B-A4B-it-NVFP4: router 0 of 30 weights quantized
|
| 89 |
+
|
| 90 |
+
### Scheme ladder
|
| 91 |
+
|
| 92 |
+
The same expert weights rounded through each scheme, which is the cell that discriminates between them. Ladder rungs hold that weight set fixed across schemes, so a deployed rung is not the expert cell above it when the checkpoint quantizes only some of those weights.
|
| 93 |
+
|
| 94 |
+
| Scheme | Experts-only mean KLD | Variant | Support |
|
| 95 |
+
|---|---|---|---|
|
| 96 |
+
| fp8_block | not expressible | | |
|
| 97 |
+
| mxfp8 | 0.46808904 | gemma-4-26B-A4B-it-qdq-experts-mxfp8 | [report](attribution/gemma-4-26B-A4B-it-qdq-experts-mxfp8.json) |
|
| 98 |
+
| nvfp4 | 0.65884572 | gemma-4-26B-A4B-it-qdq-experts-nvfp4 | [report](attribution/gemma-4-26B-A4B-it-qdq-experts-nvfp4.json) |
|
| 99 |
+
|
| 100 |
+
`fp8_block` has no rung on this model: fp8_block lays scales on a 128-wide grid and model.language_model.layers.0.experts.down_proj is 128x2816x704, whose 704 is not a multiple of 128. Padding the weight to fit the grid would change the scales, so the rung is reported absent rather than measured under a layout no checkpoint deploys.
|
| 101 |
+
|
| 102 |
+
Full cells, digests, and the deployed checkpoint's inspection are in [attribution.json](attribution.json).
|
| 103 |
+
|
| 104 |
+
## Routing divergence
|
| 105 |
+
|
| 106 |
+
Both runs saw the same tokens, and the candidate's routers are the reference's routers to the bit where the checkpoint leaves them unquantized. They still selected different experts, because the activations arriving at them were already perturbed. This section compares the two runs' recorded selections over 30 routed layers, 8 experts per token.
|
| 107 |
+
|
| 108 |
+
| Measure | Rate | Of what |
|
| 109 |
+
|---|---|---|
|
| 110 |
+
| Selections changed | 66.5133% | of 47162880 (token, layer) choices |
|
| 111 |
+
| Expert slots changed | 35.3904% | of individual expert slots |
|
| 112 |
+
| Highest-weighted expert changed | 22.3356% | includes reordering within an unchanged selection |
|
| 113 |
+
| Positions with any flip | 100.0000% | 1572096 of 1572096 scored positions |
|
| 114 |
+
|
| 115 |
+
| Positions where | Mean KLD | Count |
|
| 116 |
+
|---|---|---|
|
| 117 |
+
| Routing held | n/a | 0 |
|
| 118 |
+
| Routing flipped | 1.16528327 | 1572096 |
|
| 119 |
+
|
| 120 |
+
**Ranking floor undefined.** Every one of the 1572096 scored positions rerouted in at least one layer, so there is no held-routing population to compare the flipped ones against. The floor is not small here, it is unmeasurable, and no rescore changes that: the perturbation is large enough that routing divergence is the whole picture rather than a correction to it. Rank against this candidate on the experts-only cell, and treat the deployed mean as unranked.
|
| 121 |
+
|
| 122 |
+
Divergence rises with how many layers rerouted, which is what distinguishes a single reroute a later layer can absorb from a token that took a different path through the model:
|
| 123 |
+
|
| 124 |
+
| Layers rerouted | Positions | Mean KLD |
|
| 125 |
+
|---|---|---|
|
| 126 |
+
| 2-3 | 3 | 0.47295016 |
|
| 127 |
+
| 4-7 | 794 | 0.43082415 |
|
| 128 |
+
| 8+ | 1571299 | 1.16565572 |
|
| 129 |
+
|
| 130 |
+
Per-layer selection change runs from 14.82% to 84.85% (worst at layer 29), averaging 33.08% across the first quarter of routed layers and 82.99% across the last. Rerouting that grows with depth is accumulated perturbation, not router precision.
|
| 131 |
+
|
| 132 |
+
## Fidelity by domain
|
| 133 |
+
|
| 134 |
+
The suite is stratified, so the mean above is an average over kinds of text that do not degrade equally. `x run` is a domain's mean divided by the run's, so 1.00 degrades exactly as much as the model overall. The reference's own top-1 probability is shown because a domain the reference finds harder will diverge more for that reason alone.
