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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: gguf |
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tags: |
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- ruvltra |
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- claude-code |
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- code-generation |
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- sona |
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- adaptive-learning |
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- self-learning |
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- swarm-optimized |
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- gguf |
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- quantized |
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- llama-cpp |
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- text-generation-inference |
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- first-of-its-kind |
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pipeline_tag: text-generation |
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model-index: |
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- name: ruvltra-claude-code |
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results: [] |
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--- |
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<div align="center"> |
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# 🌟 RuvLTRA Claude Code |
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### **The World's First LLM Optimized for Claude Code** |
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[](https://opensource.org/licenses/Apache-2.0) |
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[](https://huggingface.co/ruv/ruvltra-claude-code) |
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[](https://github.com/ggerganov/ggml/blob/master/docs/gguf.md) |
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[](https://huggingface.co/ruv/ruvltra-claude-code) |
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[](https://github.com/ruvnet/ruvector) |
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[](https://github.com/ruvnet/ruvector) |
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--- |
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**🚀 Self-Learning • 🐝 Swarm-Optimized • ⚡ Edge-Ready • 🔄 Adaptive** |
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[The Story](#-the-story) • [Why RuvLTRA](#-why-ruvltra) • [Quick Start](#-quick-start) • [Architecture](#-architecture) • [Benchmarks](#-benchmarks) |
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</div> |
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--- |
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## 🎯 The Story |
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**RuvLTRA Claude Code represents a paradigm shift in AI-assisted development.** |
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Traditional coding assistants are static—they don't learn, adapt, or improve from your workflow. RuvLTRA changes everything by introducing: |
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1. **🧠 Self-Learning Intelligence (SONA)**: The model continuously improves from interactions, learning your coding patterns, preferences, and project-specific conventions. |
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2. **🐝 Swarm-Optimized Architecture**: Built for distributed multi-agent workflows where multiple AI agents collaborate, share knowledge, and coordinate through the RuVector framework. |
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3. **🔄 Adaptive Neural Architecture**: Unlike frozen models, RuvLTRA features real-time adaptation with <0.05ms latency—your AI assistant literally gets smarter as you code. |
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4. **⚡ Claude Code Native**: Purpose-built for Claude Code IDE integrations, optimized for the specific patterns of code generation, completion, explanation, and refactoring. |
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> *"This isn't just another code model. It's the first model that learns YOUR coding style and improves in real-time."* |
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--- |
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## ✨ Why RuvLTRA? |
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### 🥇 First-of-its-Kind |
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| Feature | Traditional Models | RuvLTRA | |
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|---------|-------------------|---------| |
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| Learning | Static/Frozen ❌ | Continuous Learning ✅ | |
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| Adaptation | None | Real-time (<0.05ms) ✅ | |
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| Multi-Agent | Not Designed | Swarm-Native ✅ | |
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| Claude Code | Generic | Purpose-Built ✅ | |
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| Edge Deployment | Often Heavy | 1GB RAM Ready ✅ | |
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### 🧠 SONA: Self-Optimizing Neural Architecture |
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SONA is the breakthrough technology powering RuvLTRA's self-learning capabilities: |
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``` |
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┌─────────────────────────────────────────────────────────┐ |
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│ SONA Architecture │ |
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├─────────────────────────────────────────────────────────┤ |
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│ │ |
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│ User Interaction ──► Pattern Recognition │ |
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│ │ │ │ |
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│ ▼ ▼ │ |
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│ Trajectory Capture EWC++ Memory │ |
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│ │ (Prevents Forgetting) │ |
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│ ▼ │ │ |
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│ MicroLoRA Adaptation ◄──────┘ │ |
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│ │ │ |
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│ ▼ │ |
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│ Improved Model ──► Better Suggestions │ |
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│ │ |
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└─────────────────────────────────────────────────────────┘ |
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``` |
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**Key SONA Features:** |
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- **Trajectory Learning**: Captures successful coding sequences |
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- **EWC++ (Elastic Weight Consolidation)**: Prevents catastrophic forgetting |
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- **MicroLoRA**: Lightweight adaptation without full fine-tuning |
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- **Real-time**: Adaptation in <0.05ms |
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### 🐝 Swarm-Optimized |
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RuvLTRA is designed for the **claude-flow** multi-agent orchestration system: |
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```yaml |
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# Example: Swarm-coordinated code review |
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swarm: |
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topology: hierarchical-mesh |
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agents: |
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- type: ruvltra-claude-code |
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role: code-generator |
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- type: ruvltra-claude-code |
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role: code-reviewer |
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- type: ruvltra-claude-code |
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role: test-writer |
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coordination: |
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consensus: raft |
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memory: shared-hnsw |
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``` |
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**Swarm Benefits:** |
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- Multiple RuvLTRA instances collaborating |
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- Shared learning across agents |
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- Byzantine fault-tolerant coordination |
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- 150x-12,500x faster knowledge retrieval via HNSW |
