Skill · em Dados, IA e pesquisa

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…

Procedência

Antes de instalar

2 arquivos · 12,3 KB · só texto, nenhum script

Instalar na sua CLI

O comando baixa a versão fixada (commit 57f899e) direto da origem, para a pasta que a CLI lê. Precisa de curl (macOS e Linux); no Windows não há comando, porque o Rook Labs é para macOS.

Claude Code

Neste projeto: instala em .claude/skills/llava/.

d=".claude/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Global: instala em ~/.claude/skills/llava/.

d="$HOME/.claude/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Codex

Neste projeto: instala em .agents/skills/llava/.

d=".agents/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Global: instala em ~/.agents/skills/llava/.

d="$HOME/.agents/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Antigravity

Neste projeto: instala em .agents/skills/llava/.

d=".agents/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Global: instala em ~/.gemini/antigravity-cli/skills/llava/.

d="$HOME/.gemini/antigravity-cli/skills/llava"
u="https://raw.githubusercontent.com/davila7/claude-code-templates/57f899e5394bb8ca166f38eacae8f0853cbfe033/cli-tool/components/skills/ai-research/multimodal-llava"
curl -fsSL --create-dirs \
  -o "$d/SKILL.md" "$u/SKILL.md" \
  -o "$d/references/training.md" "$u/references/training.md"

Peça ao Rook

Já usa o Rook Labs? Cole no chat do Rook: instale a skill https://rooklabs.sh/marketplace/cct.llava

Prévia do SKILL.md

---
name: llava
description: 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…
version: 1.0.0
author: Orchestra Research
license: MIT
tags: [LLaVA, Vision-Language, Multimodal, Visual Question Answering, Image Chat, CLIP, Vicuna, Conversational AI, Instruction Tuning, VQA]
dependencies: [transformers, torch, pillow]
---

# 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
…

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