fix readme.md

README.md

@@ -1,26 +1,91 @@
 ---
 pipeline_tag: zero-shot-image-classification
 license: mit
+base_model: openai/clip-vit-base-patch32
 library_name: kerasformers
 tags:
 - keras
 - kerasformers
 - clip
-- jax
+- zero-shot-image-classification
+- vision
+- arxiv:2103.00020
 - pytorch
+- jax
 - tf
 ---
 
-# clip_vit_base_32 (Keras 3)
+## ***See [our collection](https://huggingface.co/collections/kerasformers/clip-6a6a9c7bfdc6c38dcb984c24) for all versions of CLIP.***
 
-Pure-Keras 3 weights for [kerasformers](https://github.com/IMvision12/KerasFormers), mirrored from the GitHub release. License: `mit`.
+# Run CLIP with Keras 3: JAX, PyTorch, or TensorFlow
 
-```python
+[![GitHub](https://img.shields.io/badge/GitHub-KerasFormers-black?logo=github)](https://github.com/IMvision12/KerasFormers) [![Docs](https://img.shields.io/badge/Docs-CLIP-blue)](https://imvision12.github.io/KerasFormers/clip/) [![Collection](https://img.shields.io/badge/HF-CLIP%20collection-yellow)](https://huggingface.co/collections/kerasformers/clip-6a6a9c7bfdc6c38dcb984c24)
+
+# kerasformers/clip_vit_base_32
+
+Paper: [Learning Transferable Visual Models From Natural Language Supervision (arXiv:2103.00020)](https://arxiv.org/abs/2103.00020) · [HF Papers](https://huggingface.co/papers/2103.00020)
+
+CLIP (Contrastive Language-Image Pre-training) is a vision + text dual-encoder trained on (image, caption) pairs with a contrastive loss. Both encoders project to a shared embedding space for zero-shot classification, retrieval, and embeddings.
+
+For more details on the model, please go to the upstream [model card](https://huggingface.co/openai/clip-vit-base-patch32).
+
+Pure-**Keras 3** conversion of [`openai/clip-vit-base-patch32`](https://huggingface.co/openai/clip-vit-base-patch32) for [kerasformers](https://github.com/IMvision12/KerasFormers). One implementation runs unmodified on **TensorFlow / Torch / JAX**.
+
+This is a **zero-shot image-text** checkpoint (`CLIPZeroShotClassify`): pass image(s) and text prompts at inference time.
 
-from kerasformers.models.clip import CLIPModel, CLIPTokenizer
+## ✨ Quick start
 
-model = CLIPModel.from_weights("clip_vit_base_32")
+```python
+import os
+os.environ["KERAS_BACKEND"] = "torch"  # or "jax" / "tensorflow"
+
+from kerasformers.models.clip import (
+    CLIPProcessor,
+    CLIPZeroShotClassify,
+)
 
-tokenizer = CLIPTokenizer.from_weights("clip_vit_base_32")
+processor = CLIPProcessor.from_weights("kerasformers/clip_vit_base_32")
+model = CLIPZeroShotClassify.from_weights("kerasformers/clip_vit_base_32")
 
+labels = [
+    "a photo of a cat",
+    "a photo of a dog",
+    "a photo of a car",
+    "a photo of a living room",
+]
+inputs = processor(text=labels, image_paths="your_image.jpg")
+output = model(
+    {
+        "images": inputs["images"],
+        "token_ids": inputs["input_ids"],
+        "padding_mask": inputs["attention_mask"],
+    }
+)
+print(output["image_logits"].shape)
 ```
+
+Load any CLIP variant the same way with `from_weights("kerasformers/<variant>")`:
+
+| Variant | Hub | Notes |
+|---|---|---|
+| `clip_vit_base_16` | [`kerasformers/clip_vit_base_16`](https://huggingface.co/kerasformers/clip_vit_base_16) | OpenAI |
+| `clip_vit_base_32` | [`kerasformers/clip_vit_base_32`](https://huggingface.co/kerasformers/clip_vit_base_32) | OpenAI |
+| `clip_vit_large_14` | [`kerasformers/clip_vit_large_14`](https://huggingface.co/kerasformers/clip_vit_large_14) | OpenAI |
+| `clip_vit_large_14_336` | [`kerasformers/clip_vit_large_14_336`](https://huggingface.co/kerasformers/clip_vit_large_14_336) | OpenAI |
+| `clip_vit_g_14` | [`kerasformers/clip_vit_g_14`](https://huggingface.co/kerasformers/clip_vit_g_14) | LAION |
+| `clip_vit_bigg_14` | [`kerasformers/clip_vit_bigg_14`](https://huggingface.co/kerasformers/clip_vit_bigg_14) | LAION |
+
+## Tips
+
+- Set `KERAS_BACKEND` **before** importing Keras / kerasformers.
+- Prefer `Processor.from_weights(...)` so image size and tokenizer match the variant.
+- Map processor `input_ids` / `attention_mask` to model `token_ids` / `padding_mask`.
+- OpenAI variants use `quick_gelu`; LAION g/G use `gelu`.
+- See [CLIP docs](https://imvision12.github.io/KerasFormers/clip/) and [Loading Weights](https://imvision12.github.io/KerasFormers/loading_weights/).
+- Community / upstream safetensors still work via the `hf:` prefix, e.g. `CLIPZeroShotClassify.from_weights("hf:openai/clip-vit-base-patch32")`.
+
+## Special Thanks
+
+A huge thank you to the OpenAI CLIP and LAION authors for creating and releasing these models.
+
+License: MIT.