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implementing-llms-litgpt

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

1|Updated Mar 5, 2026
Search Tags:#model deployment#lora#fsdp#qlora#llm fine-tuning#pretraining#litgpt
One-click install
npx skills add https://github.com/Clay-HHK/claude-skills --skill implementing-llms-litgpt-clay-hhk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: implementing-llms-litgpt
Source: https://github.com/Clay-HHK/claude-skills/tree/main/litgpt
Command: npx skills add https://github.com/Clay-HHK/claude-skills --skill implementing-llms-litgpt-clay-hhk

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) with configurable rank, alpha, and dropout parameters, then merge weights for deployment. - Pretraining and distributed training: Pretrain models from scratch with FSDP multi-GPU and multi-node support via Lightning Fabric. - Model deployment: Quantize models to 4-bit, convert to GGUF for llama.cpp, and serve via FastAPI endpoints. - Use Case: Fine-tune Phi-2 on a custom Alpaca-format JSON dataset using LoRA on a single 16GB GPU, merge the adapter weights, and deploy the merged model behind a REST API. ## Quick Start Fine-tune microsoft/phi-2 with LoRA on my dataset at data/my_dataset.json using LitGPT, then merge the adapter weights for deployment.

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 3 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. Training checkpoints save automatically to the out/finetune directory, and you can merge adapters afterward with litgpt merge_lora.

What is the difference between LitGPT and Axolotl or TRL?▼

LitGPT provides clean single-file implementations suited for understanding architectures and educational use, while Axolotl and TRL offer more fine-tuning features with YAML configs. LitGPT fits Lightning ecosystem users; HuggingFace Transformers offers broader model support.

How much GPU memory does LitGPT LoRA fine-tuning require?▼

LoRA fine-tuning a 7B model requires about 16GB VRAM, while QLoRA with 4-bit quantization reduces this to roughly 6GB. Smaller models like Phi-2 need only 8GB with LoRA, and full fine-tuning of 7B models requires 40GB or more.

Does LitGPT support multi-GPU distributed training?▼

Yes, LitGPT automatically enables FSDP when devices is set above 1, sharding parameters, gradients, and optimizer states across GPUs. It supports multi-node training via SLURM or torchrun with HYBRID_SHARD as the default sharding strategy.

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 add more GPUs with FSDP sharding.

Can LitGPT models be converted to GGUF for llama.cpp?▼

Yes, use the convert_lit_checkpoint.py script with the checkpoint path and output path to produce a GGUF file. You can also quantize to 4-bit with bnb.nf4-dq before conversion to reduce model size by about 75 percent.

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