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LitGPT LLM Implementation and Fine-Tuning

Lets the agent set up, fine-tune, pretrain and export LLMs with Lightning AI's LitGPT toolkit

Source Download skill
Repository
Orchestra-Research/AI-Research-SKILLs
Stars
14,195
Licence
MIT
Files
5 files

What it does

Once installed, this skill teaches the agent how to work with LitGPT, an open-source toolkit with readable single-file implementations of more than 20 popular open language models such as Llama, Gemma, Phi, Qwen and Mistral. The agent can then write and run the commands to download a model, prepare a training dataset, fine-tune it cheaply with LoRA (a method that trains small add-on weights instead of the whole model), merge the result, and pretrain a model from scratch on a cluster. It is aimed at people who want to understand or customise model internals rather than just call a hosted API.

Great for

  • ML engineers who want to fine-tune an open model on their own data with limited GPU memory
  • Students or researchers who want to read clean implementations of transformer architectures
  • Teams planning multi-GPU or multi-node pretraining with FSDP

Skip it if

  • Users without a suitable GPU: the skill's own examples need 12-16GB for LoRA and 40GB+ for full fine-tuning of 7B models, so training will fail or be impractical on ordinary laptops.
  • Anyone who wants a simple chat or API integration with no training: the skill is focused on training and model internals, and its examples write files and launch long, costly jobs.
  • Sensitive or proprietary data run through unknown setups: training commands read your datasets and write checkpoints to local folders, and gated models such as Llama may need account access.

Try asking

  • “Fine-tune microsoft/phi-2 with LoRA on my JSON instruction dataset on a single 16GB GPU”
  • “Download google/gemma-2b with LitGPT and set up the Alpaca-format data file for training”
  • “Merge my LoRA adapter from out/phi2-lora into a standalone model for deployment”
  • “Explain how the Pythia-160m config differs from Llama architecture in LitGPT”
Step-by-step guide: how to use LitGPT LLM Implementation and Fine-TuningInstall, first run, examples, tips and FAQs

Install in 3 steps

  1. 1

    Download the skill

    The ZIP contains the 5 original files, the MIT licence and an attribution note.

    litgpt.zip
  2. 2

    Add it to your agent

    Claude Code (all projects)

    mkdir -p ~/.claude/skills && unzip litgpt.zip -d ~/.claude/skills/

    Claude Code / Agent SDK (this project only)

    mkdir -p .claude/skills && unzip litgpt.zip -d .claude/skills/

    Claude apps (web/desktop): Settings → Capabilities → Skills → Upload skill, and choose the ZIP.

  3. 3

    Ask for the task

    No special command needed — the agent reads the skill's description and loads it when your request matches. Try one of the prompts above.

Get better results

  • Start with a small model such as phi-2 or Llama 3 1B and a small dataset to confirm the setup before launching long runs with large max_steps or max_tokens values.
  • Review every litgpt download, finetune and pretrain command before running it, since they can download multi-gigabyte files and consume significant GPU time and cost.
  • Keep sensitive training data in local paths and check the output folders (checkpoints/, out/) before sharing any model artifacts.

What's inside

  • references/custom-models.md
  • references/distributed-training.md
  • references/supported-models.md
  • references/training-recipes.md
  • SKILL.md

Redistributed unmodified from Orchestra-Research/AI-Research-SKILLs under the MIT licence. The assessment was drafted with AI from the skill's files on 2026-10-29. Skills can instruct your agent to run code — read SKILL.md and any scripts before installing.