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Implementing Llms Litgpt

Checked
davila7·vlatest·ai-ml·MIT

This skill streamlines LLM integration by automatically deploying and configuring GPT models for your projects, boosting AI capabilities quickly.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill implementing-llms-litgpt -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/model-architecture-litgpt/SKILL.md
Scanned
5 files, 2,200 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
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About this skill

What it does

This skill, LitGPT, streamlines the integration of Large Language Models (LLMs) into your projects. It provides pre-built implementations for over 20 LLMs with clean code and workflows designed for production environments. LitGPT simplifies model loading, text generation, and fine-tuning, allowing you to quickly leverage powerful AI capabilities without complex setup. The skill supports both full fine-tuning and more efficient LoRA (Low-Rank Adaptation) techniques.

When to use it

  • You want to easily integrate a pre-trained LLM into your application or workflow.
  • You need to fine-tune an existing LLM on a custom dataset for improved performance on specific tasks.
  • You have limited GPU resources and require a memory-efficient fine-tuning approach (LoRA).
  • You want a quick way to experiment with different LLMs like Phi-2, Llama 3, or Gemma.

Key capabilities

  • Pre-built LLM implementations: Offers over 20 ready-to-use LLM models.
  • Model Loading: Simple command for loading pre-trained models (e.g., LLM.load("microsoft/phi-2")).
  • Text Generation: Provides a straightforward way to generate text using the loaded model.
  • Fine-tuning Support: Enables both full fine-tuning and LoRA fine-tuning techniques.
  • Dataset Format Flexibility: Supports Alpaca format for custom datasets.
  • Model Download Tooling: Includes a command (litgpt download) to easily download available models.

Example prompts

  • "Load the microsoft/phi-2 model and generate text based on the prompt: 'What is the capital of France?'"
  • "List all available LLM models that can be downloaded."
  • "Fine-tune the meta-llama/Meta-Llama-3-8B model using my custom dataset located at data/my_dataset.json."

Tips & gotchas

  • Installation: Requires pip install 'litgpt[extra]' for full functionality.
  • GPU Requirements: Full fine-tuning can require significant GPU memory (40GB+ for 7B models), while LoRA is more efficient (16GB GPU).
  • Dataset Format: Ensure your custom dataset follows the Alpaca format (instruction, input, output) and is saved as a JSON file.