Update MODEL_CARD: Add K2 Think v2 integration section, explainable AI features

MODEL_CARD.md

@@ -9,6 +9,8 @@ tags:
   - efficient-deep-learning
   - nisq
   - pennylane
+  - k2-think
+  - explainable-ai
 pipeline_tag: text-generation
 ---
 
@@ -22,6 +24,7 @@ pipeline_tag: text-generation
 - **Parameters**: Configurable (50K–50M range)
 - **Compression ratio**: 1.5–3× vs. equivalent dense transformer
 - **Quantum overhead**: <30% of tokens routed through quantum (adjustable sparsity)
+- **K2 Think v2 Integration**: Explainable AI for every compression and routing decision
 
 ## Core Mechanism
 
@@ -33,6 +36,19 @@ The attention entropy (a classical proxy for quantum entanglement) measures inpu
 
 **Budget-constrained mode**: Set `max_params`, `max_latency_ms`, or `max_energy_per_query` and the model auto-adjusts ranks to stay within budget.
 
+## K2 Think v2 Integration (Explainable AI)
+
+Q-TensorFormer integrates with **K2 Think v2** (MBZUAI-IFM/K2-Think-v2) to provide natural language explanations for every compression and routing decision:
+
+| Component | What K2 Think Explains |
+|-----------|----------------------|
+| **RankScheduler** | Why entropy X → rank Y ("Token 47 has high attention dispersion, needs more capacity") |
+| **QuantumRouter** | Why token went to quantum ("This embedding is near decision boundary, quantum feature map may help") |
+| **Budget Tracker** | How budget constraints affected model size ("Reduced rank to 4 to stay under 2M params") |
+| **Compression Report** | Full audit trail of per-layer, per-token compression choices |
+
+**Live Demo**: [AlphaForge x K2 Think V2](https://huggingface.co/spaces/Premchan369/alphaforge-k2think)
+
 ## Intended Uses
 
 | Use Case | Model Size | Expected Metric |
@@ -41,6 +57,7 @@ The attention entropy (a classical proxy for quantum entanglement) measures inpu
 | Enterprise model compression | 10–50M params | 2× param reduction at equal accuracy |
 | Multilingual low-resource | <10M params | Better representation per parameter |
 | Research: quantum-classical hybrid | Small | Demonstrate quantum value in NLP |
+| Financial NLP (with K2 Think) | Any | Explainable compression for regulated industries |
 
 ## Limitations
 
@@ -66,3 +83,9 @@ The attention entropy (a classical proxy for quantum entanglement) measures inpu
 - Tensor Networks: Cichocki et al., "Tensor Networks for Dimensionality Reduction and Large-scale Optimization" (arXiv:2007.02779)
 - Quantum Transformers: Quixer (arXiv:2406.04305), QKSAN (arXiv:2308.13422)
 - PennyLane: Bergholm et al., "PennyLane: Automatic differentiation of hybrid quantum-classical computations" (arXiv:1811.04968)
+- K2 Think v2: MBZUAI-IFM/K2-Think-v2, Build with K2 Think V2 Challenge
+
+## Related Projects
+
+- [AlphaForge x K2 Think V2](https://huggingface.co/spaces/Premchan369/alphaforge-k2think) — Live quant trading demo with K2 Think v2 reasoning
+- [AlphaForge Platform](https://huggingface.co/Premchan369/alphaforge-quant-system) — 25-module open-source quant system