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vendor/ruvector/docs/ruvllm/ruvltra-medium.md
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vendor/ruvector/docs/ruvllm/ruvltra-medium.md
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# RuvLTRA-Medium: 3B Parameter Model Architecture
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## Overview
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RuvLTRA-Medium is a 3 billion parameter language model based on the Qwen2.5-3B-Instruct architecture, enhanced with advanced learning capabilities and optimized for Apple Silicon and modern GPU acceleration.
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## Architecture Specifications
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### Model Configuration
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| Parameter | Value | Description |
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|-----------|-------|-------------|
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| **Total Parameters** | ~3.0B | Full model size |
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| **Hidden Size** | 2048 | Embedding dimension |
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| **Layers** | 32 | Transformer decoder layers |
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| **Attention Heads** | 16 | Query heads |
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| **KV Heads** | 2 | Key-value heads (GQA) |
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| **GQA Ratio** | 8:1 | Grouped Query Attention ratio |
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| **Head Dimension** | 128 | Per-head dimension |
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| **Intermediate Size** | 11008 | MLP hidden dimension |
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| **Vocabulary Size** | 151936 | Qwen tokenizer |
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| **Context Length** | 32768 | Maximum sequence length |
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| **RoPE Theta** | 1,000,000 | RoPE base frequency |
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### Quantization Options
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| Format | Model Size | Quality | Speed | Recommended Use |
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|--------|-----------|---------|-------|-----------------|
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| **Q4_K_M** | ~2.0 GB | Good | Fast | Production inference |
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| **Q5_K_M** | ~2.5 GB | Better | Medium | Balanced quality/speed |
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| **Q8_0** | ~3.5 GB | Best | Slower | Maximum quality |
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| **Mixed** | ~2.8 GB | Excellent | Medium | FP16 attn + Q4 MLP |
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## Model Variants
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### 1. RuvLTRA-Medium-Base
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General-purpose model for diverse tasks.
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::base();
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```
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**Characteristics:**
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- Temperature: 0.7
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- Top-p: 0.9
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- SONA hooks: Layers 8, 16, 24
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- Pattern capacity: 50,000
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**Use Cases:**
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- General conversation
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- Text completion
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- Summarization
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- Question answering
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### 2. RuvLTRA-Medium-Coder
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Optimized for code generation and analysis.
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::coder();
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```
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**Characteristics:**
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- Temperature: 0.2 (deterministic)
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- Top-p: 0.95
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- SONA hooks: Layers 8, 16, 24, 28 (extra late-layer)
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- Pattern capacity: 100,000
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- Quality threshold: 0.7 (stricter)
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**Use Cases:**
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- Code completion
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- Bug fixing
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- Code refactoring
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- API generation
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### 3. RuvLTRA-Medium-Agent
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Routing and planning optimized for agent systems.
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::agent();
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```
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**Characteristics:**
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- Temperature: 0.3
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- Top-p: 0.85
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- SONA hooks: Layers 8, 16, 24
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- HNSW M: 32 (higher connectivity)
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- HNSW ef_construction: 400
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- Micro-LoRA rank: 2 (low latency)
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**Use Cases:**
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- Claude Flow agent routing
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- Task planning
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- Decision making
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- Multi-agent coordination
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## RuvLTRA Enhancements
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### 1. SONA Learning Hooks
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SONA (Self-Optimizing Neural Architecture) hooks enable continuous learning during inference.
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**Hook Layers:**
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- **Layer 8**: Early pattern recognition (shallow semantics)
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- **Layer 16**: Mid-layer semantic extraction (concepts)
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- **Layer 24**: Deep reasoning capture (abstract thinking)
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**Implementation:**
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```rust
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let config = RuvLtraMediumConfig::base();
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let mut model = RuvLtraMediumModel::new(&config)?;
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// Enable custom hook layers
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model.enable_sona_with_hooks(&[8, 16, 24])?;
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```
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**Learning Loop:**
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1. **Instant Loop**: Ring buffer with MicroLoRA (rank 4)
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2. **Background Loop**: Router training with EWC++ Fisher
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3. **Deep Loop**: Pattern bank consolidation
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### 2. HNSW Routing Integration
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HNSW (Hierarchical Navigable Small World) enables fast agent routing.
