ml-training

# ML Training Capability Pack > Cross-agent portable judgment for ML model training on cloud GPU. Covers platform selection, LoRA/QLoRA fine-tuning for LLM and voice models, data preparation, cost estimation, and human-AI collaboration workflows. **CONSUMES**: Training data (JSONL/ShareGPT/audio+transcript pairs), base model name, hardware constraints, budget. **PRODUCES**: Platform recommendation, tool selection, training configuration, cost estimate. > **INTERFACE**: ai-voice-production pack defers voice training platform selection to this pack's `platform-selection.md`. This pack defers voice-specific tool selection (GPT-SoVITS, VoxCPM2 configs, audio quality thresholds) to ai-voice-production pack. When both packs load for voice training: this pack takes precedence for platform/cost decisions; ai-voice-production takes precedence for tool selection and audio quality. --- ## Step 0: Prerequisites - **Python 3.10+** — required by Unsloth, LlamaFactory, Axolotl - **pip or uv** — install in virtual environment (never global) - **Cloud account** — at least one of: Google (Colab/Kaggle), RunPod, Vast.ai - **Chrome + Claude MCP extension** — for browser-automated Colab workflows (optional) - **jq + awk** — required by `scripts/dataset-check.sh` / `cost-estimate.sh` / `vram-fit.sh` Verify: `python3 --version` --- ## Step 1: Context Detection | User Signal | Load Reference | |---|---| | fine-tune, 微调, LoRA, QLoRA, train model, 训练模型 | `references/lora-finetune.md` | | Colab...