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# 1C Training Data
Цель: подготовить безопасный датасет для будущего LoRA/adapter fine-tuning по 1С.
## Principles
- Сначала RAG и инструменты, потом дообучение.
- Дообучение выполняется только на проверенных примерах.
- Запрещено добавлять пароли, токены, строки подключения, персональные данные и клиентские секреты.
- Нельзя обучать модель на сырых выгрузках баз 1С.
- Каждый пример должен пройти экспертное ревью.
## Files
- Example dataset: `plugins/1c/training/examples/instruction.examples.jsonl`
- Dataset manifest: `plugins/1c/training/manifests/dataset.yaml`
- Adapter manifest: `plugins/1c/adapters/qwen3-coder-30b-a3b-1c-lora-v1.yaml`
- Registry card: `registry/model-cards/qwen3-coder-30b-a3b-1c-lora-v1.yaml`
## Validate Example Data
```powershell
python scripts/validate_1c_training_data.py plugins/1c/training/examples/instruction.examples.jsonl
```
## Prepare Chat JSONL
```powershell
python scripts/prepare_1c_training_data.py --input plugins/1c/training/examples/instruction.examples.jsonl --output plugins/1c/training/prepared/train.chat.jsonl
```
## Update Registry
```powershell
python scripts/validate_model_cards.py
python scripts/build_model_index.py
```
## Promotion Rule
Adapter status stays `draft` until:
- enough examples are collected;
- secret scan passes;
- expert review is complete;
- `plugins/1c/evals/smoke.yaml` passes on base, RAG, and adapter;
- rollback path is documented.