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implementing-llms-litgpt

Implements and fine-tunes LLMs using LitGPT with LoRA, QLoRA, and FSDP training workflows.

5|2|Updated Aug 10, 2026
Search Tags:#PyTorch#LoRA#LLM fine-tuning#fsdp#QLoRA#pretraining#litgpt
One-click install
npx skills add https://github.com/MedTiLab/Auto-meta-analysis --skill implementing-llms-litgpt-medtilab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: implementing-llms-litgpt
Source: https://github.com/MedTiLab/Auto-meta-analysis/tree/main/skills/model-architecture/litgpt
Command: npx skills add https://github.com/MedTiLab/Auto-meta-analysis --skill implementing-llms-litgpt-medtilab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires litgpt, torch, transformers, and includes references (resource) components.

What problem does it solve? Training and fine-tuning large language models typically involves navigating complex abstraction layers and fragmented tooling. This Skill provides clean, single-file LLM implementations with production-ready training workflows, making it straightforward to fine-tune, pretrain, quantize, and deploy models like Llama, Gemma, Phi, Qwen, and Mistral. ## Core Features & Use Cases - Fine-tuning with LoRA/QLoRA: Train adapters on consumer GPUs (12-16GB VRAM) using configurable rank, alpha, and dropout parameters, then merge weights for deployment. - Pretraining and distributed training: Pretrain models from scratch with multi-GPU FSDP sharding, mixed precision, and gradient accumulation across nodes. - Model conversion and deployment: Quantize checkpoints to 4-bit, convert to GGUF for llama.cpp, and serve models through a FastAPI endpoint. - Use Case: Fine-tune Phi-2 with LoRA on a custom Alpaca-format JSON dataset using a single 16GB GPU, merge the adapter weights, and deploy the merged model behind an inference API. ## Quick Start Ask the AI to fine-tune microsoft/phi-2 with LoRA on your instruction dataset using LitGPT and show the training command.

Frequently Asked Questions about implementing-llms-litgpt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune Llama with LoRA using LitGPT?▼

Run litgpt finetune_lora with the model name, a JSON dataset in Alpaca format, and LoRA parameters like lora_r 16 and lora_alpha 32. A 7B model with LoRA needs roughly 16GB of GPU memory, and checkpoints save automatically to the output directory.

What models does LitGPT support for fine-tuning?▼

LitGPT supports over 20 model families including Llama 3, Mistral, Mixtral, Gemma, Phi, Qwen, Falcon, and Pythia, ranging from 135M to 405B parameters. Run litgpt download list to see all available models.

LitGPT vs Axolotl for LLM fine-tuning?▼

LitGPT offers clean single-file implementations suited for understanding architectures and educational use, while Axolotl provides more fine-tuning features through YAML configs. Choose LitGPT for readable code and the Lightning ecosystem, Axolotl for broader configuration options.

How much GPU memory does QLoRA fine-tuning need?▼

QLoRA with 4-bit quantization reduces memory by about 75 percent. A 7B model needs roughly 6GB VRAM with QLoRA versus 16GB with standard LoRA, making it feasible on consumer GPUs.

Why does LitGPT training run out of memory?▼

Out-of-memory errors occur when batch size or sequence length exceeds VRAM capacity. Reduce micro_batch_size, enable 4-bit quantization with bnb.nf4, lower the LoRA rank, or shard the model across multiple GPUs with FSDP.

Can LitGPT train models across multiple GPUs?▼

Yes, LitGPT automatically enables FSDP sharding when devices is set above 1, distributing parameters, gradients, and optimizer states across GPUs. Multi-node training is supported through the num_nodes flag or SLURM and torchrun launchers.

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