LitGPT - Clean LLM Implementations

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: Launch pretraining

For large-scale pretraining on cluster:

# Using SLURM
sbatch --nodes=8 --gpus-per-node=8 \
  pretrain_script.sh

# pretrain_script.sh content:
litgpt pretrain \
  --config config/​pythia-1b.yaml \
  --data.data_dir /​shared/​data/​pretrain \
  --devices 8 \
  --num_nodes 8 \
  --train.global_batch_size 512 \
  --train.max_tokens 300_000_000_000

Workflow 4: Convert and deploy model

Export LitGPT models for production.

Model Deployment:
- [ ] Step 1: Test inference locally
- [ ] Step 2: Quantize model (optional)
- [ ] Step 3: Convert to GGUF (for llama.cpp)
- [ ] Step 4: Deploy with API

Step 1: Test inference locally

from litgpt import LLM

llm = LLM.load("out/​phi2-lora/​final")

# Single generation
print(llm.generate("What is machine learning?"))

# Streaming
for token in llm.generate("Explain quantum computing", stream=True):
    print(token, end="", flush=True)

# Batch inference
prompts = ["Hello", "Goodbye", "Thank you"]
results = [llm.generate(p) for p in prompts]

Step 2: Quantize model (optional)

Reduce model size with minimal quality loss:

# 8-bit quantization (50% size reduction)
litgpt convert_lit_checkpoint \
  out/​phi2-lora/​final \
  --dtype bfloat16 \
  --quantize bnb.nf4

# 4-bit quantization (75% size reduction)
litgpt convert_lit_checkpoint \
  out/​phi2-lora/​final \
  --quantize bnb.nf4-dq  # Double quantization

Step 3: Convert to GGUF (for llama.cpp)

python scripts/​convert_lit_checkpoint.py \
  --checkpoint_path out/​phi2-lora/​final \
  --output_path models/​phi2.gguf \
  --model_name microsoft/​phi-2

Step 4: Deploy with API

from fastapi import FastAPI
from litgpt import LLM

app = FastAPI()
llm = LLM.load("out/​phi2-lora/​final")

@app.post("/​generate")
def generate(prompt: str, max_tokens: int = 100):
    result = llm.generate(
        prompt,
        max_new_tokens=max_tokens,
        temperature=0.7
    )
    return {"response": result}

# Run: uvicorn api:app --host 0.0.0.0 --port 8000

When to use vs alternatives

Use LitGPT when:

• Want to understand LLM architectures (clean, readable code)
• Need production-ready training recipes
• Educational purposes or research
• Prototyping new model ideas
• Lightning ecosystem user

Use alternatives instead:

• Axolotl/​TRL: More fine-tuning features, YAML configs
• Megatron-Core: Maximum performance for >70B models
• HuggingFace Transformers: Broadest model support
• vLLM: Inference-only (no training)

Common issues

Issue: Out of memory during fine-tuning

Use LoRA instead of full fine-tuning:

# Instead of litgpt finetune (requires 40GB+)
litgpt finetune_lora  # Only needs 12-16GB

Or enable gradient checkpointing:

litgpt finetune_lora \
  ... \
  --train.gradient_accumulation_iters 4  # Accumulate gradients

Issue: Training too slow

Enable Flash Attention (built-in, automatic on compatible hardware):

# Already enabled by default on Ampere+ GPUs (A100, RTX 30/​40 series)
# No configuration needed

Use smaller micro-batch and accumulate:

--train.micro_batch_size 1 \
--train.global_batch_size 32 \
--train.gradient_accumulation_iters 32  # Effective batch=32

Issue: Model not loading

Check model name:

# List all available models
litgpt download list

# Download if not exists
litgpt download meta-llama/​Meta-Llama-3-8B

Verify checkpoints directory:

ls checkpoints/
# Should see: meta-llama/​Meta-Llama-3-8B/

Issue: LoRA adapters too large

Reduce LoRA rank:

--lora_r 8  # Instead of 16 or 32

Apply LoRA to fewer layers:

--lora_query true \
--lora_value true \
--lora_projection false \  # Disable this
--lora_mlp false  # And this

Advanced topics

Supported architectures: See references/​supported-models.md for complete list of 20+ model families with sizes and capabilities.

Training recipes: See references/​training-recipes.md for proven hyperparameter configurations for pretraining and fine-tuning.

FSDP configuration: See references/​distributed-training.md for multi-GPU training with Fully Sharded Data Parallel.

Custom architectures: See references/​custom-models.md for implementing new model architectures in LitGPT style.

Hardware requirements

• GPU: NVIDIA (CUDA 11.8+), AMD (ROCm), Apple Silicon (MPS)
• Memory:
  • Inference (Phi-2): 6GB
  • LoRA fine-tuning (7B): 16GB
  • Full fine-tuning (7B): 40GB+
  • Pretraining (1B): 24GB
• Storage: 5-50GB per model (depending on size)

Resources

• GitHub: https://github.com/Lightning-AI/litgpt
• Docs: https://lightning.ai/docs/litgpt
• Tutorials: https://lightning.ai/docs/litgpt/tutorials
• Model zoo: 20+ pretrained architectures (Llama, Gemma, Phi, Qwen, Mistral, Mixtral, Falcon, etc.)