Enable vision-language chat and image understanding with CLIP and Vicuna or LLaMA models.
npx skills add https://github.com/GregsGreyCode/Logos --skill llava-gregsgreycode
Please help me install this Agent Skill. Skill: llava Source: https://github.com/GregsGreyCode/Logos/tree/main/skills/mlops/models/llava Command: npx skills add https://github.com/GregsGreyCode/Logos --skill llava-gregsgreycode
Vision-language models enable machines to understand and discuss images, enabling multimodal interactions that combine visual input with natural language.
Load a pretrained LLaVA model and start a multimodal chat by feeding an image and asking questions.
High-intent search queries and answers about installing and using this skill.
Vision-language chat requires a CLIP-based vision encoder and a language model like Vicuna or LLaMA to process images and integrate them into prompts for multi-turn image conversations.
Visual question answering combines a CLIP vision encoder with a large language model to process image inputs and generate natural language responses based on the visual content.
LLaVA supports document understanding with images by integrating visual inputs into language model prompts, enabling multimodal reasoning over text and image content.
Multi-turn image conversations require a CLIP-based vision encoder, a language model such as Vicuna or LLaMA, and tooling to process images and integrate them into chat prompts.
Open-source multimodal models offer multiple model sizes and configurable vision encoder options, allowing customization of the CLIP-based vision component paired with the language model.
Multimodal vision-language models are not suitable for text-only tasks or environments lacking the hardware required to run both a CLIP vision encoder and a large language model simultaneously.
sheawinklerEnable multimodal vision-language reasoning and conversation with images.
afel6Build multimodal vision-language agents for image chat and visual question answering.
JKhyroGenerate multimodal answers from images using CLIP and Vicuna or LLaMA models.
ovachieverAutomate LLaMA-family model fine-tuning via the Llama-Factory WebUI.
