Add model card for WSVD (#1)

- Add model card for WSVD (f5166f6b639ad31403d82fd8761ca9d53b7781fe)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

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+---
+pipeline_tag: image-text-to-text
+---
+
+# WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models
+
+WSVD is a method for efficient low-rank approximation designed to enable fast and efficient execution of Low-Precision Vision-Language Models (VLMs). By applying SVD at a finer granularity (per-head) and using element-wise importance to guide fine-tuning, WSVD achieves significant decoding speedups while maintaining high accuracy.
+
+## Resources
+
+- **Paper:** [WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models](https://huggingface.co/papers/2604.02570)
+- **GitHub Repository:** [SAI-Lab-NYU/WSVD](https://github.com/SAI-Lab-NYU/WSVD)
+- **Project Page:** [OpenReview](https://openreview.net/forum?id=zrmQ4koOw9)
+
+## 🌟 Highlights
+
+- **🧩 Per-head SVD to actually speed up decoding:** WSVD applies SVD per attention head to avoid the “shared-latent reloading” overhead that can make conventional SVD slower at decode time, cutting KV-cache memory traffic and reconstruction cost.
+- **🎯 Accuracy-preserving compression: Fisher-weighted local FT + local QAT:** WSVD uses element-wise importance to guide local fine-tuning of low-rank factors, then adds quantization-aware training with outlier handling—yielding a low-precision low-rank VLM with minimal accuracy drop.
+- **📊 System-level Triton fusion with Flash Decoding for real latency wins:** WSVD integrates low-rank reconstruction directly into the flash-decoding fused kernel, translating rank reduction into practical speedups (over 1.8× decoding speedup vs. Flash Decoding).
+
+## Supported Models
+
+The implementation currently supports **LLaVA-v1.5** and **LLaVA-Next** models. Pre-computed calibration cache files for LLaVA-1.5 (7B, 13B) and LLaVA-Next (7B, 13B) are available in the official repository to facilitate the reproduction of results.
+
+## Citation
+
+```bibtex
+@inproceedings{
+  wsvd2026iclr,
+  title={WSVD: Weighted Low-Rank Approximation for Fast and Efficient Execution of Low-Precision Vision-Language Models},
+  author={...},
+  booktitle={International Conference on Learning Representations (ICLR)},
+  year={2026}
+}
+```