Update model card

README.md

@@ -0,0 +1,71 @@
+---
+library_name: lucid
+license: apache-2.0
+tags:
+  - image-classification
+  - efficientformer
+  - lucid
+datasets:
+  - imagenet-1k
+pipeline_tag: image-classification
+model-index:
+  - name: efficientformer-l7
+    results:
+      - task: { type: image-classification }
+        dataset: { name: ImageNet-1k, type: imagenet-1k }
+        metrics:
+          - { type: acc@1, value: 83.3 }
+---
+
+# EfficientFormer-L7
+
+> Li et al., 2022 — *EfficientFormer: Vision Transformers at MobileNet Speed* (arXiv:2206.01191)
+
+[Lucid](https://github.com/ChanLumerico/lucid) port of `timm/efficientformer_l7.snap_dist_in1k`,
+converted to Lucid-native safetensors.
+
+## Available weights
+
+| Tag | acc@1 | acc@5 | Params | GFLOPs | Size | Source |
+|---|---|---|---|---|---|---|
+| `SNAP_DIST_IN1K` *(default)* | 83.3 | — | 82.2M | — | 314.09 MB | timm |
+
+## Usage
+
+```python
+import lucid.models as models
+from lucid.models.vision.resnet import EfficientformerL7Weights
+
+# default tag
+model = models.efficientformer_l7_cls(pretrained=True)
+
+# explicit tag (enum or string)
+model = models.efficientformer_l7_cls(weights=EfficientformerL7Weights.SNAP_DIST_IN1K)
+model = models.efficientformer_l7_cls(pretrained="SNAP_DIST_IN1K")
+
+# preprocessing travels with the weights
+weights = EfficientformerL7Weights.SNAP_DIST_IN1K
+preprocess = weights.transforms()
+logits = model(preprocess(image)[None]).logits
+```
+
+## Conversion
+
+Converted from `timm/efficientformer_l7.snap_dist_in1k` via
+`python -m tools.convert_weights efficientformer_l7 --tag SNAP_DIST_IN1K`.
+Key mapping + numerical parity verified against the source.
+
+## License
+
+`apache-2.0` — inherited from the original weights.
+
+## Citation
+
+```
+@article{li2022efficientformer,
+  title={EfficientFormer: Vision Transformers at MobileNet Speed},
+  author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
+  journal={Advances in Neural Information Processing Systems},
+  volume={35}, year={2022}
+}
+```