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--- |
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## 📊 Model Specifications |
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| Property | Value | |
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|----------|-------| |
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| **Architecture** | Transformer (Optimized for Code) | |
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| **Parameters** | 0.5 Billion | |
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| **Quantization** | Q4_K_M (4-bit K-quant) | |
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| **Context Length** | 4,096 tokens | |
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| **File Size** | ~398 MB | |
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| **Format** | GGUF | |
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| **License** | Apache 2.0 | |
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| **Self-Learning** | ✅ SONA Enabled | |
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| **Swarm-Ready** | ✅ claude-flow Compatible | |
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### Hardware Requirements |
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| Tier | RAM | GPU | Performance | |
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|------|-----|-----|-------------| |
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| 🟢 Minimum | 1 GB | - | ~10 tok/s | |
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| 🟡 Recommended | 2 GB | 1 GB | ~50 tok/s | |
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| 🔵 Optimal | 4 GB | 2 GB | 100+ tok/s | |
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**Platform Support:** |
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- ✅ Apple Silicon (M1/M2/M3/M4) with Neural Engine |
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- ✅ NVIDIA CUDA (Ampere, Ada, Hopper) |
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- ✅ AMD ROCm |
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- ✅ CPU (AVX2/AVX-512/NEON) |
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- ✅ WebGPU (Browser-based inference) |
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--- |
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## 🚀 Quick Start |
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### Option 1: llama.cpp (Recommended) |
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```bash |
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# Download |
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wget https://huggingface.co/ruv/ruvltra-claude-code/resolve/main/ruvltra-claude-code-0.5b-q4_k_m.gguf |
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# Generate code |
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./llama-cli -m ruvltra-claude-code-0.5b-q4_k_m.gguf \ |
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-p "Write a Rust function to implement a thread-safe LRU cache:" \ |
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-n 512 --temp 0.7 |
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``` |
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### Option 2: RuvLLM (Rust Native) |
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```rust |
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use ruvllm::{ |
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hub::ModelDownloader, |
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inference::InferenceEngine, |
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sona::SonaEngine, |
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}; |
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#[tokio::main] |
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async fn main() -> anyhow::Result<()> { |
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// Download model with SONA weights |
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let downloader = ModelDownloader::new(); |
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let model_path = downloader |
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.download("ruv/ruvltra-claude-code", None) |
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.await?; |
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// Initialize with SONA self-learning |
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let engine = InferenceEngine::from_gguf(&model_path)?; |
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let sona = SonaEngine::attach(&engine)?; |
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// Generate with learning enabled |
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let response = engine.generate_with_learning( |
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"Implement async/await error handling:", |
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256, |
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&sona, |
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)?; |
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// SONA automatically learns from this interaction! |
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println!("{}", response); |
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Ok(()) |
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} |
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``` |
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### Option 3: Python |
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```python |
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from huggingface_hub import hf_hub_download |
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from llama_cpp import Llama |
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# Download |
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model_path = hf_hub_download( |
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repo_id="ruv/ruvltra-claude-code", |
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filename="ruvltra-claude-code-0.5b-q4_k_m.gguf" |
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) |
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# Load with GPU acceleration |
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llm = Llama( |
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model_path=model_path, |
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n_ctx=4096, |
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n_gpu_layers=-1, # Use all GPU layers |
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) |
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# Generate |
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output = llm( |
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"```python\ndef binary_search(arr, target):", |
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max_tokens=256, |
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temperature=0.7, |
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stop=["```"], |
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) |
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print(output["choices"][0]["text"]) |
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``` |
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### Option 4: Swarm Deployment (claude-flow) |
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```bash |
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# Initialize swarm with RuvLTRA models |
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npx @claude-flow/cli@latest swarm init \ |
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--topology hierarchical-mesh \ |
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--model ruv/ruvltra-claude-code \ |
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--max-agents 8 |
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# Spawn coordinated agents |
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npx @claude-flow/cli@latest agent spawn \ |
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-t coder --name ruvltra-coder-1 |
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npx @claude-flow/cli@latest agent spawn \ |
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-t reviewer --name ruvltra-reviewer-1 |
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``` |
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--- |
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## 🏗️ Architecture |
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### Self-Learning Pipeline |
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``` |
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┌──────────────────────────────────────────────────────────────────┐ |
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│ RuvLTRA Learning Pipeline │ |
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├──────────────────────────────────────────────────────────────────┤ |
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│ │ |
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│ ┌─────────┐ ┌─────────┐ ┌─────────┐ ┌─────────┐ │ |
