/model-architecture-litgpt

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**:

pip install 'litgpt[extra]'

**Load and use any model**:

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**:

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**

# 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):

[
  {
    "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**

# 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.

Monitor training:

# View logs
tail -f out/finetune/logs.txt

# TensorBoard (if using --train.logger_name tensorboard)
tensorboard --logdir out/finetune/lightning_logs

Workflow 2: LoRA fine-tuning on single GPU

Most memory-efficient option.

LoRA Training:
- [ ] Step 1: Choose base model
- [ ] Step 2: Configure LoRA parameters
- [ ] Step 3: Train with LoRA
- [ ] Step 4: Merge LoRA weights (optional)

**Step 1: Choose base model**

For limited GPU memory (12-16GB):

• **Phi-2** (2.7B) - Best quality/size tradeoff
• **Llama 3 1B** - Smallest, fastest
• **Gemma 2B** - Good reasoning

**Step 2: Configure LoRA parameters**

litgpt finetune_lora \
  microsoft/phi-2 \
  --data JSON \
  --data.json_path data/my_dataset.json \
  --lora_r 16 \          # LoRA rank (8-64, higher=more capacity)
  --lora_alpha 32 \      # LoRA scaling (typically 2×r)
  --lora_dropout 0.05 \  # Prevent overfitting
  --lora_query true \    # Apply LoRA to query projection
  --lora_key false \     # Usually not needed
  --lora_value true \    # Apply LoRA to value projection
  --lora_projection true \  # Apply LoRA to output projection
  --lora_mlp false \     # Usually not needed
  --lora_head false      # Usually not needed

LoRA rank guide:

• `r=8`: Lightweight, 2-4MB adapters
• `r=16`: Standard, good quality
• `r=32`: High capacity, use for complex tasks
• `r=64`: Maximum quality, 4× larger adapters

**Step 3: Train with LoRA**

litgpt finetune_lora \
  microsoft/phi-2 \
  --data JSON \
  --data.json_path data/my_dataset.json \
  --lora_r 16 \
  --train.epochs 3 \
  --train.learning_rate 1e-4 \
  --train.micro_batch_size 4 \
  --train.global_batch_size 32 \
  --out_dir out/phi2-lora

# Memory usage: ~8-12GB for Phi-2 with LoRA

**Step 4: Merge LoRA weights** (optional)

Merge LoRA adapters into base model for deployment:

litgpt merge_lora \
  out/phi2-lora/final \
  --out_dir out/phi2-merged

Now use merged model:

from litgpt import LLM
llm = LLM.load("out/phi2-merged")

Workflow 3: Pretrain from scratch

Train new model on your domain data.

Pretraining:
- [ ] Step 1: Prepare pretraining dataset
- [ ] Step 2: Configure model architecture
- [ ] Step 3: Set up multi-GPU training
- [ ] Step 4: Launch pretraining

**Step 1: Prepare pretraining dataset**

LitGPT expects tokenized data. Use `prepare_dataset.py`:

python scripts/prepare_dataset.py \
  --source_path data/my_corpus.txt \
  --checkpoint_dir checkpoints/tokenizer \
  --destination_path data/pretrain \
  --split train,val

**Step 2: Configure model architecture**

Edit config file or use existing:

# config/pythia-160m.yaml
model_name: pythia-160m
block_size: 2048
vocab_size: 50304
n_layer: 12
n_head: 12
n_embd: 768
rotary_percentage: 0.25
parallel_residual: true
bias: true

**Step 3: Set up multi-GPU training**

# Single GPU
litgpt pretrain \
  --config config/pythia-160m.yaml \
  --data.data_dir data/pretrain \
  --train.max_tokens 10_000_000_000

# Multi-GPU with FSDP
litgpt pretrain \
  --config config/pythia-1b.yaml \
  --data.data_dir data/pretrain \
  --devices 8 \
  --train.max_tokens 100_000_000_000

**Step 4: Launc