|
| 135 |
+
|
| 136 |
+
| Domain | Contexts | Reference top-1 | Mean KLD | x run | Worst context |
|
| 137 |
+
|---|---|---|---|---|---|
|
| 138 |
+
| Other multilingual content | 36 | 70.7% | 1.48858881 | 1.28 | 2.68577882 |
|
| 139 |
+
| Natural dialogue, instruction following, and assistance | 96 | 77.3% | 1.39235292 | 1.19 | 2.69614465 |
|
| 140 |
+
| Chinese across several content types | 72 | 63.3% | 1.28909623 | 1.11 | 2.49083368 |
|
| 141 |
+
| Encyclopedic and factual reference | 96 | 68.9% | 1.28788107 | 1.11 | 2.49894944 |
|
| 142 |
+
| Scientific and technical exposition | 96 | 69.8% | 1.25331400 | 1.08 | 2.74250087 |
|
| 143 |
+
| News, history, economics, legal analysis, and essays | 72 | 68.9% | 1.12880327 | 0.97 | 2.51205195 |
|
| 144 |
+
| Literary, narrative, and creative writing | 72 | 62.9% | 1.10590731 | 0.95 | 2.95968462 |
|
| 145 |
+
| Structured data, tool calls, APIs, JSON, and tables | 36 | 83.3% | 0.97822020 | 0.84 | 1.83615284 |
|
| 146 |
+
| Worked mathematics, science, and formal reasoning | 96 | 72.4% | 0.93756733 | 0.80 | 1.98123008 |
|
| 147 |
+
| Source code, tests, technical documentation, and issue discussions | 96 | 84.2% | 0.88324234 | 0.76 | 2.53701591 |
|
| 148 |
+
|
| 149 |
+
The same domains measured on the other attribution cells, which shows whether a domain's weakness is a property of the model or of the format:
|
| 150 |
+
|
| 151 |
+
| Domain | deployed | mxfp8 | nvfp4 | all_quantized |
|
| 152 |
+
|---|---|---|---|---|
|
| 153 |
+
| Other multilingual content | 1.48858881 | 0.60547717 | 0.83417492 | 1.78787763 |
|
| 154 |
+
| Natural dialogue, instruction following, and assistance | 1.39235292 | 0.59360268 | 0.81214064 | 1.63223190 |
|
| 155 |
+
| Chinese across several content types | 1.28909623 | 0.46550453 | 0.68079090 | 1.42905706 |
|
| 156 |
+
| Encyclopedic and factual reference | 1.28788107 | 0.48326683 | 0.70706639 | 1.60899741 |
|
| 157 |
+
| Scientific and technical exposition | 1.25331400 | 0.49939479 | 0.69630321 | 1.64129071 |
|
| 158 |
+
| News, history, economics, legal analysis, and essays | 1.12880327 | 0.46316973 | 0.63722594 | 1.41930026 |
|
| 159 |
+
| Literary, narrative, and creative writing | 1.10590731 | 0.39760530 | 0.61395641 | 1.34757069 |
|
| 160 |
+
| Structured data, tool calls, APIs, JSON, and tables | 0.97822020 | 0.48035757 | 0.61317963 | 1.19883758 |
|
| 161 |
+
| Worked mathematics, science, and formal reasoning | 0.93756733 | 0.39846297 | 0.55025835 | 1.22613314 |
|
| 162 |
+
| Source code, tests, technical documentation, and issue discussions | 0.88324234 | 0.36808736 | 0.51325925 | 1.10842929 |
|
| 163 |
+
|
| 164 |
+
**Other multilingual content** is this candidate's weakest domain and **Source code, tests, technical documentation, and issue discussions** its strongest, a spread of 1.7x. A deployment weighted toward the weakest domain sees more divergence than the headline mean implies; the per-source breakdown and the reading are in [strata.md](strata.md) and [strata.json](strata.json).
