Fine-tune LLMs on single GPUs with optimized VRAM usage
Skill for programmatic Unsloth Core fine-tuning on a single GPU, focused on VRAM sizing, chat templates with loss masking and export formats.
19.1.0Add to Favorites
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.
Reports
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Overview
A skill for driving Unsloth Core from code. It targets three hard parts of a run: VRAM sizing, chat template and loss masking, and export format choice. Use for programmatic single-GPU fine-tuning. It excludes the Unsloth Desktop app and Studio web UI.
What it does
Unsloth Fine-Tuning is a skill for training large language models with Unsloth. Unsloth uses 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 run out of memory.
The skill covers Unsloth Core, the Python API, because that is what an agent can drive programmatically. The Desktop app and the Studio web UI are interactive, so they are out of scope.
The hard parts of an Unsloth run are not the training call itself. They are three things: sizing the job against available VRAM, getting the chat template and loss masking right, and choosing an export format that the target runtime can actually load. The skill is organized around those three.
When to use - and when NOT to
Use it when an agent needs to fine-tune a model with Unsloth from code, especially on a single GPU with limited memory, and you want help with VRAM sizing, chat template and loss masking, and export format choices.
Do not use it for the Unsloth Desktop app or the Studio web UI, which the skill explicitly excludes.
Inputs and outputs
The inputs are a model to fine-tune, a training dataset, and the GPU you have available. The outputs are a fine-tuned model and an exported artifact in a format the target runtime can load. The skill's stated concerns for that path are fitting the job into available VRAM, applying the correct chat template with proper loss masking, and picking a loadable export format.
Who it's for
Engineers and agents that run single-GPU LLM fine-tuning jobs programmatically and want to avoid the common failure points of memory sizing, template and masking mistakes, and unusable exports.
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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