Upload MODEL_CARD.md

MODEL_CARD.md

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+---
+license: apache-2.0
+library_name: q-tensorformer
+tags:
+  - tensor-networks
+  - quantum-machine-learning
+  - model-compression
+  - transformer
+  - efficient-deep-learning
+  - nisq
+  - pennylane
+pipeline_tag: text-generation
+---
+
+# Q-TensorFormer v3 — Model Card
+
+## Model Details
+
+**Q-TensorFormer** is a hybrid transformer that compresses feed-forward layers using **Tensor-Train (TT) decomposition** and enhances token representations via **PennyLane quantum circuits**, with **adaptive TT-rank scheduling** guided by attention entropy.
+
+- **Architecture**: Quantum-Enhanced Tensor Network Transformer
+- **Parameters**: Configurable (50K–50M range)
+- **Compression ratio**: 1.5–3× vs. equivalent dense transformer
+- **Quantum overhead**: <30% of tokens routed through quantum (adjustable sparsity)
+
+## Core Mechanism
+
+```
+Attention entropy S(ρ) → norm → RankScheduler → TT-rank r(layer)
+```
+
+The attention entropy (a classical proxy for quantum entanglement) measures input complexity per token. Higher entropy → more complex patterns → higher tensor rank. Lower entropy → more compressible → aggressive TT rank reduction.
+
+**Budget-constrained mode**: Set `max_params`, `max_latency_ms`, or `max_energy_per_query` and the model auto-adjusts ranks to stay within budget.
+
+## Intended Uses
+
+| Use Case | Model Size | Expected Metric |
+|----------|-----------|----------------|
+| Edge NLP (mobile, on-device) | <5M params | PPL within 5% of dense baseline |
+| 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 |
+
+## Limitations
+
+- **NISQ-era only**: Quantum circuits are simulated (PennyLane `default.qubit`). Real quantum hardware not required.
+- **Small to medium models**: Designed for embedding dimensions ≤512. Not for GPT-scale (100M+) models.
+- **Training data**: Optimized for WikiText-2 and similar text corpora.
+- **Quantum advantage**: We claim efficiency (fewer params for same performance), not "quantum advantage" in the broad sense.
+
+## Citation
+
+```bibtex
+@software{q_tensorformer2026,
+  author = {Premchan369},
+  title = {Q-TensorFormer: Quantum-Enhanced Tensor Network LLM Compression Engine},
+  url = {https://huggingface.co/Premchan369/q-tensorformer},
+  version = {3.0.0},
+  year = {2026},
+}
+```
+
+## References
+
+- 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)