|
| 165 |
+
|
| 166 |
+
## Trunk versus head
|
| 167 |
+
|
| 168 |
+
| Component | Value |
|
| 169 |
+
|---|---|
|
| 170 |
+
| Trunk (candidate hidden states, reference head) | 1.16259473 |
|
| 171 |
+
| Deployed (candidate's own head) | 1.16528327 |
|
| 172 |
+
| Head-associated delta (not additive) | 0.00268853 |
|
| 173 |
+
| Reference head | {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 262144, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 262144, 'scale': 1.0, 'soft_cap': 30.0, 'type': 'LogitsProcessor', 'vocab_size': 262144}, 'primary': {'org_vocab_size': 262144, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]} |
|
| 174 |
+
| Candidate head | {'runtime': {'state': 'unquantized', 'workers': [{'heads': [{'name': 'language_model.lm_head', 'org_vocab_size': 262144, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}], 'logits_processor': {'head_dtype': 'torch.bfloat16', 'org_vocab_size': 262144, 'scale': 1.0, 'soft_cap': 30.0, 'type': 'LogitsProcessor', 'vocab_size': 262144}, 'primary': {'org_vocab_size': 262144, 'quant_method': 'UnquantizedEmbeddingMethod', 'state': 'unquantized', 'weight_dtype': 'torch.bfloat16'}, 'state': 'unquantized'}]}, 'state': 'unquantized', 'static': {'ignored': True, 'lm_head_dtypes': {'model.language_model.embed_tokens.weight': 'BF16'}, 'lm_head_keys': [], 'output_weight_keys': ['model.language_model.embed_tokens.weight'], 'packed_keys': [], 'quant_method': 'compressed-tensors', 'state': 'unquantized', 'tie_word_embeddings': True}} |
|
| 175 |
+
|
| 176 |
+
## Error by context depth
|
| 177 |
+
|
| 178 |
+
| Position range | Positions | Mean KLD |
|
| 179 |
+
|---|---|---|
|
| 180 |
+
| 0–511 | 393216 | 1.02836681 |
|
| 181 |
+
| 512–1023 | 393216 | 1.00813210 |
|
| 182 |
+
| 1024–1535 | 393216 | 1.18629528 |
|
| 183 |
+
| 1536–2046 | 392448 | 1.43887323 |
|
| 184 |
+
|
| 185 |
+
## Error by reference confidence
|
| 186 |
+
|
| 187 |
+
| Reference top-1 probability | Positions | Share | Mean KLD |
|
| 188 |
+
|---|---|---|---|
|
| 189 |
+
| [0.00, 0.25) | 113173 | 7.2% | 2.65648229 |
|
| 190 |
+
| [0.25, 0.50) | 290937 | 18.5% | 1.86559804 |
|
| 191 |
+
| [0.50, 0.75) | 308489 | 19.6% | 1.42333509 |
|
| 192 |
+
| [0.75, 0.95) | 318287 | 20.2% | 1.04472744 |
|
| 193 |
+
| [0.95, 1.00) | 541210 | 34.4% | 0.40080046 |
|
| 194 |
+
|
| 195 |
+
## Top-K set agreement
|
| 196 |
+
|
| 197 |
+
| K=1 | K=2 | K=3 | K=4 | K=5 |
|
| 198 |
+
|---|---|---|---|---|
|
| 199 |
+
| 67.2282% | 32.0206% | 12.4511% | 4.2012% | 1.3271% |
|
| 200 |
+
|
| 201 |
+
## Law compliance
|
| 202 |
+
|
| 203 |
+
Zero baseline: reference against itself scored **0.00000000** over 2047 positions.
|
| 204 |
+
|
| 205 |
+
| Law | Name | Status | Detail |
|
| 206 |
+
|---|---|---|---|
|
| 207 |
+
| 1 | Zero baseline | PASS | self-KLD exactly 0.0 over 2047 positions |
|
| 208 |
+
| 2 | Determinism | PASS | eager enforced; prefix caching off, max_num_seqs=1 |
|
| 209 |
+
| 3 | Frozen input | PASS | token hash matches suite gemma4-31b-it-fidelity-1024x2048-v1 [analysis]: 81d66409b44937fa |
|
| 210 |
+
| 4 | Real vocabulary | PASS | scored 262144 real tokens of 262144 declared (0 padding rows) |
|
| 211 |
+
| 5 | Manifest binding | PASS | all bound fields present; manifest 395c569193357bb7f32e750fa94d4156797bef41dc8f46f13370aca8764f4097 |
|
| 212 |
+
| 6 | Provenance | PASS | torch 2.13.0+cu132, vLLM 0.1.dev20446+gb2bc9171d @ f4cee453bfd3, driver 580.173.02, 4 GPU(s) |
|
| 213 |
+
| 7 | Storage integrity | PASS | hidden storage with bitwise-exact replay |
|
| 214 |
+
| 8 | Head transparency | PASS | trunk 1.16259473, deployed 1.16528327, delta 0.002688534913900309 |
|
| 215 |
+
| 9 | Tail and depth disclosure | PASS | mean 1.16528327, median 0.29886094, max 42.91187286, 4 depth buckets |
|
| 216 |
+
| 10 | Comparability | PASS | comparability key fully resolved |
|
| 217 |
+
| 11 | Freeze before qualification | NOT_APPLICABLE | partition is 'analysis' |
|
| 218 |
+
| 12 | Reusable reference | PASS | suite, reference, head, and checksums present; published reference matches the scored capture on every bound field |
|
| 219 |
+
| 14 | Component attribution | PASS | experts 0.65884572, router weights unquantized; selections changed at 66.513% of (token, layer) choices; ranking floor undefined: every scored position rerouted, so no held-routing population remains to compare against |
|
| 220 |
+
| 15 | Domain disclosure | PASS | 10 domains disclosed; weakest other_multilingual at 1.48858881, strongest code_docs_issues at 0.88324234, spread 1.7x |
|
| 221 |
+
| 16 | Candidate weight binding | OVERRIDE | the report is bound to weights 35808fd0b33ba55f but no inspection of the candidate was supplied to check it against |
|
| 222 |
+
| 13 | Recorded deviation | PASS | 1 override(s), each fully attributed |
|
| 223 |
+
|
| 224 |
+
### Recorded deviations
|
| 225 |
+
|
| 226 |
+
- **Law 16 (Candidate weight binding)** overridden by Andy Kitzke at 2026-09-02T05:15:00Z. Justification: These candidates were scored under laws version 7, before Law 16 required a digest of the weights each report read. The campaign runs with fetch=lease, so every candidate's weights were released immediately after scoring and no digest can be recovered from them now. The measurement itself is unaffected: the tokens, the capture, and the reference remain bound under Laws 3, 5, and 12, and each candidate still pins its hf_repo and revision. What these results cannot prove is that the directory scored held the repo it names. They publish with their weights marked unbound, and every measurement taken after laws version 8 is bound at score time. A second case is covered by the same reasoning: a report bound to a digest at score time whose inspection cannot be re-taken, because the leased weights were released before assembly and the inspection record was cleared. Such a report states which weights it read and is simply uncorroborated, which is the weaker of the two positions here, not the stronger. Underlying finding: the report is bound to weights 35808fd0b33ba55f but no inspection of the candidate was supplied to check it against.
