Skip to main content 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.
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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.
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
Training saves checkpoints to out/finetune/ automatically.
# 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 tradeoffLlama 3 1B - Smallest, fastestGemma 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
r=8: Lightweight, 2-4MB adaptersr=16: Standard, good qualityr=32: High capacity, use for complex tasksr=64: Maximum quality, 4× larger adapters 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
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
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
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 configsMegatron-Core : Maximum performance for >70B modelsHuggingFace Transformers : Broadest model supportvLLM : 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
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
# 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
--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
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 Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
CLI to manage emails via IMAP/SMTP. Use `himalaya` to list, read, write, reply, forward, search, and organize emails from the terminal. Supports multiple accounts and message composition with MML (MIME Meta Language).
