Manage the full lifecycle of LLM post-training: estimate GPU memory, configure and run SFT/OSFT/LoRA/GRPO jobs, tune hyperparameters, and diagnose training failures — all without leaving the terminal.
Use when the user wants to estimate GPU memory (VRAM) requirements for a training configuration, check if a model will fit on their GPUs, or plan GPU allocation for training.
Use when the user wants to set up LLM training for the first time, or when training_hub is not yet installed/configured in the current environment.
Use when the user wants to run a training job using a saved configuration. For algorithm selection, hyperparameter advice, or troubleshooting, use the training-hub-guide skill instead.
Guides users through LLM post-training with Training Hub, including installation, algorithm selection (SFT, OSFT, LoRA), hyperparameter tuning, troubleshooting OOM errors, interpreting loss curves, and leveraging backend-specific features. Use when the user is working with training_hub, fine-tuning language models, asking about SFT/OSFT/LoRA training, or debugging GPU/CUDA training issues.
docs/README.md
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Synthetic data generation — composable blocks and YAML-defined flows for building LLM training datasets
Inference-time scaling for LLMs — generate multiple candidates and select the best using voting, scoring, or search
npx claudepluginhub red-hat-ai-innovation-team/training_hub --plugin training-hubMachine learning training and inference pipeline using cloud GPUs (Modal, Lambda Labs, RunPod) with HuggingFace ecosystem - no local GPU required
ML engineering plugin: Give your AI coding agent ML engineering superpowers.
Skills for fine-tuning language models with the Tinker API — research, debugging, and more.
Autonomous experiment loop that edits code, runs benchmarks, measures metrics, and keeps improvements or reverts — repeating forever. Works for any optimization target: LLM training loss, test speed, bundle size, build time, Lighthouse scores, and more.
Transfer learning adaptation
Core Hugging Face Hub operations through the hf CLI, including skill installation, repo management, jobs, datasets, models, Spaces, and discovery.