README.md

---
language:
- en
license: apache-2.0
library_name: gguf
tags:
- ruvltra
- sona
- adaptive-learning
- gguf
- quantized
- edge-device
- embedded
- iot
pipeline_tag: text-generation
---

<div align="center">

# RuvLTRA Small

[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![HuggingFace](https://img.shields.io/badge/🤗%20Hugging%20Face-Model-yellow)](https://huggingface.co/ruv/ruvltra-small)
[![GGUF](https://img.shields.io/badge/Format-GGUF-green)](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)

**📱 Compact Model Optimized for Edge Devices**

[Quick Start](#-quick-start) • [Use Cases](#-use-cases) • [Integration](#-integration)

</div>

---

## Overview

RuvLTRA Small is a compact 0.5B parameter model designed for edge deployment. Perfect for mobile apps, IoT devices, and resource-constrained environments.

## Model Card

| Property | Value |
|----------|-------|
| **Parameters** | 0.5 Billion |
| **Quantization** | Q4_K_M |
| **Context** | 4,096 tokens |
| **Size** | ~398 MB |
| **Min RAM** | 1 GB |

## 🚀 Quick Start

```bash
# Download
wget https://huggingface.co/ruv/ruvltra-small/resolve/main/ruvltra-0.5b-q4_k_m.gguf

# Run with llama.cpp
./llama-cli -m ruvltra-0.5b-q4_k_m.gguf -p "Hello, I am" -n 64
```

## 💡 Use Cases

- **Mobile Apps**: On-device AI assistant
- **IoT**: Smart home device intelligence
- **Edge Computing**: Local inference without cloud
- **Prototyping**: Quick model experimentation

## 🔧 Integration

### Rust (RuvLLM)
```rust
use ruvllm::hub::ModelDownloader;

let path = ModelDownloader::new()
    .download("ruv/ruvltra-small", None)
    .await?;
```

### Python
```python
from huggingface_hub import hf_hub_download

model = hf_hub_download("ruv/ruvltra-small", "ruvltra-0.5b-q4_k_m.gguf")
```

## Hardware Support

- ✅ Apple Silicon (M1/M2/M3)
- ✅ NVIDIA CUDA
- ✅ CPU (x86/ARM)
- ✅ Raspberry Pi 4/5

---

**License**: Apache 2.0 | **GitHub**: [ruvnet/ruvector](https://github.com/ruvnet/ruvector)