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1. Using OCR models with llama.cpp

   A technical guide to running lightweight OCR models (LightOnOCR, GLM-OCR, Deepseek-OCR) on low-end hardware using llama.cpp. Includes implementation details for CLI, REST APIs, and performance optimization.
   
   Topics Covered:
   
   - llama.cpp OCR integration
   - Low-spec hardware optimization
   - CLI & REST API setup
   - Quantization & Prompting
   - Hallucination mitigation

   2026-04-11 Tags: llama.cpp, ocr, multimodal models, gguf, machine learning, computer vision, local ai by klotz
2. Gemma 4

   This document details how to run Google's Gemma 4 models locally, including the E2B, E4B, 26B-A4B, and 31B variants. Gemma 4 is a family of open models supporting over 140 languages and up to 256K context, available in both dense and MoE configurations. The E2B and E4B models support image and audio input. These models can be run locally on your device and fine-tuned using Unsloth Studio. The document outlines hardware requirements, recommended settings, and best practices for prompting and multimodal use, including guidance on context length and thinking mode.

   2026-04-02 Tags: gemma 4, llm, local ai, unsloth, models, inference, fine-tuning, llama.cpp, multimodal by klotz

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