unsloth-finetuning

Unsloth Fine-Tuning

Overview

Unsloth trains LLMs with custom kernels that cut VRAM use and step time without changing the math, which makes single-GPU fine-tuning practical on hardware that would otherwise OOM. This skill covers Unsloth Core — the Python API — because that is what an agent can drive programmatically; the Desktop app and Studio web UI are interactive and out of scope.

The hard parts of an Unsloth run are not the training call. They are sizing the job against available VRAM, getting the chat template and loss masking right, and choosing an export format the target runtime can actually load. This skill covers those three.

When to Use This Skill

• 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

template or loss-masking bug, not a hyperparameter one.

Do not use this skill when

• 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 t