{
  "format": "tensor-archive-public-metric-record",
  "version": 1,
  "published_claims": {
    "deployment_savings_percent": 71.2,
    "sequential_training_vs_gear_cdc_percent": 34.3,
    "restore": "byte-exact"
  },
  "deployment_catalog": {
    "name": "smollm2-135m-four-public-loras",
    "artifacts": 5,
    "physical_bytes": {
      "self_contained_deployments": 907323227,
      "separate_files": 323258240,
      "separate_tarc": 262181190,
      "tensor_archive_catalog": 261872394,
      "packed_catalog_v1": 261585735,
      "gear_cdc_v1": 261656335
    },
    "savings_percent": {
      "packed_vs_self_contained": 71.16950969447628,
      "packed_vs_separate_files": 19.078401528140475,
      "packed_vs_gear_cdc": 0.02698195707740078
    },
    "public_protocol_sha256": "c3ad4d99ffd397c6670667640ba2ddc38e2085e0dff1556951d26cc90ae4e367",
    "receipt_sha256": "8fc9b71a07570ee7dfe7842f6ed8405417e06fe66bcc374d6837616d1ddb87d0"
  },
  "sequential_training": {
    "name": "smollm2-135m-sequential-adamw",
    "checkpoint_count": 5,
    "files": 50,
    "tensors": 4080,
    "optimizer_tensors_per_checkpoint": 544,
    "dtype": "bfloat16",
    "source_bytes": 4059928053,
    "gear_cdc_bytes": 2810466584,
    "tensor_archive_bytes": 1845964558,
    "saved_vs_gear_cdc_bytes": 964502026,
    "saved_vs_gear_cdc_percent": 34.31821717756456,
    "replay_restore_ratio_vs_exact_fixed": 0.9507465293328505,
    "restoration": {
      "byte_exact": true,
      "random_tensor_access_verified": true
    },
    "semantic_protocol_sha256": "597e742978f65da6913cfa42fd014b7763df55e89662693b93f4735bcd337a8e",
    "receipt_sha256": "5ba76594c4d68b492f0c730f4f2502ce842fb5764a59c9e9b90ac586d30a7e62"
  },
  "competitive_landscape": {
    "direct_ranking": false,
    "warning": "Different workloads and baselines; published figures are not directly comparable.",
    "reported_results": [
      {
        "system": "PyTorch DCP + zstd",
        "reported_reduction_percent": 22,
        "baseline": "uncompressed DCP checkpoints",
        "workload": "Granite-3B and Granite-8B distributed checkpoints",
        "source": "https://pytorch.org/blog/reducing-storage-footprint-and-bandwidth-usage-for-distributed-checkpoints-with-pytorch-dcp/"
      },
      {
        "system": "FM-Delta",
        "reported_reduction_percent": 50,
        "baseline": "separately stored fine-tuned foundation models",
        "workload": "up to 100 fine-tuned models",
        "source": "https://proceedings.neurips.cc/paper_files/paper/2024/hash/7b75a7339dfb256ee4b4bec028a6890b-Abstract-Conference.html"
      },
      {
        "system": "ZipLLM",
        "reported_reduction_percent": 54,
        "baseline": "storage approaches evaluated by the authors",
        "workload": "public Hugging Face LLM repositories",
        "source": "https://www.usenix.org/conference/nsdi26/presentation/wang-zirui"
      }
    ],
    "unscored_systems": [
      {
        "system": "Xet",
        "reason": "No single universal storage-reduction percentage is published for a common workload.",
        "source": "https://huggingface.co/docs/hub/xet/deduplication"
      },
      {
        "system": "Git-Theta",
        "reason": "The paper reports workload-specific versioning results rather than one common reduction percentage.",
        "source": "https://proceedings.mlr.press/v202/kandpal23b.html"
      }
    ]
  },
  "direct_benchmark": {
    "name": "TA-Bench v1",
    "record": "ta-bench-v1-summary.json",
    "same_source_bytes": true,
    "model_family": {
      "tensor_archive_bytes": 330603375,
      "zipllm_audited_restore_closure_bytes": 585350329,
      "tensor_archive_savings_vs_zipllm_closure_percent": 43.520425526232176,
      "file_roundtrip_exact": true
    },
    "training_checkpoints": {
      "tensor_archive_bytes": 1845964557,
      "tar_zstd_bytes": 2757589861,
      "gear_cdc_bytes": 2810466581,
      "file_roundtrip_exact": true
    }
  },
  "limitations": [
    "These are controlled SmolLM2-135M workloads, not customer production data.",
    "The 71.2% claim compares self-contained deployments and is not a comparison with Xet, ZipLLM or CDC.",
    "On the adapter catalog, packed Tensor Archive saved 0.027% versus normalized Gear CDC.",
    "The 34.3% result is the current technical product signal and requires independent external reproduction."
  ],
  "disclosure": {
    "algorithm_source_included": false,
    "encoder_source_included": false,
    "private_dictionary_included": false,
    "public_evidence_only": true
  }
}