Enable multi-turn image conversations and visual question answering with CLIP and Vicuna/LLaMA models.
npx skills add https://github.com/KarlinskyS/hermesSkills --skill llava-karlinskys
Please help me install this Agent Skill. Skill: llava Source: https://github.com/KarlinskyS/hermesSkills/tree/main/mlops/models/llava Command: npx skills add https://github.com/KarlinskyS/hermesSkills --skill llava-karlinskys
LLaVA removes the complexity of building vision-language conversational systems by combining a CLIP-based vision encoder with Vicuna/LLaMA language models to enable multi-turn image chat, visual question answering, and instruction-following on images.
Use LLaVA to analyze an image and answer: "What is in this image?"
High-intent search queries and answers about installing and using this skill.
Build multi-turn image chatbots using a CLIP vision encoder combined with Vicuna/LLaMA backbones to maintain conversational context and answer specific questions about image content across turns.
Fine-tune vision-language models using feature alignment pretraining and visual instruction tuning, or apply LoRA-based efficient fine-tuning to adapt the language backbone to custom datasets.
Run conversational image understanding within limited VRAM by enabling optional 4-bit quantization, which reduces memory requirements while supporting GPU acceleration.
LLaVA supports document scene understanding and image captioning by combining a CLIP-based vision encoder with a Vicuna/LLaMA language model to process and describe image content.
Use a CLIP vision encoder with a Vicuna backbone to remove the complexity of building vision-language systems, enabling instruction-following and multi-turn image conversations.
While 4-bit quantization reduces VRAM usage for multi-turn image conversations, it may impact model precision during instruction tuning and complex visual question answering tasks.
NITISH-gitbitEnable image-based conversations and visual instruction tuning with CLIP and Vicuna/LLaMA models.
zerwizAnswer visual questions and caption images through conversational prompts.
GarrettRoiCombine CLIP vision encoders with Vicuna and LLaMA for visual question answering.
zangjeicyIntegrate CLIP vision encoder with LLaMA models for visual question answering.