|
| 227 |
+
|
| 228 |
+
## Environment
|
| 229 |
+
|
| 230 |
+
| Item | Value |
|
| 231 |
+
|---|---|
|
| 232 |
+
| Host | d011sd01 |
|
| 233 |
+
| Platform | Linux-6.8.0-137-generic-x86_64-with-glibc2.39 |
|
| 234 |
+
| Python | 3.12.3 |
|
| 235 |
+
| vLLM | 0.1.dev20446+gb2bc9171d |
|
| 236 |
+
| vLLM commit | f4cee453bfd38b1afcb8e983517c385e426ec488 |
|
| 237 |
+
| torch | 2.13.0+cu132 |
|
| 238 |
+
| torch CUDA runtime | 13.2 |
|
| 239 |
+
| cuDNN | 9.20.0 (92000) |
|
| 240 |
+
| NCCL | 2.29.7 |
|
| 241 |
+
| CUDA arch list | sm_75, sm_80, sm_86, sm_90, sm_100, sm_120 |
|
| 242 |
+
| nvcc | Cuda compilation tools, release 13.0, V13.0.88 |
|
| 243 |
+
| gcc | gcc (Ubuntu 13.3.0-6ubuntu2~24.04.1) 13.3.0 |
|
| 244 |
+
| glibc / ldd | ldd (Ubuntu GLIBC 2.39-0ubuntu8.8) 2.39 |
|
| 245 |
+
| NVIDIA driver | 580.173.02 |
|
| 246 |
+
| float32 matmul precision | highest |
|
| 247 |
+
| TF32 (matmul / cuDNN) | False / True |
|
| 248 |
+
| torch deterministic algorithms | False |
|
| 249 |
+
|
| 250 |
+
### GPUs
|
| 251 |
+
|
| 252 |
+
| Index | Device | Compute capability | Memory | SMs |
|
| 253 |
+
|---|---|---|---|---|
|
| 254 |
+
| 0 | NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 12.0 | 94.97 GiB | 188 |
|
| 255 |
+
| 1 | NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 12.0 | 94.97 GiB | 188 |
|
| 256 |
+
| 2 | NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 12.0 | 94.97 GiB | 188 |
|
| 257 |
+
| 3 | NVIDIA RTX PRO 6000 Blackwell Workstation Edition | 12.0 | 94.97 GiB | 188 |
|
| 258 |
+
|
| 259 |
+
### Captured environment variables
|
| 260 |
+
|
| 261 |
+
Every set variable matching the prefixes the capture watches: `VLLM_`, `TORCH`, `PYTORCH`, `CUDA`, `CUBLAS`, `NCCL`, `TRITON`, `FLASHINFER`, `OMP_`, `MKL_`, `HF_`, `SAFETENSORS_`. Any of these can move a bitwise result, so they are part of the identity.
|
| 262 |
+
|
| 263 |
+
Credential values are never published. Set but redacted: `HF_TOKEN`.
|
| 264 |
+
|
| 265 |
+
| Variable | Value |
|
| 266 |
+
|---|---|
|
| 267 |
+
| `CUDA_HOME` | `/usr/local/cuda-13.0` |
|
| 268 |
+
| `HF_TOKEN` | `<redacted>` |
|
| 269 |
+
|
| 270 |
+
## Files in this artifact
|
| 271 |
+
|
| 272 |
+
Paths are relative to the artifact root, the same paths `checksums.txt` uses. Verify the whole tree with `sha256sum --check checksums.txt` from that root.
