# implementing-llms-litgpt

This skill helps implement, train, and fine-tune LLMs with LitGPT. Use it for model architecture work and LoRA or QLoRA training workflows.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 948 installs · ★ 13956
- Author: Orchestra Research
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/litgpt/SKILL.md
- Updated: 2026-10-11
- HTML: https://affpapa.org/en/nevibecode/skills/implementing-llms-litgpt

## What it does

The skill is built around LitGPT and gives an agent guidance for implementing and training language models. It covers 20+ pretrained architectures, including Llama, Gemma, Phi, Qwen, and Mistral, with single-file implementations and no abstraction layers. The listed dependencies are `litgpt`, `torch`, and `transformers`.

The workflows cover downloading checkpoints, preparing JSON datasets, full fine-tuning, LoRA training, pretraining, and merging adapter weights. Commands include `litgpt finetune`, `litgpt finetune_lora`, `litgpt pretrain`, and `litgpt merge_lora`.

Full fine-tuning for 7B models requires a `40GB+` GPU, so check memory before choosing that path. The examples use a `16GB` GPU for LoRA and report `~8-12GB` for Phi-2 with LoRA.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill implementing-llms-litgpt -a claude-code
```
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/litgpt/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/litgpt/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/litgpt/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/litgpt/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Orchestra-Research/AI-Research-SKILLs litgpt
```

2. **Manually: put the files in place**
- File: `.github/skills/litgpt/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Orchestra-Research/AI-Research-SKILLs --path 01-model-architecture/litgpt
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/litgpt/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Orchestra-Research/AI-Research-SKILLs --skill litgpt -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Orchestra-Research/AI-Research-SKILLs/tree/HEAD/01-model-architecture/litgpt/SKILL.md

## When to use it
- implement an LLM architecture with LitGPT
- download a pretrained checkpoint with `litgpt download`
- prepare a JSON dataset for fine-tuning
- run full, LoRA, or QLoRA training
- merge LoRA weights into a base model

## Example prompts
- Set up LoRA fine-tuning for `microsoft/phi-2` with `data/my_dataset.json` using LitGPT.
- Prepare a full fine-tuning workflow for `meta-llama/Meta-Llama-3-8B` and check the GPU requirement.
- Show how to pretrain with `litgpt pretrain` and `config/pythia-160m.yaml`.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 23be93780b40b30c.

BEGIN THIRD-PARTY FILE 23be93780b40b30c
````text
---
name: implementing-llms-litgpt
description: Implements and trains LLMs using Lightning AI's LitGPT with 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral). Use when need clean model implementations, educational understanding of architectures, or production fine-tuning with LoRA/QLoRA. Single-file implementations, no abstraction layers.
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [Model Architecture, LitGPT, Lightning AI, LLM Implementation, LoRA, QLoRA, Fine-Tuning, Llama, Gemma, Phi, Mistral, Educational]
dependencies: [litgpt, torch, transformers]
---

# LitGPT - Clean LLM Implementations

## Quick start

LitGPT provides 20+ pretrained LLM implementations with clean, readable code and production-ready training workflows.

**Installation**:
```bash
pip install 'litgpt[extra]'
```

**Load and use any model**:
```python
from litgpt import LLM

# Load pretrained model
llm = LLM.load("microsoft/phi-2")

# Generate text
result = llm.generate(
    "What is the capital of France?",
    max_new_tokens=50,
    temperature=0.7
)
print(result)
```

**List available models**:
```bash
litgpt download list
```

## Common workflows

### Workflow 1: Fine-tune on custom dataset

Copy this checklist:

```
Fine-Tuning Setup:
- [ ] Step 1: Download pretrained model
- [ ] Step 2: Prepare dataset
- [ ] Step 3: Configure training
- [ ] Step 4: Run fine-tuning
```

**Step 1: Download pretrained model**

```bash
# Download Llama 3 8B
litgpt download meta-llama/Meta-Llama-3-8B

# Download Phi-2 (smaller, faster)
litgpt download microsoft/phi-2

# Download Gemma 2B
litgpt download google/gemma-2b
```

Models are saved to `checkpoints/` directory.

**Step 2: Prepare dataset**

LitGPT supports multiple formats:

**Alpaca format** (instruction-response):
```json
[
  {
    "instruction": "What is the capital of France?",
    "input": "",
    "output": "The capital of France is Paris."
  },
  {
    "instruction": "Translate to Spanish: Hello, how are you?",
    "input": "",
    "output": "Hola, ¿cómo estás?"
  }
]
```

Save as `data/my_dataset.json`.

**Step 3: Configure training**

```bash
# Full fine-tuning (requires 40GB+ GPU for 7B models)
litgpt finetune \
  meta-llama/Meta-Llama-3-8B \
  --data JSON \
  --data.json_path data/my_dataset.json \
  --train.max_steps 1000 \
  --train.learning_rate 2e-5 \
  --train.micro_batch_size 1 \
  --train.global_batch_size 16

# LoRA fine-tuning (efficient, 16GB GPU)
litgpt finetune_lora \
  microsoft/phi-2 \
  --data JSON \
  --data.json_path data/my_dataset.json \
  --lora_r 16 \
  --lora_alpha 32 \
  --lora_dropout 0.05 \
  --train.max_steps 1000 \
  --train.learning_rate 1e-4
```

**Step 4: Run fine-tuning**

Training saves checkpoints to `out/finetune/` automatically.
````
END THIRD-PARTY FILE 23be93780b40b30c

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