Guide LoRA and QLoRA fine-tuning workflows for local model training.
npx skills add https://github.com/ZardLi1115/zedclaw --skill unsloth-zardli1115
Please help me install this Agent Skill. Skill: unsloth Source: https://github.com/ZardLi1115/zedclaw/tree/main/optional-skills/mlops/training/unsloth Command: npx skills add https://github.com/ZardLi1115/zedclaw --skill unsloth-zardli1115
Unsloth helps you fine-tune large language models efficiently by reducing training time and GPU VRAM usage while keeping results high quality.
Ask the AI to generate a step-by-step plan to fine-tune your chosen LLM with QLoRA on your GPU, including recommended hyperparameters and expected VRAM needs.
High-intent search queries and answers about installing and using this skill.
QLoRA fine-tuning reduces GPU VRAM usage and training time by applying parameter-efficient techniques. You can follow a step-by-step workflow to select models, set hyperparameters, and manage memory constraints for local training.
Recommended LoRA hyperparameters for local model training depend on your specific dataset and GPU VRAM constraints. You can generate a tailored plan that includes memory-efficient 4-bit settings to optimize training performance and avoid out-of-memory errors.
You can train large models locally without running out of VRAM by using memory-efficient 4-bit QLoRA. This approach minimizes memory requirements while maintaining high-quality results, allowing you to reference system requirements to prevent misconfiguration.
Dataset preparation involves formatting data for the fine-tuning workflow, while model export requires saving and preparing the trained output for downstream inference. Guidance covers both steps to ensure your fine-tuned model is ready for deployment.
Local fine-tuning setups fail with OOM errors when GPU VRAM is exceeded by model size or hyperparameters. Troubleshooting involves checking system requirements, applying memory-efficient 4-bit QLoRA, and adjusting configurations to resolve misconfiguration.
matthew-johnsonAccelerate LoRA/QLoRA fine-tuning while reducing VRAM usage.
ScientiaCapitalExport fine-tuned models to GGUF and deploy with vLLM or Ollama.
Bakery88Preserve facial identity across generated images using compatible face-ID methods.
GitnappConfigure LoRA and QLoRA adapters for memory-efficient LLM fine-tuning.
