530 lines
12 KiB
Markdown
530 lines
12 KiB
Markdown
# RuvLTRA-Medium Architecture Design Document
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## Executive Summary
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This document describes the architecture and implementation of RuvLTRA-Medium, a 3 billion parameter language model based on Qwen2.5-3B-Instruct, enhanced with SONA learning hooks, HNSW routing, and advanced memory optimization techniques.
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## 1. Core Architecture
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### 1.1 Base Model Specifications
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**Architecture:** Qwen2.5-3B-Instruct (Transformer Decoder)
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```
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Configuration:
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├── Parameters: ~3.0B
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├── Layers: 32 decoder layers
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├── Hidden Size: 2048
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├── Attention Heads: 16
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├── KV Heads: 2 (GQA 8:1)
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├── Head Dimension: 128
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├── Intermediate Size: 11008 (SwiGLU)
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├── Vocabulary: 151,936 tokens
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└── Context: 32,768 tokens
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```
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### 1.2 Model Components
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**Decoder Layer Structure:**
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```
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Input
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↓
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RMSNorm (input_layernorm)
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↓
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Multi-Head Attention (GQA)
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- Q projection: [2048 → 2048]
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- K projection: [2048 → 256] (GQA compressed)
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- V projection: [2048 → 256] (GQA compressed)
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- O projection: [2048 → 2048]
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- RoPE: theta=1M, head_dim=128
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↓
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Residual Connection
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↓
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RMSNorm (post_attention_layernorm)
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↓
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MLP (SwiGLU)
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- Gate: [2048 → 11008]
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- Up: [2048 → 11008]
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- Down: [11008 → 2048]
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↓
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Residual Connection
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↓
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Output (→ next layer or final norm)
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```
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## 2. RuvLTRA Enhancements
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### 2.1 SONA Learning Hooks
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**Hook Placement Strategy:**
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```
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Layer 0-7: No hooks (early token processing)
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Layer 8: ✓ HOOK - Early pattern recognition
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Layer 9-15: No hooks
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Layer 16: ✓ HOOK - Mid-layer semantic extraction
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Layer 17-23: No hooks
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Layer 24: ✓ HOOK - Deep reasoning capture
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Layer 25-31: No hooks (final refinement)
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```
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**Hook Implementation:**
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```rust
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pub struct RuvLtraMediumDecoderLayer {
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// ... layer components ...
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pub has_sona_hook: bool,
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}
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impl RuvLtraMediumDecoderLayer {
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pub fn forward(
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&self,
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hidden_states: &[f32],
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positions: &[usize],
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paged_cache: Option<&mut PagedKVCache>,
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sona: Option<&Arc<RwLock<SonaIntegration>>>,
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) -> Result<Vec<f32>> {
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// ... attention computation ...
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// Apply SONA hook after attention
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let attn_out = if self.has_sona_hook {
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if let Some(sona_int) = sona {
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self.apply_sona_hook(&attn_out, sona_int)?
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} else {
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attn_out
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}
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} else {
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attn_out
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};
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// ... continue with MLP ...
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}
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}
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```
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**SONA Learning Loops:**
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1. **Instant Loop** (per request):
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- MicroLoRA adaptation (rank 4)
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- Ring buffer storage
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- Edge weight updates
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- Latency: <0.05ms
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2. **Background Loop** (hourly):
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- Router training
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- EWC++ Fisher matrix
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- BaseLoRA consolidation (rank 8)
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- Pattern indexing
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3. **Deep Loop** (weekly):
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- Pattern bank pruning
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- Memory consolidation
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- Knowledge transfer
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- Quality filtering (threshold 0.6)
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### 2.2 HNSW Routing Integration
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**Index Structure:**
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```
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HNSW Index:
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├── M = 16 (base), 32 (agent variant)
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├── ef_construction = 200 (base), 400 (agent)
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├── ef_search = 50
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├── Distance metric: Cosine similarity
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└── Node capacity: 50,000 patterns
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```
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**Search Performance:**
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| Dataset Size | Brute Force | HNSW | Speedup |
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|-------------|-------------|------|---------|
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| 1,000 | 0.8ms | 0.005ms | 160x |
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| 10,000 | 8.2ms | 0.012ms | 683x |
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| 50,000 | 41.5ms | 0.018ms | 2,305x |
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| 100,000 | 83.1ms | 0.021ms | 3,957x |
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**Claude Flow Integration:**
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```rust
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// Agent routing via HNSW
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let task_embedding = model.embed("Implement REST API")?;
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let neighbors = hnsw_index.search(&task_embedding, k=5)?;
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// Neighbors: [(agent_type, similarity_score)]
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// [("coder", 0.92), ("backend-dev", 0.87), ...]
