sugarknight
/

erabi-practical-v1-experimental

@@ -22,7 +22,7 @@ This is an **experimental**, uncalibrated choice-ranking model. It is not an off
 - Base: [knowledgator/gliclass-instruct-large-v1.0](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0), Apache-2.0, 438,672,897 parameters.
 - First fine-tune: one epoch on 2,414 Practical V1 training records, peak learning rate 2.5e-6, 151 optimizer steps, microbatch 2, gradient accumulation 8, fp16 AMP.
 - Second, exploratory fine-tune (2026-09-23): one selected epoch on 175 privately held Exam-QA transformations mixed with 175 deterministic Practical V1 replay records, learning rate 1.5e-6, 22 optimizer steps, maximum training length 1,024 tokens.
-- Practical V1 data consists of original synthetic Japanese, English, and Simplified Chinese examples in six task families, generated and answer-blind rejudged with DeepSeek V4.1 Flash via OrcaRouter.
 - The Exam-QA source was filtered and transformed with the same DeepSeek model. Symbolic answer labels were mapped to source choice text. Ambiguous, multi-answer, figure-dependent, partial-credit, incomplete, or over-1,024-token items were skipped. Generated distractors were train-only; validation used source-provided choices only.
 - Exam-QA source records, transformed JSONL, and API responses are **not published** pending human review and source-by-source redistribution review. They are not claimed as human gold.
 - Data and training code: [GitHub repository](https://github.com/sugarkwork/erabi/tree/main/data/practical_v1) and [training script](https://github.com/sugarkwork/erabi/blob/main/scripts/train_practical_v1.py). Labels remain **unreviewed synthetic teacher agreement**, not human gold.

 - Base: [knowledgator/gliclass-instruct-large-v1.0](https://huggingface.co/knowledgator/gliclass-instruct-large-v1.0), Apache-2.0, 438,672,897 parameters.
 - First fine-tune: one epoch on 2,414 Practical V1 training records, peak learning rate 2.5e-6, 151 optimizer steps, microbatch 2, gradient accumulation 8, fp16 AMP.
 - Second, exploratory fine-tune (2026-09-23): one selected epoch on 175 privately held Exam-QA transformations mixed with 175 deterministic Practical V1 replay records, learning rate 1.5e-6, 22 optimizer steps, maximum training length 1,024 tokens.
+- Practical V1 data consists of original synthetic Japanese, English, and Simplified Chinese examples in six task families, generated and answer-blind rejudged with DeepSeek V4.1 Flash.
 - The Exam-QA source was filtered and transformed with the same DeepSeek model. Symbolic answer labels were mapped to source choice text. Ambiguous, multi-answer, figure-dependent, partial-credit, incomplete, or over-1,024-token items were skipped. Generated distractors were train-only; validation used source-provided choices only.
 - Exam-QA source records, transformed JSONL, and API responses are **not published** pending human review and source-by-source redistribution review. They are not claimed as human gold.
 - Data and training code: [GitHub repository](https://github.com/sugarkwork/erabi/tree/main/data/practical_v1) and [training script](https://github.com/sugarkwork/erabi/blob/main/scripts/train_practical_v1.py). Labels remain **unreviewed synthetic teacher agreement**, not human gold.