Guide ML model training on cloud GPUs with platform selection and cost estimation.
npx skills add https://github.com/Sheldon-92/TAD --skill ml-training-sheldon-92
Please help me install this Agent Skill. Skill: ml-training Source: https://github.com/Sheldon-92/TAD/tree/main/.tad/capability-packs/ml-training Command: npx skills add https://github.com/Sheldon-92/TAD --skill ml-training-sheldon-92
ML model training on cloud GPUs is complex and costly to orchestrate; this pack provides structured guidance for platform selection, LoRA/QLoRA fine-tuning, data preparation, cost estimation, and human-AI collaboration workflows to streamline end-to-end training.
Provide your base model, training data formats, and budget, then activate the skill to get a platform recommendation and initial training setup.
High-intent search queries and answers about installing and using this skill.
Cloud GPU training cost estimation maps your VRAM requirements to hourly rates across platforms like RunPod and Vast.ai. It calculates total expenses by factoring in estimated GPU hours and specific LoRA configurations to provide a budget-aware routing recommendation.
Choosing a cloud platform for LLM training involves budget-aware routing across Colab, RunPod, Vast.ai, and Lambda. The system evaluates your specific budget and VRAM constraints to recommend the most suitable platform for your fine-tuning needs.
QLoRA fine-tuning for voice models is supported on free cloud platforms like Colab. The system guides configurations for both large language and voice models, ensuring your setup aligns with available VRAM and platform limitations.
Data preparation for voice model fine-tuning involves structuring your training data formats before activating the skill. The system provides specific data preparation pipelines tailored for both large language and voice fine-tuning workflows.
MCP browser automation supports Colab-based training workflows through human-AI collaboration. It enables automated execution and monitoring of training processes directly within the Colab environment.
VRAM requirements for QLoRA configurations depend on the base model size and fine-tuning strategy chosen. The system enforces your specific platform and VRAM requirements via provided references to ensure successful cloud GPU training.
handsomelong922Scale distributed data processing for ML pipelines across clusters.
ScientiaCapitalExport fine-tuned models to GGUF and deploy with vLLM or Ollama.
broomvaOrchestrate Google Colab GPU instances as remote training backends via SSH tunnels.
matthew-johnsonAccelerate LoRA/QLoRA fine-tuning while reducing VRAM usage.
