Using OCR models with llama.cpp

Llama.cpp has expanded its capabilities to support various small OCR models that can function effectively on low-end devices, including GPUs with 4GB VRAM and even some CPUs. Among the supported models are LightOnOCR, Qianfan-OCR, and PaddleOCR-VL, among others, as well as general-purpose multimodal models like LFM2.5-VL-450M that can execute OCR tasks. Users are guided to install llama.cpp and employ specific commands for running OCR models, with the option to deploy a server for application integration via a REST API. The post emphasizes the importance of using the correct prompt formats for different models and suggests ways to improve model performance and reduce hallucinations. The document highlights that most models are quantized to Q8_0 for optimized quality and performance, though F16 can be used for enhanced quality if hardware allows.