README.md

metadata

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

RuvLTRA Small

📱 Compact Model Optimized for Edge Devices

Quick Start • Use Cases • Integration

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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

# 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)

use ruvllm::hub::ModelDownloader;

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

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

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License: Apache 2.0 | GitHub: ruvnet/ruvector