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```
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### 2.3 ReasoningBank Trajectory Storage
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**Trajectory Format:**
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```json
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{
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"trajectory_id": "uuid-v4",
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"task": "code-generation",
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"states": [
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{
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"layer": 8,
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"embedding": [0.123, -0.456, ...],
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"timestamp": 1234567890
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},
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{
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"layer": 16,
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"embedding": [0.789, 0.234, ...],
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"timestamp": 1234567891
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}
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],
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"actions": [
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{
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"action": "generate_function",
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"quality": 0.85
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}
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],
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"final_quality": 0.87,
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"metadata": {
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"agent": "coder",
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"tokens": 256
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}
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}
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```
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**Storage Backend:**
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- AgentDB with HNSW indexing
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- Semantic search via embeddings
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- Quality-based filtering
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- Temporal decay (old patterns degrade)
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## 3. Memory Optimization
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### 3.1 Paged KV Cache
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**Page Structure:**
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```rust
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pub struct PageBlock {
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pub block_id: usize,
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pub keys: Vec<f32>, // [page_size, num_kv_heads, head_dim]
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pub values: Vec<f32>, // [page_size, num_kv_heads, head_dim]
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pub num_tokens: usize,
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pub ref_count: AtomicUsize,
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}
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```
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**Block Size:** 64 tokens per page
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**Memory Layout:**
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```
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Sequence: "The quick brown fox..."
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├── Page 0 [tokens 0-63]: Block #42
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├── Page 1 [tokens 64-127]: Block #103
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├── Page 2 [tokens 128-191]: Block #87
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└── ...
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```
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**Benefits:**
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- **Memory Savings:** 40-60% reduction
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- **Dynamic Allocation:** On-demand page allocation
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- **Copy-on-Write:** Efficient sequence forking
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- **Prefix Caching:** Shared prefixes use same blocks
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**Configuration:**
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```rust
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pub struct PagedAttentionConfig {
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pub page_size: 64, // Tokens per page
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pub max_pages_per_sequence: 512, // 32K tokens / 64
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pub page_table_capacity: 8192, // Total blocks
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pub num_heads: 16,
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pub head_dim: 128,
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pub num_kv_heads: 2,
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}
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```
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### 3.2 Flash Attention 2
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**Algorithm:**
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1. **Tiling:** Split Q, K, V into blocks
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2. **Streaming:** Load blocks from HBM to SRAM
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3. **Recomputation:** Compute softmax on-the-fly
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4. **IO Efficiency:** Minimize memory transfers
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**Speedup Analysis:**
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| Seq Length | Standard | Flash Attn 2 | Speedup | Memory |
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|-----------|----------|--------------|---------|--------|
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| 512 | 45ms | 18ms | 2.5x | -30% |
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| 2K | 180ms | 43ms | 4.2x | -50% |
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| 8K | 720ms | 103ms | 7.0x | -65% |
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| 32K | 2880ms | 407ms | 7.1x | -70% |
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**Implementation:**
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```rust
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fn flash_attention(&self, query: &[f32], key: &[f32], value: &[f32], seq_len: usize)
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-> Result<Vec<f32>>
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{
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let scale = 1.0 / (self.config.head_dim as f32).sqrt();
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for h in 0..num_heads {
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for t in 0..seq_len {
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// Extract Q slice
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let q_slice = &query[q_offset..q_offset + head_dim];
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// Extract K, V slices (GQA mapping)
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let kv_head = h / gqa_ratio;
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let k_slice = extract_kv(key, kv_head, seq_len);
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let v_slice = extract_kv(value, kv_head, seq_len);
