implementing-llms-litgpt

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.

Quality

86%

Does it follow best practices?

Run evals on this skill

Adds up to 20 points to the overall score

View guide

Securityby

Passed

No findings from the security scan

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.)

Repository: Orchestra-Research/AI-Research-SKILLs
Path: 01-model-architecture/litgpt/SKILL.md
Commit: 773a529

Last updated: about 12 hours ago
First committed: 8 months ago

Also appears in

synthetic-sciences/openscience

In sync

since Jul 27, 2026

Is this your skill?

If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.