|
| 273 |
+
|
| 274 |
+
| Path | Size | What it is |
|
| 275 |
+
|---|---|---|
|
| 276 |
+
| `unsloth-gemma-4-26B-A4B-it-NVFP4/report.md` | 21.66 KiB | This document. |
|
| 277 |
+
| `unsloth-gemma-4-26B-A4B-it-NVFP4/report.json` | 252.41 KiB | Every statistic behind it, machine-readable: per-bucket means, percentiles, agreement rates, and the phase timings. |
|
| 278 |
+
| `unsloth-gemma-4-26B-A4B-it-NVFP4/manifest.json` | 85.83 KiB | The capture manifest this result is bound to (Law 5). Scoring refuses to run if the live configuration differs from it. |
|
| 279 |
+
| `unsloth-gemma-4-26B-A4B-it-NVFP4/compliance.json` | 35.84 KiB | The law-by-law receipt, including the comparability key. |
|
| 280 |
+
| `baselines/self-kld.json` | 4.76 KiB | The zero-baseline proof required by Law 1: the reference scored against a capture of itself. |
|
| 281 |
+
| `suite/suite-manifest.json` | 382.61 KiB | The frozen evaluation input's identity: token hashes per context and per partition, sources, strata, and the analysis/qualification split. |
|
| 282 |
+
| `suite/tokens` | 17.32 MiB in 1024 files | The token IDs themselves. These are the evaluation input, not a description of it; retokenizing source text does not reproduce them. |
|
| 283 |
+
| `suite/sources.json` | 737.48 KiB | Per-context provenance: dataset, revision, licence, source unit, and the deterministic token offset chosen within the document. |
|
| 284 |
+
| `suite/validation/capability-overlap.json` | 1.48 KiB | The benchmark-contamination scan and every document it blocked. |
|
| 285 |
+
| `reference/manifest.json` | 85.83 KiB | The reference capture's own manifest: geometry, vocabulary, storage mode, and a hash for every tensor file. |
|
| 286 |
+
| `reference` | 11.62 GiB in 771 files | The reusable reference distributions. Pass this directory as `--reference-logits` to score a new candidate against the same reference without loading the reference checkpoint. |
|
| 287 |
+
| `reference/lm_head.safetensors` | 1.38 GiB | The reference language-model head, which turns the stored hidden states back into reference logits. |
|
| 288 |
+
| `environment/runtime.json` | 2.26 KiB | Machine-readable provenance: torch, CUDA, cuDNN, NCCL, driver, devices, and the captured environment variables. |
|
| 289 |
+
| `environment/summary.md` | 1.10 KiB | The same provenance as prose, plus an index of every captured file. |
|
| 290 |
+
| `environment/toolchain-nvcc.txt` | 243 B | `nvcc --version` verbatim; `toolchain-gcc.txt` and `toolchain-ldd.txt` sit beside it. |
|
| 291 |
+
| `environment/gpu-smi-query.txt` | 54.73 KiB | `nvidia-smi -q` verbatim: ECC state, persistence mode, clocks, and throttle reasons, any of which can move a bitwise result. |
|
| 292 |
+
| `environment/pip-freeze.txt` | 4.31 KiB | Every installed package version in the scoring environment. |
|
| 293 |
+
| `environment/models` | 11.35 KiB in 7 files | Checkpoint fingerprints: file listing, sizes, config and tokenizer hashes, and `config.json` verbatim for each model scored. |
|
| 294 |
+
| `checksums.txt` | 191.21 KiB | `sha256sum --check` compatible over every other file here. This is authoritative for integrity (Law 12). |
|
| 295 |
+
| `LAWS.md` | 29.81 KiB | The laws this artifact was produced under, including the override procedure. |
|
| 296 |
+
|
| 297 |
+
## Scope
|
| 298 |
+
|
| 299 |
+
This artifact measures teacher-forced next-token distribution fidelity at the stated context length, over the stated token suite, through the stated runtime. It does not measure free-running generation, benchmark accuracy, instruction following, long-context behavior beyond the suite's context length, multimodal behavior, tool use, or throughput.
|
| 300 |
+
|
| 301 |
+
Per Law 10, these numbers are comparable only to numbers produced from the same suite, geometry, and runtime identity. A KLD threshold borrowed from another model, corpus, tokenizer, or serving stack carries no verdict here.
