FLAP – Fine-tune large language models locally without cloud costs

Summary: FLAP enables fine-tuning of large language models (1B to 670B+ parameters) on local GPUs with as little as 6 GB VRAM using memory-mapped sharding. It supports models like Llama, Mistral, and Qwen, eliminating cloud GPU expenses and vendor lock-in.

What it does

FLAP fine-tunes LLMs entirely on local GPUs by memory-mapped parameter sharding, allowing large models to run on limited VRAM without cloud dependency.

Who it's for

Developers and researchers needing cost-effective, local fine-tuning of large language models without cloud infrastructure.

Why it matters

It reduces fine-tuning costs by approximately 95% and removes reliance on expensive cloud GPU services.