TrainingProject 設定リファレンス

{
  "schema_version": "1.0",
  "mode": "automatic",
  "objective": "Classify support messages as billing, account_access or technical_issue.",
  "model": {"path": "./models/student", "adapter": "transformers-causal-lm", "revision": null},
  "teacher": {"provider": "openai", "reference_model_id": 123},
  "data": {
    "sources": ["./data/train.jsonl"], "teacher_sharing": "none",
    "validation_path": "./data/validation.jsonl", "test_path": "./data/test.jsonl",
    "deduplicate": true, "seed": 42
  },
  "runtime": {"target": "customer_datacenter", "devices": "auto"},
  "training": {"method": "auto"},
  "curriculum": {"topics": ["intent", "ambiguous_requests", "class_balance"], "max_improvement_cycles": 3, "stop_on_regression": true},
  "budgets": {"max_training_minutes": 120, "max_teacher_requests": 1000, "max_storage_gib": 20},
  "checkpoints": {"keep_last": 3},
  "evaluation": {"rubric": "./rubric.json", "baseline_required": true},
  "output": {"directory": "./runs/german-001", "format": "safetensors"}
}

{
  "schema_version": "1.0",
  "mode": "advanced",
  "model": {"path": "./models/student", "adapter": "transformers-causal-lm", "revision": null},
  "teacher": {"provider": "openai", "reference_model_id": 123},
  "objective": "Classify support messages as billing, account_access or technical_issue.",
  "data": {"sources": ["./data/train.jsonl"], "teacher_sharing": "none", "validation_path": "./data/validation.jsonl", "test_path": "./data/test.jsonl"},
  "runtime": {"target": "customer_datacenter", "devices": [0, 1], "distributed": {"backend": "ddp", "nodes": 1}},
  "training": {
    "method": "LoRA",
    "epochs": 3,
    "learning_rate": 0.0002,
    "batch_size": 1,
    "gradient_accumulation_steps": 16,
    "max_sequence_length": 2048,
    "precision": "bf16",
    "lora": {"rank": 16, "alpha": 32, "dropout": 0.05}
  },
  "curriculum": {
    "topics": ["intent", "ambiguous_requests", "class_balance"],
    "max_improvement_cycles": 3,
    "stop_on_regression": true
  },
  "checkpoints": {"every_steps": 100, "keep_last": 3, "resume_validation": "strict"},
  "budgets": {"max_training_minutes": 120, "max_teacher_requests": 1000, "max_storage_gib": 20},
  "evaluation": {"rubric": "./rubric.json", "baseline_required": true},
  "output": {"directory": "./runs/german-advanced-001", "format": "safetensors"}
}

SDK 0.2.0 CONFIGURATION RULES
Unknown fields, duplicate IDs, nonfinite numbers and unsafe paths are rejected.
All model, data, rubric and output paths must resolve inside the project root.
Automatic mode resolves "auto" once; save all resolved values in the plan manifest.
Advanced mode requires explicit method, epochs, learning_rate, batch_size,
gradient_accumulation_steps, max_sequence_length and precision.
SFT does not consume LoRA options. LoRA/QLoRA require a compatible adapter.
Model files and pinned revisions are verified within the project root.
Use the CLI for explicit GPU indexes; Python callers can set CUDA_VISIBLE_DEVICES
before starting Python and keep runtime.devices set to auto.
No automatic execution of untrusted model code, shell commands or plugins.
All three budgets must be finite, positive and explicitly supplied.
Storage includes owned checkpoints, datasets and artifacts, not unrelated files.
Held-out test data must not be sent to the teacher or used for weight updates.
CLI overrides are limited to the documented flags; never silently override a plan.
RUBRIC RULES
Every criterion requires a registered metric and minimum_score; descriptions are not semantic evaluators.
Built-in metrics: exact_match, macro_f1, text_similarity and chrf.
Custom deterministic scorers use register_evaluator(name, scorer, version="...").
Evaluator versions and scoring configuration are pinned in the plan.
0 <= minimum_score <= 1 for the example's normalized scale.
All required criteria must pass; do not average away a failed criterion.
The final_test.path must match data.test_path; allow_training_use must be false.
Scores and budgets in examples are requested settings, not measured results.