|
unsloth-gemma-4-26B-A4B-it-NVFP4/strata.json
ADDED
|
@@ -0,0 +1,1780 @@
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| 1 |
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| 2 |
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| 4 |
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| 7 |
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| 10 |
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"contexts": 96,
|
| 1681 |
+
"max_kld": 33.868255615234375,
|
| 1682 |
+
"mean_kld": 0.8832423431706881,
|
| 1683 |
+
"mean_ref_top1_prob": 0.8422180790368852,
|
| 1684 |
+
"median_context_kld": 0.7867652145713631,
|
| 1685 |
+
"median_context_p99": 9.64005184173584,
|
| 1686 |
+
"p90_context_kld": 1.4638790388548903,
|
| 1687 |
+
"positions": 196512,
|
| 1688 |
+
"top1_agreement": 0.7802780491776584,
|
| 1689 |
+
"worst_context_id": 647,
|
| 1690 |
+
"worst_context_kld": 2.5370159138677204
|
| 1691 |
+
},
|
| 1692 |
+
"mxfp8": {
|
| 1693 |
+
"contexts": 96,
|
| 1694 |
+
"max_kld": 32.57528305053711,
|
| 1695 |
+
"mean_kld": 0.36808736316743346,
|
| 1696 |
+
"mean_ref_top1_prob": 0.8422180790368852,
|
| 1697 |
+
"median_context_kld": 0.3361295913815615,
|
| 1698 |
+
"median_context_p99": 5.228585243225098,
|
| 1699 |
+
"p90_context_kld": 0.6134838321126249,
|
| 1700 |
+
"positions": 196512,
|
| 1701 |
+
"top1_agreement": 0.868033504315258,
|
| 1702 |
+
"worst_context_id": 683,
|
| 1703 |
+
"worst_context_kld": 1.1701674744047748
|
| 1704 |
+
},
|
| 1705 |
+
"nvfp4": {
|
| 1706 |
+
"contexts": 96,
|
| 1707 |
+
"max_kld": 29.875577926635742,
|
| 1708 |
+
"mean_kld": 0.5132592529574869,
|
| 1709 |
+
"mean_ref_top1_prob": 0.8422180790368852,
|
| 1710 |
+
"median_context_kld": 0.4626252669584593,
|
| 1711 |
+
"median_context_p99": 6.88368558883667,
|
| 1712 |
+
"p90_context_kld": 0.8069560432184525,
|
| 1713 |
+
"positions": 196512,
|
| 1714 |
+
"top1_agreement": 0.8380149812734082,
|
| 1715 |
+
"worst_context_id": 635,
|
| 1716 |
+
"worst_context_kld": 1.8701465055601982
|
| 1717 |
+
}
|
| 1718 |
+
},
|
| 1719 |
+
"key": "code_docs_issues",
|
| 1720 |
+
"label": "Source code, tests, technical documentation, and issue discussions",
|
| 1721 |
+
"relative_to_run": 0.7579636362608442
|
| 1722 |
+
}
|
| 1723 |
+
]
|
| 1724 |
+
},
|
| 1725 |
+
"overall": {
|
| 1726 |
+
"all_quantized": {
|
| 1727 |
+
"contexts": 768,
|
| 1728 |
+
"max_kld": 42.83122253417969,
|
| 1729 |
+
"mean_kld": 1.4355058325437036,
|
| 1730 |
+
"mean_ref_top1_prob": 0.72091871112155,
|
| 1731 |
+
"median_context_kld": 1.4206255482738561,
|
| 1732 |
+
"median_context_p99": 11.263713836669922,
|
| 1733 |
+
"p90_context_kld": 2.255998387184873,
|
| 1734 |
+
"positions": 1572096,
|
| 1735 |
+
"top1_agreement": 0.6330885645660316,
|
| 1736 |
+
"worst_context_id": 942,
|
| 1737 |
+
"worst_context_kld": 3.2817656972546416
|
| 1738 |
+
},
|
| 1739 |
+
"deployed": {
|
| 1740 |
+
"contexts": 768,
|
| 1741 |
+
"max_kld": 42.91187286376953,
|
| 1742 |
+
"mean_kld": 1.1652832681101482,
|
| 1743 |
+
"mean_ref_top1_prob": 0.72091871112155,
|
| 1744 |
+
"median_context_kld": 1.1185765191055959,
|
| 1745 |
+
"median_context_p99": 10.098844528198242,
|
| 1746 |
+
"p90_context_kld": 1.8833169039892543,
|
| 1747 |
+
"positions": 1572096,
|
| 1748 |
+
"top1_agreement": 0.6725715223497801,
|
| 1749 |
+
"worst_context_id": 439,
|
| 1750 |
+
"worst_context_kld": 2.959684619608816
|
| 1751 |
+
},
|
| 1752 |
+
"mxfp8": {
|
| 1753 |
+
"contexts": 768,
|
| 1754 |
+
"max_kld": 44.75519561767578,
|
| 1755 |
+
"mean_kld": 0.46808904126519385,
|
| 1756 |
+
"mean_ref_top1_prob": 0.72091871112155,
|
| 1757 |
+
"median_context_kld": 0.4141621782891876,
|
| 1758 |
+
"median_context_p99": 5.832452774047852,
|
| 1759 |
+
"p90_context_kld": 0.8408755750586642,
|
| 1760 |
+
"positions": 1572096,
|
| 1761 |
+
"top1_agreement": 0.8047657394968246,
|
| 1762 |
+
"worst_context_id": 216,
|
| 1763 |
+
"worst_context_kld": 1.6596375001742996
|
| 1764 |
+
},
|
| 1765 |
+
"nvfp4": {
|
| 1766 |
+
"contexts": 768,
|
| 1767 |
+
"max_kld": 35.18211364746094,
|
| 1768 |
+
"mean_kld": 0.6588457176668272,
|
| 1769 |
+
"mean_ref_top1_prob": 0.72091871112155,
|
| 1770 |
+
"median_context_kld": 0.6296393578504391,
|
| 1771 |
+
"median_context_p99": 7.511585235595703,
|
| 1772 |
+
"p90_context_kld": 1.1252841317147846,
|
| 1773 |
+
"positions": 1572096,
|
| 1774 |
+
"top1_agreement": 0.7603428798241328,
|
| 1775 |
+
"worst_context_id": 346,
|
| 1776 |
+
"worst_context_kld": 2.0271412242925955
|
| 1777 |
+
}
|
| 1778 |
+
},
|
| 1779 |
+
"primary": "deployed"
|
| 1780 |
+
}
|
unsloth-gemma-4-26B-A4B-it-NVFP4/strata.md
ADDED
|
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|
| 1 |
+
# Fidelity by domain - gemma-4-26B-A4B-it / unsloth-gemma-4-26B-A4B-it-NVFP4
|
| 2 |
+
|
| 3 |
+
768 contexts, 1572096 scored positions, mean 1.16528327, reference top-1 72.1%, top-1 agreement 67.2572%.
