Litgpt

Litgpt is a community skill for fine-tuning and deploying large language models with LitGPT, covering model loading, training configuration, LoRA adaptation, quantization settings, and inference serving for customized LLM workflows.

What Is This?

Overview

Litgpt provides tools for working with large language models through the LitGPT library. It covers model loading that downloads and initializes pre-trained models from supported architectures including LLaMA, Mistral, Phi, and Gemma families, training configuration that sets learning rates, batch sizes, gradient accumulation, and precision settings for full fine-tuning or adapter methods, LoRA adaptation that adds low-rank trainable parameters to frozen base models for parameter-efficient fine-tuning, quantization settings that configure model weight precision reduction for memory-efficient inference on consumer hardware, and inference serving that loads fine-tuned checkpoints for text generation with configurable sampling parameters. The skill enables ML practitioners to customize and deploy language models with minimal infrastructure overhead.

Who Should Use This

This skill serves ML engineers fine-tuning open-weight language models for specific domains, researchers experimenting with training configurations across model architectures, and application developers deploying customized LLMs for production text generation.

Why Use It?

Problems It Solves

Fine-tuning large language models requires managing complex training loops with gradient checkpointing, mixed precision, and distributed compute configurations. Each model architecture has different configuration requirements making it difficult to switch between model families. Full fine-tuning demands GPU memory that exceeds consumer hardware capacity for large parameter models. Deploying fine-tuned models requires converting checkpoints and configuring inference parameters.

Core Highlights

Model loader initializes pre-trained weights from multiple architecture families through a unified interface. Trainer configures full fine-tuning with automatic mixed precision and gradient management. LoRA adapter adds trainable low-rank layers for memory-efficient adaptation. Inference runner loads checkpoints with quantization for deployment on limited hardware.

How to Use It?

Basic Usage

import litgpt

class ModelManager:
  def __init__(
    self,
    model_name: str
  ):
    self.name = model_name
    self.model = None

  def download(self):
    litgpt.download(
      self.name)

  def generate(
    self,
    prompt: str,
    max_tokens:
      int = 256,
    temperature:
      float = 0.7
  ) -> str:
    return litgpt\
      .generate(
        self.name,
        prompt=prompt,
        max_new_tokens=(
          max_tokens),
        temperature=(
          temperature))

  def finetune(
    self,
    data_dir: str,
    output_dir: str,
    epochs: int = 3,
    lr: float = 2e-5
  ):
    litgpt.finetune(
      self.name,
      data=data_dir,
      out_dir=(
        output_dir),
      train_epochs=(
        epochs),
      learning_rate=lr)

Real-World Examples

class LoRATrainer:
  def __init__(
    self,
    base_model: str,
    lora_r: int = 8,
    lora_alpha: int = 16
  ):
    self.model = (
      base_model)
    self.lora_r = lora_r
    self.alpha = (
      lora_alpha)

  def train(
    self,
    data_dir: str,
    output_dir: str,
    micro_batch: int = 4,
    epochs: int = 3
  ):
    litgpt.finetune_lora(
      self.model,
      data=data_dir,
      out_dir=output_dir,
      lora_r=self.lora_r,
      lora_alpha=(
        self.alpha),
      train_epochs=(
        epochs),
      train_micro_batch=(
        micro_batch))

  def merge_and_export(
    self,
    checkpoint_dir: str,
    output_dir: str
  ):
    litgpt.merge_lora(
      checkpoint_dir,
      out_dir=(
        output_dir))

  def serve(
    self,
    merged_dir: str,
    port: int = 8000
  ):
    litgpt.serve(
      merged_dir,
      port=port)

Advanced Tips

Use QLoRA with 4-bit quantized base weights to fine-tune large models on single consumer GPUs while maintaining quality close to full precision training. Prepare training data in the Alpaca format with instruction, input, and output fields for instruction-following fine-tuning. Merge LoRA weights into the base model after training for inference without adapter overhead.

When to Use It?

Use Cases

Fine-tune an open-weight LLM on domain-specific instruction data using LoRA for efficient adaptation. Quantize a fine-tuned model to 4-bit precision for deployment on consumer GPU hardware. Compare generation quality across model architectures using the same training data and evaluation prompts.

Related Topics

LLM fine-tuning, LitGPT, LoRA, quantization, language model inference, model adaptation, and open-weight model deployment.

Important Notes

Requirements

PyTorch installation with CUDA support for GPU-accelerated training. LitGPT package installed from PyPI or source. Sufficient GPU memory for the chosen model size and training method.

Usage Recommendations

Do: start with LoRA fine-tuning before attempting full parameter training to validate data quality with lower resource requirements. Use gradient checkpointing to reduce memory usage when training large models. Evaluate on a held-out dataset between training epochs to detect overfitting early.

Don't: fine-tune on datasets that are too small as models will memorize rather than generalize from limited examples. Use very high learning rates that cause catastrophic forgetting of base model capabilities. Skip evaluation and deploy fine-tuned checkpoints without testing generation quality.

Limitations

Model support depends on LitGPT architecture implementations which may lag behind new model releases. Full fine-tuning of large models requires multi-GPU setups or cloud compute resources. Quantized inference introduces minor quality degradation compared to full precision model weights.