Add LiteRT converted deit_small_patch16_224

README.md

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+---
+library_name: litert
+base_model: timm/deit_small_patch16_224.fb_in1k
+tags:
+  - vision
+  - image-classification
+datasets:
+  - imagenet-1k
+---
+# deit_small_patch16_224
+
+Converted TIMM image classification model for LiteRT.
+
+- Source architecture: `deit_small_patch16_224`
+- Source checkpoint: `timm/deit_small_patch16_224.fb_in1k`
+- File: `model.tflite`
+- Input: `float32` tensor in NCHW layout, shape `[1, 3, 224, 224]`
+- Output: ImageNet-1K logits, shape `[1, 1000]`
+
+## Runtime Status
+
+- CPU smoke test: passed with LiteRT `CompiledModel`.
+- GPU delegation: currently blocked for this model by rank-5 tensor patterns in the GPU backend, mostly `RESHAPE`, `TRANSPOSE`, and related window/attention operations. The model is published as CPU-ready while GPU support is being improved.
+
+## Model Details
+
+- **Model Type:** Image classification / feature backbone
+- **Model Stats:**
+  - Params (M): 22.1
+  - GMACs: 4.6
+  - Activations (M): 11.9
+  - Image size: 224 x 224
+- **Papers:**
+  - Training data-efficient image transformers & distillation through attention: https://arxiv.org/abs/2012.12877
+- **Original:** https://github.com/facebookresearch/deit
+- **Dataset:** ImageNet-1k
+
+## Citation
+
+```bibtex
+@InProceedings{pmlr-v139-touvron21a,
+  title =     {Training data-efficient image transformers & distillation through attention},
+  author =    {Touvron, Hugo and Cord, Matthieu and Douze, Matthijs and Massa, Francisco and Sablayrolles, Alexandre and Jegou, Herve},
+  booktitle = {International Conference on Machine Learning},
+  pages =     {10347--10357},
+  year =      {2021},
+  volume =    {139},
+  month =     {July}
+}
+```
+```bibtex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
+}
+```
+

model.tflite

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