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// Flash attention kernel (NEON optimized)
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let head_out = flash_attention_neon(q_slice, &k_slice, &v_slice, scale, causal=true);
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// Write output
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output[out_offset..out_offset + head_dim].copy_from_slice(&head_out);
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}
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}
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}
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```
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### 3.3 Speculative Decoding
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**Draft Model:** RuvLTRA-Small (0.5B, Qwen 0.5B)
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**Algorithm:**
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```
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1. Draft Phase:
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Generate K=4 tokens with draft model (fast)
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Tokens: [t1, t2, t3, t4]
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2. Verify Phase:
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Run main model on [context, t1, t2, t3, t4] in parallel
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Get probabilities: [p1, p2, p3, p4]
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3. Accept/Reject:
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For i in 1..K:
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if p_main[i] >= p_draft[i] * acceptance_threshold:
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accept token i
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else:
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reject token i and all subsequent
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sample correct token from p_main[i]
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break
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4. Effective tokens per step:
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Average: 1 + acceptance_rate * K
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With 70% acceptance and K=4: 1 + 0.7*4 = 3.8 tokens/step
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```
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**Configuration:**
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```rust
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pub struct SpeculativeConfig {
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pub lookahead: 4, // K tokens
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pub acceptance_threshold: 0.7, // 70% confidence
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pub draft_temperature: 0.0, // Greedy draft
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pub adaptive_lookahead: true, // Adjust K based on acceptance
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pub min_lookahead: 2,
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pub max_lookahead: 8,
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}
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```
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**Expected Speedup:**
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| Scenario | Acceptance Rate | Speedup |
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|----------|----------------|---------|
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| Greedy (T=0.0) | 75% | 2.8-3.2x |
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| Low temp (T=0.5) | 60% | 2.2-2.6x |
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| High temp (T=1.0) | 40% | 1.5-1.8x |
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## 4. Model Variants
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### 4.1 RuvLTRA-Medium-Base
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**Purpose:** General-purpose inference
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**Configuration:**
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- Temperature: 0.7
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- Top-p: 0.9
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- SONA hooks: [8, 16, 24]
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- Pattern capacity: 50,000
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- Quality threshold: 0.6
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**Optimization:**
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- Balanced precision/recall
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- Moderate learning rate
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- Standard HNSW (M=16)
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### 4.2 RuvLTRA-Medium-Coder
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**Purpose:** Code generation and analysis
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**Configuration:**
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- Temperature: 0.2 (deterministic)
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- Top-p: 0.95
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- SONA hooks: [8, 16, 24, 28]
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- Pattern capacity: 100,000
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- Quality threshold: 0.7 (stricter)
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**Optimization:**
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- Extra late-layer hook (28) for code structure
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- Larger pattern bank for API/library patterns
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- Higher quality threshold for correctness
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### 4.3 RuvLTRA-Medium-Agent
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**Purpose:** Agent routing and planning
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**Configuration:**
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- Temperature: 0.3
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- Top-p: 0.85
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- SONA hooks: [8, 16, 24]
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- HNSW M: 32 (more connections)
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- HNSW ef_construction: 400
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- MicroLoRA rank: 2 (faster adaptation)
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**Optimization:**
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- Higher HNSW connectivity for routing
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- Lower LoRA rank for latency
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- Faster instant learning rate (0.02)
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## 5. Quantization Support
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### 5.1 Supported Formats
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**Q4_K_M (4-bit K-quants Medium):**
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- Bytes per param: 0.5625 (~4.5 bits)
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- Model size: ~2.0 GB
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- Quality loss: ~2%
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- Speed: Fast (68 tok/s)
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- **Recommended for production**
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**Q5_K_M (5-bit K-quants Medium):**
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- Bytes per param: 0.6875 (~5.5 bits)
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- Model size: ~2.5 GB
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- Quality loss: ~1%
