Initial SQL-only 1C adapter baseline

docs/runbooks/1c-lora-training.md

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+# 1C LoRA Training
+
+Цель: обучить draft LoRA adapter `qwen3-coder-30b-a3b-1c-lora-v1` поверх `qwen3-coder-30b-a3b-instruct`.
+
+## Preconditions
+
+- Полностью скачана базовая модель: `/models/base/qwen3-coder-30b-a3b-instruct`.
+- Подготовлен датасет: `plugins/1c/training/prepared/train.chat.jsonl`.
+- Есть GPU/CUDA на `docker-gpu.cin.su`.
+- В датасете достаточно проверенных примеров. Синтетические 2 записи подходят только для smoke-run, не для полезного качества.
+
+## Current Preflight Status
+
+На 2026-07-04 локальный preflight для нового Qwen3-Coder training contour не стартует, потому что:
+
+- локально нет CUDA/GPU;
+- training-зависимости не установлены в локальный Python;
+- HF-база `qwen3-coder-30b-a3b-instruct` еще не лежит в `/models/base/qwen3-coder-30b-a3b-instruct` на текущем workspace path;
+- запускать обучение нужно на `docker-gpu.cin.su`, потому что именно там есть GPU-контур для этой модели.
+
+Если файл будет удален или поврежден, восстановить/докачать базовую модель можно так:
+
+```powershell
+python scripts/download_hf_range.py qwen3-coder-30b-a3b-instruct `
+  --local-dir models/base/qwen3-coder-30b-a3b-instruct `
+  --chunk-size 16mb `
+  --retries 20
+```
+
+## Prepare Dataset
+
+```powershell
+python scripts/validate_1c_training_data.py plugins/1c/training/examples/instruction.examples.jsonl
+python scripts/prepare_1c_training_data.py --input plugins/1c/training/examples/instruction.examples.jsonl --output plugins/1c/training/prepared/train.chat.jsonl
+```
+
+## Local Preflight
+
+```powershell
+python scripts/preflight_1c_training.py
+```
+
+## Dry Run
+
+```powershell
+python scripts/train_1c_lora.py --dry-run
+```
+
+## GPU Docker Run
+
+```powershell
+docker --host ssh://docker-gpu.cin.su compose --env-file core/deploy/docker-gpu/training/1c-lora.env.example -f core/deploy/docker-gpu/training/1c-lora.compose.yaml up --abort-on-container-exit
+```
+
+Или через готовый wrapper:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/run_1c_lora_training_gpu.ps1
+```
+
+Full end-to-end orchestration for the current `Q6` route:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/train_and_publish_q6_lora.ps1
+```
+
+Preview the whole flow without executing:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/train_and_publish_q6_lora.ps1 -PlanOnly
+```
+
+Troubleshooting:
+
+```text
+docs/runbooks/q6-lora-troubleshooting.md
+```
+
+## Output
+
+Adapter path:
+
+```text
+/models/adapters/1c/qwen3-coder-30b-a3b-1c-lora-v1
+```
+
+After a successful training run:
+
+1. Run `plugins/1c/evals/smoke.yaml`.
+2. Convert the adapter for `llama.cpp` GGUF format or merge it before rebuilding the Q6 GGUF deployment artifact.
+
+GGUF adapter export on `docker-gpu`:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/convert_1c_lora_to_gguf_gpu.ps1
+```
+
+The default output path is:
+
+```text
+/models/adapters/1c/qwen3-coder-30b-a3b-1c-lora-v1.gguf
+```
+
+If the export container should reuse an existing `llama.cpp` checkout on the host without pulling updates:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/convert_1c_lora_to_gguf_gpu.ps1 -SkipClone
+```
+
+3. To launch the current GPU Q6 route with a converted adapter, use:
+
+```powershell
+powershell -NoProfile -ExecutionPolicy Bypass -File scripts/manage_gpu_q6_service.ps1 `
+  -Action start `
+  -LoraPath /models/adapters/1c/qwen3-coder-30b-a3b-1c-lora-v1.gguf
+```
+
+If you need a custom adapter scale, pass `-LoraScale 0.5` or another value.
+4. Compare base, RAG, and adapter outputs.
+5. Promote model card from `draft` only after expert review.