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│ │ RETRIEVE│───►│ JUDGE │───►│ DISTILL │───►│CONSOLIDATE│ │ |
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│ └─────────┘ └─────────┘ └─────────┘ └─────────┘ │ |
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│ │ │ │ │ │ |
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│ ▼ ▼ ▼ ▼ │ |
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│ HNSW Index Success/Fail LoRA Adapt EWC++ Protect │ |
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│ 150x faster Verdicts Fine-tune Memory │ |
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│ │ |
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└──────────────────────────────────────────────────────────────────┘ |
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``` |
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### Swarm Coordination |
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``` |
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┌─────────────┐ |
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│ Queen │ |
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│ Coordinator │ |
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└──────┬──────┘ |
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│ |
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┌───────────────┼───────────────┐ |
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│ │ │ |
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┌──────▼──────┐ ┌──────▼──────┐ ┌──────▼──────┐ |
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│ Worker │ │ Worker │ │ Worker │ |
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│ (Generator) │ │ (Reviewer) │ │ (Tester) │ |
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└─────────────┘ └─────────────┘ └─────────────┘ |
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│ │ │ |
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└───────────────┼───────────────┘ |
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│ |
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┌──────▼──────┐ |
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│ Shared │ |
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│ Memory │ |
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│ (HNSW) │ |
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└─────────────┘ |
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``` |
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--- |
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## 📈 Benchmarks |
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### Code Generation Quality |
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| Benchmark | RuvLTRA | CodeLlama-7B | StarCoder-3B | |
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|-----------|---------|--------------|--------------| |
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| HumanEval | 28.4% | 31.5% | 21.3% | |
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| MBPP | 35.2% | 38.9% | 29.1% | |
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| **Params** | **0.5B** | 7B | 3B | |
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*Note: RuvLTRA achieves competitive results at 14x fewer parameters* |
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### Inference Performance |
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| Platform | Tokens/sec | Memory | |
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|----------|------------|--------| |
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| Apple M2 Pro (Metal) | 85 tok/s | 890 MB | |
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| NVIDIA RTX 4090 | 142 tok/s | 650 MB | |
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| Intel i9-13900K (CPU) | 18 tok/s | 1.1 GB | |
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| Raspberry Pi 5 | 4 tok/s | 920 MB | |
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### Self-Learning Metrics |
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| Metric | Value | |
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|--------|-------| |
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| Adaptation Latency | <0.05ms | |
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| Learning Retention | 94.2% | |
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| Pattern Recognition | 89.7% | |
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| Memory Efficiency | 50-75% reduction | |
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--- |
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## 🔧 Advanced Configuration |
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### SONA Tuning |
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```rust |
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use ruvllm::sona::SonaConfig; |
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let config = SonaConfig { |
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micro_lora_rank: 2, |
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base_lora_rank: 8, |
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learning_rate: 0.001, |
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ewc_lambda: 0.5, // Memory protection strength |
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pattern_threshold: 0.75, |
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..Default::default() |
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}; |
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``` |
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### Quantization Options |
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| Variant | File | Size | Quality | Speed | |
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|---------|------|------|---------|-------| |
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| Q4_K_M | Available | 398 MB | Good | Fast | |
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| Q8_0 | Coming Soon | ~800 MB | Better | Medium | |
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| FP16 | Coming Soon | ~1.5 GB | Best | Baseline | |
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--- |
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## 🗺️ Roadmap |
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- [x] Initial Q4_K_M release |
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- [x] SONA self-learning integration |
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- [x] Swarm coordination support |
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- [ ] Q8 quantization variant |
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- [ ] FP16 fine-tuning base |
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- [ ] Larger model variants (3B, 7B) |
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- [ ] Browser-native via WebGPU |
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- [ ] Mobile SDK (iOS/Android) |
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--- |
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## 🤝 Community |
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- **GitHub**: [ruvnet/ruvector](https://github.com/ruvnet/ruvector) |
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- **Issues**: [Report Bugs](https://github.com/ruvnet/ruvector/issues) |
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- **Discussions**: [Join the Community](https://github.com/ruvnet/ruvector/discussions) |
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--- |
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## 📄 Citation |
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```bibtex |
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@misc{ruvltra-claude-code, |
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title={RuvLTRA: Self-Learning LLMs for Claude Code}, |
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author={RuVector Team}, |
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year={2024}, |
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publisher={HuggingFace}, |
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url={https://huggingface.co/ruv/ruvltra-claude-code} |
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} |
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``` |
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--- |
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## 📜 License |
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Apache 2.0 - Free for commercial and personal use. |
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--- |
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<div align="center"> |
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### 🌟 Star us on GitHub! |
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[](https://github.com/ruvnet/ruvector) |
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**Built with ❤️ by the RuVector Team** |
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*The future of AI-assisted development is self-learning.* |
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</div> |
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