Fine-tune LLMs on single GPUs with optimized VRAM usage
Unsloth Fine-Tuning skill covers Unsloth Core's Python API for training LLMs using custom kernels that reduce VRAM usage and step time without altering math.
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Why it matters
Enable agents to programmatically fine-tune large language models on consumer-grade hardware by managing VRAM constraints, configuring chat templates with proper loss masking, and exporting models in runtime-compatible formats.
Outcomes
What it gets done
Size training jobs against available VRAM to prevent out-of-memory errors
Configure chat templates and loss masking for supervised fine-tuning
Select and export model formats compatible with target inference runtimes
Drive Unsloth Core Python API programmatically for automated fine-tuning workflows
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-unsloth-finetuning | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
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Overview
Unsloth Fine-Tuning
This skill covers Unsloth Core's Python API for fine-tuning large language models with custom kernels that reduce VRAM consumption and step time without changing the underlying math. It covers the three hard parts: sizing jobs against available VRAM, getting chat templates and loss masking right, and choosing export formats the target runtime can actually load. Use this skill when working with Unsloth Core's Python API to fine-tune LLMs on single-GPU hardware with limited VRAM, or when handling the configuration of training jobs to avoid out-of-memory failures while managing template and export setup.
What it does
This skill covers Unsloth Core's Python API for fine-tuning large language models on single-GPU hardware. Unsloth trains LLMs with custom kernels that cut VRAM use and step time without changing the math, making fine-tuning practical on hardware that would otherwise run out of memory. The skill covers the three hard parts of an Unsloth run: 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.
When to use - and when NOT to
Use this skill when working with Unsloth Core's Python API to fine-tune LLMs on single-GPU hardware, when dealing with VRAM constraints, or when configuring chat templates, loss masking, and export formats. Do NOT use this skill if you need to interact with Unsloth's Desktop app or Studio web UI, as those are interactive tools outside the scope of this programmatic API skill.
Inputs and outputs
Users work with parameters needed to configure an Unsloth fine-tuning job: VRAM constraints, chat template specifications, loss masking requirements, and target export format preferences. The skill covers sizing training configurations against available VRAM, configuring chat templates and loss masking, and selecting export formats for the target runtime.
Who it's for
This skill is for those who need to work with Unsloth Core's Python API to fine-tune large language models. It serves users running single-GPU setups who would otherwise face out-of-memory failures, and those who need to handle the configuration decisions around VRAM sizing, template formatting, and model export.
Source README
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.
FAQ
Common questions
Discussion
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