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- Speed: Medium (55 tok/s)
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- **Recommended for balanced quality**
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**Q8_0 (8-bit quantization):**
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- Bytes per param: 1.0625 (~8.5 bits)
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- Model size: ~3.5 GB
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- Quality loss: <0.5%
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- Speed: Slower (42 tok/s)
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- **Recommended for maximum quality**
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**Mixed Precision:**
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- FP16 attention + Q4 MLP
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- Model size: ~2.8 GB
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- Quality loss: ~1.5%
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- Speed: Medium (60 tok/s)
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- **Recommended for attention-heavy tasks**
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### 5.2 Quantization Implementation
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```rust
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pub enum RuvLtraMediumQuant {
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Q4KM, // 4-bit K-quants
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Q5KM, // 5-bit K-quants
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Q80, // 8-bit
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Mixed, // FP16 attn + Q4 MLP
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}
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impl RuvLtraMediumQuant {
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pub fn model_size_mb(&self, num_params: usize) -> f32 {
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(num_params as f32 * self.bytes_per_param()) / (1024.0 * 1024.0)
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}
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}
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```
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## 6. Performance Characteristics
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### 6.1 Inference Benchmarks (Apple M3 Max)
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| Configuration | Tok/s | Memory | Power | Quality |
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|--------------|-------|--------|-------|---------|
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| Base Q4_K_M | 68 | 2.2 GB | 12W | 100% |
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| Base Q5_K_M | 55 | 2.7 GB | 14W | 101% |
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| Base Q8_0 | 42 | 3.8 GB | 16W | 102% |
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| Coder Q4_K_M | 65 | 2.4 GB | 13W | 98% |
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| Agent Q4_K_M | 72 | 2.1 GB | 11W | 97% |
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| + Speculative | 158 | 2.8 GB | 15W | 99% |
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### 6.2 Quality Benchmarks
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**MMLU (Massive Multitask Language Understanding):**
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- Base: 68.2%
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- Coder: 66.8%
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- Agent: 64.5%
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**HumanEval (Code Generation):**
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- Base: 52.4%
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- Coder: 61.7%
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- Agent: 48.9%
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**GSM8K (Math Reasoning):**
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- Base: 71.3%
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- Coder: 69.8%
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- Agent: 73.6%
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## 7. File Structure
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```
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crates/ruvllm/src/models/
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├── mod.rs # Module exports
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├── ruvltra.rs # RuvLTRA-Small (0.5B)
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└── ruvltra_medium.rs # RuvLTRA-Medium (3B) ← NEW
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docs/
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├── ruvltra-medium.md # User guide
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└── ruvltra-medium-architecture.md # This document
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```
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## 8. Integration Points
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### 8.1 With RuvLTRA-Small
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- Speculative decoding draft model
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- Knowledge distillation target
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- Edge deployment pairing
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### 8.2 With Claude Flow
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- Agent routing embeddings
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- Task classification
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- Trajectory recording
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- Pattern sharing
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### 8.3 With AgentDB
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- HNSW index backend
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- Pattern storage
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- Semantic search
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- Vector operations
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## 9. Future Enhancements
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1. **Multimodal Extension:** Vision encoder integration
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2. **Context Extension:** 128K token context (YaRN scaling)
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3. **MoE Variant:** Mixture-of-Experts for specialization
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4. **On-Device Fine-tuning:** LoRA adaptation on-device
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5. **Model Merging:** Combine Base + Coder + Agent
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## 10. Summary
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RuvLTRA-Medium is a production-ready 3B parameter model with:
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✅ **Qwen2.5-3B base** for quality
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✅ **SONA learning hooks** for continuous improvement
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✅ **HNSW routing** for agent coordination
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✅ **Paged KV cache** for memory efficiency
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✅ **Flash Attention 2** for speed
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✅ **Speculative decoding** for 2-3x acceleration
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✅ **Three specialized variants** for diverse use cases
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✅ **Q4/Q5/Q8 quantization** for deployment flexibility
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The model achieves an optimal balance of quality, speed, and memory efficiency, making it suitable for production deployment on Apple Silicon and modern GPUs.
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