{
  "revision": "format-numbers-v5",
  "weights_sha256": "54602b3a2e0c484fe7ae796a4290969d3a1cc58e099c83e59948fe94889150a0",
  "public400": {
    "previous_step400": {
      "all": {
        "n": 400,
        "lexical_exact": 239,
        "exact": 110,
        "cer": 0.10115321252059307,
        "truncated": 0
      },
      "test_en": {
        "n": 100,
        "lexical_exact": 64,
        "exact": 57,
        "cer": 0.09015921734126223,
        "truncated": 0
      },
      "test_identity": {
        "n": 100,
        "lexical_exact": 92,
        "exact": 38,
        "cer": 0.014404852160727824,
        "truncated": 0
      },
      "test_zh": {
        "n": 200,
        "lexical_exact": 83,
        "exact": 15,
        "cer": 0.16789739603575593,
        "truncated": 0
      }
    },
    "current": {
      "all": {
        "n": 400,
        "lexical_exact": 232,
        "exact": 108,
        "cer": 0.10247116968698518,
        "truncated": 0
      },
      "test_en": {
        "n": 100,
        "lexical_exact": 60,
        "exact": 54,
        "cer": 0.09246115480529446,
        "truncated": 0
      },
      "test_identity": {
        "n": 100,
        "lexical_exact": 91,
        "exact": 38,
        "cer": 0.01592115238817286,
        "truncated": 0
      },
      "test_zh": {
        "n": 200,
        "lexical_exact": 81,
        "exact": 16,
        "cer": 0.16712009327633112,
        "truncated": 0
      }
    },
    "decoding": {
      "dtype": "bfloat16",
      "temperature": 0,
      "batch_size": 16,
      "max_new_tokens": 256,
      "backend": "Transformers 5.3 / PyTorch 2.10, CUDA, SDPA"
    },
    "scope": "Raw model outputs on the same reused 400 public text cases. Lexical metrics remove punctuation and retain case, numbers, negation and order; not semantic accuracy or a new blind benchmark."
  },
  "development20": {
    "offline_exact": 18,
    "sequential_orin_vllm_exact": 19,
    "cases": 20,
    "numbered_list_cases": 6,
    "numbered_list_cases_rendered_as_numbered_lines": 6,
    "scope": "Diagnostic probes, excluded from training inputs after lexical normalization. Runtime/reference mismatch retained equivalent Chinese quantity spelling. Backend/batching may change greedy BF16 output; results are not a new broad quality benchmark."
  },
  "token_metrics": {
    "status": "not_recomputed_for_current_weights",
    "historical_file": "history/step400/token-metrics.json"
  },
  "deployment_content_guard": "none; external configuration, not model weights",
  "limits": [
    "English lexical sentence matches decreased from 64/100 to 60/100.",
    "Overall lexical CER increased from 10.115% to 10.247%.",
    "Number/name correction and paragraph/list grouping are not universally reliable."
  ]
}