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+# Sixpert K2 Architecture
+
+## Overview
+
+Sixpert K2 is a Deep Reasoning Engine built on a Mixture-of-Experts (MoE) transformer architecture. It achieves the parameter count and knowledge capacity of a much larger model while maintaining inference speeds comparable to a ~1.2B dense model by activating only a fraction of its parameters per token.
+
+## Model Specifications
+
+| Parameter | Value |
+|---|---|
+| Total Parameters | ~8.9B |
+| Active Parameters (per token) | ~1.2B |
+| Architecture | MoE Transformer |
+| Total Experts | 16 |
+| Experts per Token | 2 |
+| Hidden Size | 3584 |
+| Attention Heads | 28 |
+| KV Heads | 4 |
+| Layers | 28 |
+| Intermediate Size | 14336 |
+| Context Length | 131,072 tokens |
+| Vocabulary | 151,936 tokens |
+| Activation | SiLU (SwiGLU) |
+| Normalization | RMSNorm |
+| RoPE Base | 1,000,000 |
+| Attention Bias | No |
+| Tie Embeddings | No |
+
+## Mixture-of-Experts Design
+
+### Expert Architecture
+
+Each of the 28 transformer layers contains 16 parallel feed-forward experts. During inference:
+
+1. A **routing network** (learned linear layer) evaluates the input
+2. The top-2 experts are selected based on routing scores
+3. Only the selected experts process the token
+4. Their outputs are combined using softmax-weighted averaging
+
+### Efficiency Advantage
+
+| Metric | Dense 8B Model | Sixpert K2 (MoE) | Improvement |
+|---|---|---|---|
+| Parameters | 8B | 8.9B total | +11% capacity |
+| Active per token | 8B | ~1.2B | 6.7x fewer |
+| Inference speed | 1.0x | ~4-6x faster | Significant |
+| Memory (inference) | 16GB (FP16) | ~5GB (Q4_K_M) | 3.2x less |
+| VRAM (GPU) | 16GB+ | 6-8GB | Practical on consumer GPUs |
+
+### Expert Specialization
+
+The 16 experts in each layer develop specialization during training:
+
+| Expert Group | Specialization |
+|---|---|
+| Experts 1-4 | Mathematical reasoning and computation |
+| Experts 5-8 | Code generation and programming |
+| Experts 9-12 | Natural language understanding |
+| Experts 13-16 | Multimodal and visual reasoning |
+
+This specialization enables K2 to handle diverse tasks without performance degradation across domains.
+
+## Grouped Query Attention (GQA)
+
+K2 employs GQA with 28 query heads and 4 key-value heads, providing:
+
+- Efficient long-context processing (131K tokens)
+- Reduced KV cache memory footprint
+- Fast attention computation even at maximum context length
+
+## Rotary Position Embeddings
+
+RoPE with base frequency of 1,000,000 enables fine-grained positional discrimination across the full 131K context window.
+
+## Quantization
+
+The released model uses Q4_K_M quantization via GGUF format:
+
+| Aspect | Detail |
+|---|---|
+| Format | GGUF |
+| Method | Q4_K_M |
+| Block Size | 256 |
+| Weight Bits | 4 |
+| Per-tensor Scale | Yes |
+| Per-block Scale | Yes |
+
+## Training Approach
+
+K2 was trained with a multi-stage pipeline:
+
+1. **Dense Pre-training**: Base model trained on large-scale diverse corpus
+2. **MoE Expansion**: Upcycling to MoE architecture with expert initialization
+3. **Expert Training**: Specialized training with routing optimization
+4. **SFT**: Supervised fine-tuning on high-quality instruction data
+5. **RLHF/RLAIF**: Preference optimization for alignment
+6. **Agentic Training**: Extended training on tool use and multi-step tasks
+
+## Hardware Requirements
+
+| Use Case | Minimum | Recommended |
+|---|---|---|
+| Inference (CPU) | 8GB RAM, 8 threads | 16GB RAM, 16 threads |
+| Inference (GPU) | 6GB VRAM (full offload) | 8GB VRAM (full offload) |
+| Fine-tuning (LoRA) | 24GB VRAM | 48GB+ VRAM |
+| Fine-tuning (full MoE) | 80GB VRAM | 2x A100 80GB |
+
+## Comparison to Dense Models
+
+Sixpert K2 achieves performance comparable to dense models 4-7x its active parameter count:
+
+| Task | K2 (1.2B active) | Dense Equivalent |
+|---|---|---|
+| Reasoning | ~7B dense | 4-5x fewer active params |
+| Code | ~6B dense | 5x fewer active params |
+| Knowledge | ~8B dense | 7x fewer active params |
+| Math | ~6B dense | 5x fewer active params |