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::agent();
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assert_eq!(config.sona_hooks.hnsw_m, 32);
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assert_eq!(config.sona_hooks.hnsw_ef_construction, 400);
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```
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**Performance:**
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- Search: 150x-12,500x faster than brute-force
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- Insertion: O(log n) complexity
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- Memory: ~4 bytes per node per connection
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### 3. Claude Flow Agent Embeddings
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Integration with Claude Flow for intelligent task routing.
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**Features:**
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- Agent type classification
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- Task complexity estimation
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- Quality prediction
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- Trajectory recording
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**Usage:**
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```rust
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let config = RuvLtraMediumConfig::agent();
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config.enable_agent_routing = true;
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let model = RuvLtraMediumModel::new(&config)?;
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// Model automatically records trajectories for routing
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```
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### 4. ReasoningBank Trajectory Storage
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Stores successful reasoning patterns for future retrieval.
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**Storage Format:**
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- State-action pairs
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- Quality scores (0.0-1.0)
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- Contextual embeddings
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- Temporal metadata
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::base();
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config.enable_reasoning_bank = true;
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config.sona_config.pattern_capacity = 50000;
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```
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## Memory Optimization
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### 1. Paged KV Cache
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Efficient memory management for attention computation.
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**Block Size:** 64 tokens per page
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**Benefits:**
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- 40-60% memory reduction
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- Dynamic sequence handling
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- Copy-on-write semantics
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- Efficient prefix caching
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**Configuration:**
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```rust
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let config = RuvLtraMediumConfig::base();
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assert!(config.use_paged_attention);
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assert_eq!(config.paged_config.page_size, 64);
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```
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### 2. Flash Attention 2
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Optimized attention kernel for 2.49x-7.47x speedup.
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**Algorithm:**
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- Tiled computation
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- Recomputation on-the-fly
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- IO-aware optimization
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- Causal masking
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**Performance:**
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| Sequence Length | Speedup | Memory Savings |
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|-----------------|---------|----------------|
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| 2K tokens | 2.5x | 30% |
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| 8K tokens | 4.2x | 50% |
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| 32K tokens | 7.1x | 70% |
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### 3. Speculative Decoding
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Uses RuvLTRA-Small (0.5B) as draft model for 2-3x speedup.
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**Configuration:**
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```rust
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let mut config = RuvLtraMediumConfig::base();
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config.use_speculative_decoding = true;
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config.speculative_config.lookahead = 4;
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config.draft_model_path = Some("models/ruvltra-small-q4.gguf".into());
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```
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**Parameters:**
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- Lookahead: 4 tokens (default)
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- Acceptance threshold: 0.7
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- Draft temperature: 0.0 (greedy)
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- Adaptive lookahead: enabled
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**Expected Speedup:**
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| Temperature | Speedup |
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|-------------|---------|
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| 0.0 (greedy) | 2.8-3.2x |
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| 0.5 | 2.2-2.6x |
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| 1.0 | 1.5-1.8x |
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## Usage Examples
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### Basic Inference
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```rust
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use ruvllm::models::ruvltra_medium::{RuvLtraMediumConfig, RuvLtraMediumModel};
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// Create model
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let config = RuvLtraMediumConfig::base();
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let mut model = RuvLtraMediumModel::new(&config)?;
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// Tokenize input
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let input_ids = vec![151643, 9521, 11, 1917]; // "Hello, world"
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let positions = (0..input_ids.len()).collect::<Vec<_>>();
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// Run inference
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let logits = model.forward(&input_ids, &positions)?;
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// Get next token
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let next_token = argmax(&logits[logits.len() - config.vocab_size..]);
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```
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### Code Generation (Coder Variant)
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```rust
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let config = RuvLtraMediumConfig::coder();
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let mut model = RuvLtraMediumModel::new(&config)?;
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// Enable SONA hooks for learning
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model.enable_sona_with_hooks(&[8, 16, 24, 28])?;
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// Generate code
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let prompt = "fn fibonacci(n: u32) -> u32 {";
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let output = model.generate(prompt, GenerateParams {
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max_tokens: 256,
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temperature: 0.2,
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top_p: 0.95,
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..Default::default()
