ml-training

name	ml-training
description	Machine learning model training with HuggingFace. Fine-tuning LLMs, dataset creation, GPU selection, training monitoring. Use for training custom models. Don't use for using pre-trained models via API.
disable-model-invocation	true
model	opus
effort	high
allowed-tools	Read, Write, Edit, Bash, Grep, WebFetch

ML Model Training

Pipeline

1. Define task and success metrics
2. Collect/prepare training data
3. Choose base model (by size and task fit)
4. Select GPU and estimate cost
5. Configure training (SFT, DPO, or GRPO)
6. Monitor training run
7. Evaluate on test set
8. Deploy model

Model Selection

• Small tasks (classification): Qwen 0.6B-3B
• Medium tasks (generation): Mistral 7B, Llama 8B
• Complex tasks (reasoning): Qwen 27B, Llama 70B

Training Methods

• SFT: supervised fine-tuning on examples
• DPO: preference optimization (good vs bad)
• GRPO: group relative policy optimization

Deployment

• Ollama for local inference
• LitServe for MCP server
• llama.cpp for production