|
| 4 |
+
|
| 5 |
+
`x run` is the domain's mean divided by the run's mean, so 1.00 is a domain that degrades exactly as much as the model as a whole. `Median ctx` and `p90 ctx` are the spread of per-context means within the domain.
|
| 6 |
+
|
| 7 |
+
## Ranked by domain
|
| 8 |
+
|
| 9 |
+
| Domain | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
|
| 10 |
+
|---|---|---|---|---|---|---|---|
|
| 11 |
+
| Other multilingual content | 36 | 70.7% | 1.48858881 | 1.28 | 1.43453338 | 2.35286937 | 2.685779 |
|
| 12 |
+
| Natural dialogue, instruction following, and assistance | 96 | 77.3% | 1.39235292 | 1.19 | 1.35528403 | 2.21189446 | 2.696145 |
|
| 13 |
+
| Chinese across several content types | 72 | 63.3% | 1.28909623 | 1.11 | 1.30697944 | 1.95793367 | 2.490834 |
|
| 14 |
+
| Encyclopedic and factual reference | 96 | 68.9% | 1.28788107 | 1.11 | 1.31897195 | 1.84772937 | 2.498949 |
|
| 15 |
+
| Scientific and technical exposition | 96 | 69.8% | 1.25331400 | 1.08 | 1.27416524 | 1.83992214 | 2.742501 |
|
| 16 |
+
| News, history, economics, legal analysis, and essays | 72 | 68.9% | 1.12880327 | 0.97 | 1.07002075 | 1.77502999 | 2.512052 |
|
| 17 |
+
| Literary, narrative, and creative writing | 72 | 62.9% | 1.10590731 | 0.95 | 1.05297524 | 1.81587063 | 2.959685 |
|
| 18 |
+
| Structured data, tool calls, APIs, JSON, and tables | 36 | 83.3% | 0.97822020 | 0.84 | 0.85293840 | 1.66969589 | 1.836153 |
|
| 19 |
+
| Worked mathematics, science, and formal reasoning | 96 | 72.4% | 0.93756733 | 0.80 | 0.84777938 | 1.52997248 | 1.981230 |
|
| 20 |
+
| Source code, tests, technical documentation, and issue discussions | 96 | 84.2% | 0.88324234 | 0.76 | 0.78676521 | 1.46387904 | 2.537016 |
|
| 21 |
+
|
| 22 |
+
## The same domains across the ladder
|
| 23 |
+
|
| 24 |
+
Each column is a separate measurement of the same contexts, so a domain that moves between columns is one the format treats differently.
