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Run LLM post-training (SFT, OSFT, LoRA, GRPO) with environment setup, GPU VRAM estimation, hyperparameter tuning, and loss curve troubleshooting
npx claudepluginhub red-hat-ai-innovation-team/training_hub --plugin training-hubUse 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.
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
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