Update README.md

README.md

@@ -7,4 +7,74 @@ tags:
 - text2text-generation
 - image-text-to-text
 library_name: transformers.js
----
+---
+
+https://huggingface.co/microsoft/Florence-2-large with ONNX weights to be compatible with Transformers.js.
+
+## Usage (Transformers.js)
+
+> [!IMPORTANT]
+> NOTE: Florence-2 support is experimental and requires you to install Transformers.js [v3](https://github.com/xenova/transformers.js/tree/v3) from source.
+
+If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [GitHub](https://github.com/xenova/transformers.js/tree/v3) using:
+```bash
+npm install xenova/transformers.js#v3
+```
+
+**Example:** Perform image captioning with `onnx-community/Florence-2-large`.
+```js
+import {
+    Florence2ForConditionalGeneration,
+    AutoProcessor,
+    AutoTokenizer,
+    RawImage,
+} from '@xenova/transformers';
+
+// Load model, processor, and tokenizer
+const model_id = 'onnx-community/Florence-2-large';
+const model = await Florence2ForConditionalGeneration.from_pretrained(model_id, {
+    dtype: {
+        embed_tokens: 'fp16', // or 'fp32'
+        vision_encoder: 'fp16', // or 'fp32'
+        encoder_model: 'q4',
+        decoder_model_merged: 'q4',
+    },
+});
+const processor = await AutoProcessor.from_pretrained(model_id);
+const tokenizer = await AutoTokenizer.from_pretrained(model_id);
+
+// Load image and prepare vision inputs
+const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg';
+const image = await RawImage.fromURL(url);
+const vision_inputs = await processor(image);
+
+// Specify task and prepare text inputs
+const task = '<MORE_DETAILED_CAPTION>';
+const prompts = processor.construct_prompts(task);
+const text_inputs = tokenizer(prompts);
+
+// Generate text
+const generated_ids = await model.generate({
+    ...text_inputs,
+    ...vision_inputs,
+    max_new_tokens: 256,
+});
+
+// Decode generated text
+const generated_text = tokenizer.batch_decode(generated_ids, { skip_special_tokens: false })[0];
+
+// Post-process the generated text
+const result = processor.post_process_generation(generated_text, task, image.size);
+console.log(result);
+// { '<MORE_DETAILED_CAPTION>': 'The image shows a vintage Volkswagen Beetle car parked on a cobblestone street in front of a yellow building with two wooden doors. The car is a bright turquoise color and has a classic design with a round body and a sloping roofline. It has two doors on either side of the car, one on the left side and one in the center, with a brown door on the right side. The doors are made of wood and have a rustic, weathered look. The building behind the car is painted in a light yellow color and appears to be old and dilapidated. The sky is blue and there are trees in the background. The image is taken from a low angle, looking up at the car and the building.' }
+```
+
+We also released an online demo, which you can try yourself: https://huggingface.co/spaces/Xenova/florence2-webgpu
+
+
+<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/61b253b7ac5ecaae3d1efe0c/BJj3jQXNqS_7Nt2MSb2ss.mp4"></video>
+
+---
+
+Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
+