Agent skill · writing content · zechenzhangagi
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.
Why this skill is useful
Provides domain-specific knowledge for implementing vision-language models and multi-turn image conversations that the AI wouldn't reliably generate on its own.
What it needs
Requires pillow, torch, transformers installed locally. About 4k tokens when loaded. Last updated 2026-06-16. 11,472 stars on the source repository.
What this skill does
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 Basic usage Available models Model Parameters VRAM Quality ------- ------------ ------ --------- LLaVA-v1.5-7B 7B ~14 GB Good LLaVA-v1.5-13B 13B ~28 GB Better LLaVA-v1.6-34B 34B ~70 GB Best CLI usage Web UI (Gradio) Multi-turn conversations Common tasks Image captioning Visual question answering Object detection (textual) Scene understanding Document understanding Training custom model Quantization (reduce VRAM) Best practices 1. Start with 7B model - Good quality, manageable VRAM 2. Use 4-bit quantization - Reduces VRAM significantly 3. GPU required - CPU inference extremely slow 4. Clear prompts - Specific questions get better answers 5. Multi-turn conversations - Maintain conversation context 6. Temperature 0.2-0.7 - Balance creativity/consistency 7. maxnewtokens 512-1024 - For detailed responses 8. Batch processing - Process multiple images sequentially Performance Model VRAM (FP16) VRAM (4-bit) Speed (tokens/s) ------- ------------- -------------- ------------------ 7B ~14 GB ~4 GB ~20 13B ~28 GB ~8 GB ~12 34B ~70 GB ~18 GB ~5 On A100 GPU Benchmarks LLaVA achieves competitive scores on: VQAv2: 78.5% GQA: 62.0% MM-Vet: 35.4% MMBench: 64.3% Limitations 1. Hallucinations - May describe things not in image 2. Spatial reasoning - Struggles with precise locations 3. Small text - Difficulty reading fine print 4. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills zechenzhangagi/llava