{
  "schema_version": 1,
  "measurement_owner": "SlayerLab / Fabryka AI",
  "status": "descriptive; training-lineage contamination scan pending",
  "source_aggregate_sha256": "fc6f67a20bdc6ada687349d35de26f6811e27766dfe1aa197082000c03535796",
  "source_generator_sha256": null,
  "benchmark_community_report": "https://github.com/slayerlabs/benchmarks-fabryka-ai/blob/8b547007526971bd2fc35dfe56ff2c7d9b802568/research/results/full-run-2026-11/README.md",
  "community_report_is_separate_measurement": true,
  "protocol": {
    "few_shot": 0,
    "dtype": "float32",
    "context_limit": 4096,
    "initial_token_id": 0,
    "special_tokens": false,
    "truncation": "left",
    "scoring": "summed continuation-token log likelihood; task-specific partial scoring for WinoGrande",
    "metric_units": "fraction; multiply by 100 for card percentages",
    "primary_metrics": {
      "arc_challenge": "acc_norm",
      "hellaswag": "acc_norm",
      "piqa": "acc_norm",
      "winogrande": "acc",
      "boolq": "acc",
      "openbookqa": "acc_norm",
      "belebele_pol": "acc",
      "hellaswag_pl": "acc_norm",
      "mmlu_pl": "acc"
    },
    "mmlu_aggregation": "micro-average over questions",
    "polish_task_definitions": "OpenGPTX ogx_hellaswagx_pl and ogx_mmlux_pl; source revision 0018eb9b",
    "task_protocols": {
      "arc_challenge": "lm-eval arc_challenge: ctx='Question: {question}\\nAnswer:', kont=' '+choices.text[i], gold=choices.label.index(answerKey); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "hellaswag": "lm-eval hellaswag: process_docs (preprocess(activity_label+': '+ctx_a+' '+ctx_b.capitalize()), endings preprocess), ctx=query, kont=' '+ending, gold=int(label); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "piqa": "lm-eval piqa (baber/piqa): ctx='Question: {goal}\\nAnswer:', kont=' '+[sol1,sol2][i], gold=int(label); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "winogrande": "lm-eval winogrande (partial scoring): ctx=sentence[:idx('_')]+option_i, kont=' '+sentence[idx+1:].strip() (wspólna), gold={'1':0,'2':1}[answer]; 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "boolq": "lm-eval boolq v2: ctx='{passage}\\nQuestion: {question}?\\nAnswer:', kont=' no'|' yes', gold=int(label); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "openbookqa": "lm-eval openbookqa: ctx=question_stem, kont=' '+choices.text[i], gold=choices.label.index(answerKey.lstrip()); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "belebele_pol": "lm-eval belebele_pol_Latn 0-shot: ctx='P: {flores_passage}\\nQ: {question.strip()}\\nA: {mc_answer1}\\nB: ...\\nC: ...\\nD: {mc_answer4}\\nAnswer:', kont=' A'..' D', gold=int(correct_answer_num)-1; 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "hellaswag_pl": "OpenGPTX ogx_hellaswagx_pl @0018eb9b (= lm-eval hellaswag process_docs na polskich polach); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32",
      "mmlu_pl": "OpenGPTX ogx_mmlux_pl-* @0018eb9b 0-shot: ctx=description(PL, doklejone BEZ separatora, jak w forku)+'{question.strip()}\\nA. {c0}\\nB. {c1}\\nC. {c2}\\nD. {c3}\\nOdpowiedź:', kont=' A'..' D', gold=answer; acc = mikro po 14042 pozycjach (jak grupa lm-eval mmlu, wagi = rozmiary); 0-shot; LL = suma log p tokenów kontynuacji; start id 0; add_special_tokens=False; _encode_pair jak HFLM; obcięcie z lewej do block; fp32"
    }
  },
  "ci": {
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    "method": "percentile bootstrap via Binomial(n,p)/n for binary item scores",
    "replicates": 10000,
    "base_seed": 20261110,
    "seed_scope": "Updated aggregate records seed 20261110; stream derivation is not supplied. Previous aggregate documented a model-specific stream.",
    "assumption": "items treated as independent; no subject, template or source clusters",
    "interpretation": "pointwise interval for one result; not a paired interval for model differences; not training-seed variance"
  },
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              "runner_sha256": "c906036b4e597e2c17ad7f1f6441e72fbf8e066946c2a7731966612674e3995e",
              "results_sha256": "5cf7805fa9c7d3d65d6af44920b6659287c621294efe4d7cde62f2055e65562e",
              "source_id_digest": "a32d2c4b15b4e22c87fc4e4717cf476baa2aa90a17f61230cef747c0a3bb61d0"
            },
            "sst2": {
              "task_id": "T21",
              "metric": "acc,none",
              "value": 0.5825688073394495,
              "unit": "score",
              "ci95": {
                "lower": 0.5493119266055045,
                "upper": 0.6135321100917431,
                "level": 0.95,
                "method": "paired_bootstrap",
                "replicates": 10000,
                "seed": 20261028
              },
              "n_scored": 872,
              "n_population": 872,
              "contamination": "unscanned",
              "state": "ok",
              "runner_sha256": "c906036b4e597e2c17ad7f1f6441e72fbf8e066946c2a7731966612674e3995e",
              "results_sha256": "d47706c62088fe772f1c26d2ed13f52dfecc8d832bbdecd14dd24c89086e9659",
              "source_id_digest": "7a716d60452e40d779aaad8da40aa11cdfb66cf0a069b6f0a5c5b38c78819ce7"
            }
          }
        }
      }
    }
  },
  "source_generator_note": "The updated approved aggregate does not supply its CI-generator SHA; evaluator SHA is retained per model. Previous aggregate: ce0822a324f805bb61f28cc8ea3197e6a9abd97a4591239349dc60141f5289db; previous CI generator: b792b5a7b4f5218acff4b3d5e9fb63c6f11d39fa26cca6fbd467758c90555a34."
}