This article compares two ways to add vision to large language models—training a native multimodal model from scratch or attaching a visual module to a pretrained LLM—then details the VoRA approach that uses LoRA adapters to inject visual knowledge without extra inference cost.
The article analyzes the limitations of current visual language models and large reasoning models, proposes a combined Vision‑Reasoning Model (VRM), details its architecture using LLaVA, describes end‑to‑end fine‑tuning and reinforcement‑learning reward design, and argues that such models will become the next breakthrough in AI.
This article explains what Ollama is, how to download it for different operating systems, and provides detailed command‑line examples for running LLaMA 2 and the multimodal LLaVA models locally, showcasing the power of open‑source large language models on your own computer.
This tutorial walks you through downloading Ollama, an open‑source LLM platform, and demonstrates how to run the Meta LLaMA 2 text model and the multimodal LLaVA model on your own computer, including command‑line usage and image‑based queries.
This article introduces LLaVA, an open‑source large language‑visual assistant that replicates GPT‑4‑V capabilities, explains its architecture, training process, and key features, and provides step‑by‑step instructions for using the web demo, running it locally via Ollama or HuggingFace, and building a simple Gradio chatbot with code examples.
