llava
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# LLaVA - Large Language and Vision Assistant Open-source vision-language model for conversational image understanding. ## When to use LLaVA **Use when:** - Building vision-language chatbots - Visual question answering (VQA) - Image description and captioning - Multi-turn image conversations - Visual instruction following - Document understanding with images **Metrics**: - **23,000+ GitHub stars** - GPT-4V level capabilities (targeted) - Apache 2.0 License - Multiple model sizes (7B-34B params) **Use alternatives instead**: - **GPT-4V**: Highest quality, API-based - **CLIP**: Simple zero-shot classification - **BLIP-2**: Better for captioning only - **Flamingo**: Research, not open-source ## Quick start ### Installation ```bash # Clone repository git clone https://github.com/haotian-liu/LLaVA cd LLaVA # Install pip install -e . ``` ### Basic usage ```python from llava.model.builder import load_pretrained_model from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN from llava.conversation import conv_templates from PIL import Image import torch # Load model model_path = "liuhaotian
- When to use LLaVA
- Quick start
- Installation
- Basic usage
- Available models
- CLI usage
- Web UI (Gradio)
- Multi-turn conversations
- Common tasks
- Image captioning
- Visual question answering
- Object detection (textual)
- Scene understanding
- Document understanding
Clone repository git clone https://github.com/haotian-liu/LLaVA cd LLaVA Install pip install -e . Single image query python -m llava.serve.cli \ Multi-turn conversation Then type questions interactively Launch Gradio interface
What does the llava skill do?
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
How do I install it?
Run `npx skills add Orchestra-Research/AI-Research-SKILLs --skill llava --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From Orchestra-Research/AI-Research-SKILLs, a repository with 11,391 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.