README.md

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

<div align="center">

# RuvLTRA Medium

[![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-medium)
[![GGUF](https://img.shields.io/badge/Format-GGUF-green)](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md)

**⚖️ Balanced Model for General-Purpose Tasks**

</div>

---

## Overview

RuvLTRA Medium provides the sweet spot between capability and resource usage. Ideal for desktop applications, development workstations, and moderate-scale deployments.

## Model Card

| Property | Value |
|----------|-------|
| **Parameters** | 1.1 Billion |
| **Quantization** | Q4_K_M |
| **Context** | 8,192 tokens |
| **Size** | ~669 MB |
| **Min RAM** | 2 GB |
| **Recommended RAM** | 4 GB |

## 🚀 Quick Start

```bash
# Download
wget https://huggingface.co/ruv/ruvltra-medium/resolve/main/ruvltra-1.1b-q4_k_m.gguf

# Run inference
./llama-cli -m ruvltra-1.1b-q4_k_m.gguf \
  -p "Explain quantum computing in simple terms:" \
  -n 512 -c 8192
```

## 💡 Use Cases

- **Development**: Code assistance and generation
- **Writing**: Content creation and editing
- **Analysis**: Document summarization
- **Chat**: Conversational AI applications

## 🔧 Integration

### Rust
```rust
use ruvllm::hub::ModelDownloader;

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

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

model_path = hf_hub_download("ruv/ruvltra-medium", "ruvltra-1.1b-q4_k_m.gguf")
llm = Llama(model_path=model_path, n_ctx=8192)
```

### OpenAI-Compatible Server

```bash
python -m llama_cpp.server \
  --model ruvltra-1.1b-q4_k_m.gguf \
  --host 0.0.0.0 --port 8000
```

## Performance

| Platform | Tokens/sec |
|----------|------------|
| M2 Pro (Metal) | 65 tok/s |
| RTX 4080 (CUDA) | 95 tok/s |
| i9-13900K (CPU) | 25 tok/s |

---

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