|
| 25 |
+
|
| 26 |
+
| Domain | deployed | mxfp8 | nvfp4 | all_quantized |
|
| 27 |
+
|---|---|---|---|---|
|
| 28 |
+
| Other multilingual content | 1.48858881 | 0.60547717 | 0.83417492 | 1.78787763 |
|
| 29 |
+
| Natural dialogue, instruction following, and assistance | 1.39235292 | 0.59360268 | 0.81214064 | 1.63223190 |
|
| 30 |
+
| Chinese across several content types | 1.28909623 | 0.46550453 | 0.68079090 | 1.42905706 |
|
| 31 |
+
| Encyclopedic and factual reference | 1.28788107 | 0.48326683 | 0.70706639 | 1.60899741 |
|
| 32 |
+
| Scientific and technical exposition | 1.25331400 | 0.49939479 | 0.69630321 | 1.64129071 |
|
| 33 |
+
| News, history, economics, legal analysis, and essays | 1.12880327 | 0.46316973 | 0.63722594 | 1.41930026 |
|
| 34 |
+
| Literary, narrative, and creative writing | 1.10590731 | 0.39760530 | 0.61395641 | 1.34757069 |
|
| 35 |
+
| Structured data, tool calls, APIs, JSON, and tables | 0.97822020 | 0.48035757 | 0.61317963 | 1.19883758 |
|
| 36 |
+
| Worked mathematics, science, and formal reasoning | 0.93756733 | 0.39846297 | 0.55025835 | 1.22613314 |
|
| 37 |
+
| Source code, tests, technical documentation, and issue discussions | 0.88324234 | 0.36808736 | 0.51325925 | 1.10842929 |
|
| 38 |
+
|
| 39 |
+
## Ranked by source dataset
|
| 40 |
+
|
| 41 |
+
| Source | Contexts | Ref top-1 | Mean KLD | x run | Median ctx | p90 ctx | Worst ctx |
|
| 42 |
+
|---|---|---|---|---|---|---|---|
|
| 43 |
+
| wikipedia_ru | 6 | 68.3% | 2.13370892 | 1.83 | 2.09868144 | 2.65187879 | 2.651879 |
|
| 44 |
+
| wikipedia_cs | 6 | 70.6% | 1.81702746 | 1.56 | 2.26407436 | 2.68577882 | 2.685779 |
|
| 45 |
+
| wildchat | 96 | 77.3% | 1.39235292 | 1.19 | 1.35528403 | 2.21189446 | 2.696145 |
|
| 46 |
+
| wikipedia_zh | 39 | 62.4% | 1.38978578 | 1.19 | 1.35828013 | 1.96439666 | 2.490834 |
|
| 47 |
+
| wikipedia_de | 7 | 77.2% | 1.36396752 | 1.17 | 1.37212058 | 1.48300965 | 1.483010 |
|
| 48 |
+
| wikipedia_en | 96 | 68.9% | 1.28788107 | 1.11 | 1.31897195 | 1.84772937 | 2.498949 |
|
| 49 |
+
| scientific_papers | 96 | 69.8% | 1.25331400 | 1.08 | 1.27416524 | 1.83992214 | 2.742501 |
|
| 50 |
+
| wikipedia_ja | 7 | 69.8% | 1.23796801 | 1.06 | 1.25633812 | 1.56559478 | 1.565595 |
|
| 51 |
+
| open_news | 23 | 66.5% | 1.22226201 | 1.05 | 1.27663216 | 1.95651965 | 2.199544 |
|
| 52 |
+
| wikipedia_fr | 3 | 69.4% | 1.20526674 | 1.03 | 1.20455543 | 1.31146527 | 1.311465 |
|
| 53 |
+
| wikisource_zh | 33 | 64.5% | 1.17009949 | 1.00 | 1.19123931 | 1.49533335 | 2.208613 |
|
| 54 |
+
| public_domain_review | 26 | 64.7% | 1.15914697 | 0.99 | 1.06068198 | 1.74898456 | 2.288028 |
|
| 55 |
+
| wikipedia_es | 7 | 67.9% | 1.15077571 | 0.99 | 1.00411189 | 2.10786814 | 2.107868 |
|
| 56 |
+
| public_domain_books | 72 | 62.9% | 1.10590731 | 0.95 | 1.05297524 | 1.81587063 | 2.959685 |
|
| 57 |
+
| regulations | 23 | 76.1% | 1.00104294 | 0.86 | 0.97176037 | 1.64848997 | 2.512052 |
|
| 58 |
+
| starcoder_structured | 36 | 83.3% | 0.97822020 | 0.84 | 0.85293840 | 1.66969589 | 1.836153 |
|
| 59 |
+
| github_code | 52 | 87.8% | 0.94707813 | 0.81 | 0.83596054 | 1.65602768 | 2.537016 |
|
| 60 |
+
| libretexts | 96 | 72.4% | 0.93756733 | 0.80 | 0.84777938 | 1.52997248 | 1.981230 |
|
| 61 |
+
| stackv2 | 44 | 80.0% | 0.80780005 | 0.69 | 0.78676521 | 1.23354769 | 1.890014 |
|
| 62 |
+
|
| 63 |
+
## Reading
|
| 64 |
+
|
| 65 |
+
- Weakest domain: **Other multilingual content** at 1.48858881, 1.28x the run mean of 1.16528327 over 36 context(s).
|
| 66 |
+
- Strongest domain: **Source code, tests, technical documentation, and issue discussions** at 0.88324234, 0.76x the run mean. The spread across domains is 1.7x.
|
| 67 |
+
- Every cell is weakest on the same domain (Other multilingual content), so the ranking is a property of the model and the text rather than of one format.
|
| 68 |
+
|