# SmolVLM2-2.2B-Instruct: Ultra-Compact Vision-Language Model
## 🚀 Overview
SmolVLM2-2.2B-Instruct is a highly efficient 2.2 billion parameter vision-language model from HuggingFace, designed for image understanding, video analysis, and multimodal reasoning. Despite its compact size, it delivers impressive performance on vision tasks while running on consumer hardware.
## 🎯 Key Features
- **Ultra-compact** - Only 2.2B parameters, runs on laptops and mobile devices
- **Vision & Video** - Understands images, analyzes video frames, reads documents
- **Instruction-tuned** - Optimized for following natural language instructions
- **Fast inference** - Q4_K_M runs at ~30+ tokens/sec on M-series Macs
- **Apache 2.0** - Fully open source, no restrictions
## 📊 Capabilities
- **Image Understanding**: Describe, analyze, and answer questions about images
- **Document OCR**: Extract text and understand document layouts
- **Video Analysis**: Process video frames for temporal understanding
- **Visual Reasoning**: Solve problems requiring visual comprehension
- **Chart/Graph Reading**: Interpret data visualizations
## 🏷️ Available Versions
| Tag | Size | RAM Required | Description |
|-----|------|--------------|-------------|
| `q4_k_m` | 1.0 GB | ~4GB | **Recommended** - best quality/size ratio |
| `q8_0` | 1.8 GB | ~6GB | Higher quality, minimal loss |
| `f16` | 3.4 GB | ~8GB | Full precision, maximum quality |
## 💻 Quick Start
```bash
# Recommended version (Q4_K_M)
ollama run richardyoung/smolvlm2-2.2b-instruct "Describe this image"
# Higher quality version
ollama run richardyoung/smolvlm2-2.2b-instruct:q8_0 "What text is in this document?"
# Full precision
ollama run richardyoung/smolvlm2-2.2b-instruct:f16 "Analyze this chart"
```
## 🛠️ Example Use Cases
### Image Description
```bash
ollama run richardyoung/smolvlm2-2.2b-instruct "Describe what you see in detail"
```
### Document Analysis
```bash
ollama run richardyoung/smolvlm2-2.2b-instruct "Extract all text from this document"
```
### Visual Q&A
```bash
ollama run richardyoung/smolvlm2-2.2b-instruct "How many people are in this photo?"
```
### Video Understanding
```bash
ollama run richardyoung/smolvlm2-2.2b-instruct "What is happening in these video frames?"
```
## 📋 System Requirements
### Minimum Requirements
- **RAM**: 4GB
- **CPU**: Any modern x86_64 or ARM64
- **Storage**: 2GB free space
### Recommended Setup
- **RAM**: 8GB+
- **Device**: Apple Silicon Mac, modern laptop, or smartphone
- **Storage**: 5GB free space (for all quantizations)
## 🌟 What Makes This Model Special
1. **Tiny Footprint**: Runs on devices where larger VLMs cannot
2. **Video Support**: Native understanding of video frame sequences
3. **Efficient Architecture**: Optimized for edge deployment
4. **Multilingual**: Supports multiple languages for vision tasks
5. **Production Ready**: Battle-tested by HuggingFace team
## 🔗 Links
- **Original Model**: [HuggingFaceTB/SmolVLM2-2.2B-Instruct](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct)
- **GGUF Files**: [richardyoung/SmolVLM2-2.2B-Instruct-GGUF](https://huggingface.co/richardyoung/SmolVLM2-2.2B-Instruct-GGUF)
## 🤝 Credits
- **Original Model**: HuggingFace Team
- **GGUF Conversion**: Richard Young (deepneuro.ai)
- **Quantization**: llama.cpp
## 📝 License
Apache 2.0 - Free for commercial and personal use.
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**Note**: For vision tasks, use with an Ollama client that supports image input (e.g., Open WebUI, Ollama API with base64 images).