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unsloth-finetuning

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Fine-tune and post-train LLMs with Unsloth Core on a single consumer GPU: VRAM sizing, LoRA/QLoRA, GRPO/DPO, chat-template correctness, and GGUF export.

When to use

• Use when fine-tuning an LLM on one GPU and VRAM is the binding constraint.
• Use when a training run OOMs and needs to be resized rather than rewritten.
• Use when doing preference or RL post-training (GRPO, DPO) on consumer hardware.
• Use when a fine-tuned model must be exported to GGUF, vLLM, or merged 16-bit weights.
• Use when a fine-tune "ran fine" but the model's output format is wrong — usually a chat
• The training is multi-node or large-scale multi-GPU. Use plain TRL with Accelerate/DeepSpeed.
• The architecture is unsupported by Unsloth. Fall back to TRL; do not force it.
• The user wants managed cloud training. That is Hugging Face Jobs, not local Unsloth.

When not to use

• The VRAM figures above are planning heuristics, not benchmarks. Confirm on target hardware.
• Architecture support changes between releases. Check upstream before assuming a model works.
• Unsloth's speed and memory claims are the project's own published figures, measured on their
• This skill does not replace environment-specific validation, testing, or expert review.
• Stop and ask for clarification if the GPU, model, dataset format or export target is unknown —

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