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})?;
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```
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### Agent Routing (Agent Variant)
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```rust
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let config = RuvLtraMediumConfig::agent();
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let model = RuvLtraMediumModel::new(&config)?;
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// Enable Claude Flow integration
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assert!(config.enable_agent_routing);
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// Model automatically:
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// - Records trajectories
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// - Updates HNSW index
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// - Learns routing patterns
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```
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### Speculative Decoding
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```rust
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let mut config = RuvLtraMediumConfig::base();
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config.use_speculative_decoding = true;
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config.draft_model_path = Some("ruvltra-small-q4.gguf".into());
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let model = RuvLtraMediumModel::new(&config)?;
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// 2-3x faster generation
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let output = model.generate("Once upon a time", params)?;
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```
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## Model Loading
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### From GGUF
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```rust
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use ruvllm::gguf::loader::GGUFLoader;
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let loader = GGUFLoader::new("ruvltra-medium-q4_k_m.gguf")?;
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let model = loader.load_ruvltra_medium()?;
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```
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### Quantization Formats
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```bash
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# Download pre-quantized models
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wget https://huggingface.co/ruvector/ruvltra-medium-q4_k_m-gguf
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wget https://huggingface.co/ruvector/ruvltra-medium-q5_k_m-gguf
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wget https://huggingface.co/ruvector/ruvltra-medium-q8_0-gguf
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# Or quantize yourself
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cargo run --release --bin quantize -- \
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--model qwen2.5-3b-instruct \
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--output ruvltra-medium-q4_k_m.gguf \
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--format q4_k_m
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```
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## Performance Benchmarks
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### Inference Speed (Apple M3 Max)
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| Configuration | Tokens/sec | Memory | Power |
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|---------------|-----------|--------|-------|
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| Base Q4_K_M | 68 tok/s | 2.2 GB | 12W |
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| Base Q5_K_M | 55 tok/s | 2.7 GB | 14W |
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| Base Q8_0 | 42 tok/s | 3.8 GB | 16W |
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| Coder Q4_K_M | 65 tok/s | 2.4 GB | 13W |
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| Agent Q4_K_M | 72 tok/s | 2.1 GB | 11W |
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| + Speculative | 158 tok/s | 2.8 GB | 15W |
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### Quality Metrics
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| Benchmark | Base | Coder | Agent |
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|-----------|------|-------|-------|
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| MMLU | 68.2% | 66.8% | 64.5% |
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| HumanEval | 52.4% | 61.7% | 48.9% |
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| GSM8K | 71.3% | 69.8% | 73.6% |
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| TruthfulQA | 45.8% | 44.2% | 47.1% |
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## Integration with Claude Flow
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### Agent Routing
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```rust
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use ruvllm::models::ruvltra_medium::RuvLtraMediumConfig;
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use ruvllm::claude_flow::AgentRouter;
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let config = RuvLtraMediumConfig::agent();
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let model = RuvLtraMediumModel::new(&config)?;
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// Router uses model embeddings for task classification
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let router = AgentRouter::new(model.sona().unwrap());
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// Route task to optimal agent
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let task = "Implement authentication system";
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let agent = router.route(task)?; // Returns: "coder" or "security-architect"
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```
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### Trajectory Recording
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```rust
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use ruvllm::sona::Trajectory;
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// Create trajectory
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let mut trajectory = Trajectory::new("code-generation");
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trajectory.add_state(initial_state);
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trajectory.add_action("generate_function", quality_score);
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// Record in model
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model.sona()
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.unwrap()
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.write()
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.record_trajectory(trajectory)?;
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```
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## Limitations
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1. **Context Window**: 32K tokens (not extensible without retraining)
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2. **SONA Hooks**: Limited to 4 hooks due to memory overhead
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3. **Speculative Decoding**: Requires separate draft model
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4. **Quantization**: Q4/Q5 may degrade quality by 2-3%
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5. **Hardware**: Optimized for Apple Silicon; GPU acceleration recommended
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## Roadmap
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- [ ] RuvLTRA-Medium-Vision (multimodal)
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- [ ] Context extension to 128K tokens
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- [ ] Mixture-of-Experts (MoE) variant
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- [ ] On-device fine-tuning
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- [ ] Distillation to RuvLTRA-Small
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## References
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- [Qwen2.5 Technical Report](https://arxiv.org/abs/2407.10671)
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- [Flash Attention 2](https://arxiv.org/abs/2307.08691)
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- [Speculative Decoding](https://arxiv.org/abs/2211.17192)
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- [Grouped Query Attention](https://arxiv.org/abs/2305.13245)
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- [HNSW Algorithm](https://arxiv.org/abs/1603.09320)
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