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.)