Update README with usage instructions

README.md

@@ -28,9 +28,81 @@ Compared to multi-vector (ColBERT-like) architectures, eager-embed-v1 offers a s
 
 ## How to Get Started with the Model
 
-**TODO: Coming soon**. Check [here for now](https://github.com/eagerworks/eager-embed/blob/main/inference.py) for now.
+Load the model and define encode helper function
 ```python
-TODO: Add inference code
+import torch
+from transformers import AutoProcessor, Qwen3VLForConditionalGeneration
+from transformers.utils.import_utils import is_flash_attn_2_available
+from qwen_vl_utils import process_vision_info
+
+MODEL_NAME = "eagerworks/eager-embed-v1"
+DEVICE = torch.device("cpu")
+if torch.cuda.is_available():
+    DEVICE = torch.device("cuda:0")
+elif torch.backends.mps.is_available():
+    DEVICE = torch.device("mps")
+DTYPE = torch.bfloat16
+
+processor = AutoProcessor.from_pretrained(MODEL_NAME)
+model = Qwen3VLForConditionalGeneration.from_pretrained(
+    MODEL_NAME,
+    attn_implementation=(
+        "flash_attention_2" if is_flash_attn_2_available() else None
+    ),
+    dtype=DTYPE
+).to(DEVICE).eval()
+
+# Function to Encode Message
+def encode_message(message):
+    with torch.no_grad():
+        texts = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True) + "<|endoftext|>"
+        image_inputs, video_inputs = process_vision_info(message)
+
+        inputs = processor(
+            text=texts,
+            images=image_inputs,
+            videos=video_inputs,
+            return_tensors="pt",
+            padding="longest",
+        ).to(DEVICE)
+
+        model_outputs = model(**inputs, return_dict=True, output_hidden_states=True)
+
+        last_hidden_state = model_outputs.hidden_states[-1]
+        embeddings = last_hidden_state[:, -1]
+        embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=-1)
+        return embeddings
+```
+
+🌍 Multilingual Text Retrieval
+```python
+example_query = "Query: What is the capital city of Uruguay?"
+example_text_1 = "Montevideo es la capital y la ciudad más poblada de la República Oriental del Uruguay, así como la capital del departamento homónimo"
+example_text_2 = "El río Uruguay es un río internacional que forma parte de la cuenca del Plata. Nace en Brasil, recorre unos 1.800 km y desemboca en el Río de la Plata"
+query = [{'role': 'user', 'content': [{'type': 'text', 'text': example_query}]}]
+text_1 = [{'role': 'user', 'content': [{'type': 'text', 'text': example_text_1}]}]
+text_2 = [{'role': 'user', 'content': [{'type': 'text', 'text': example_text_2}]}]
+
+sim1 = torch.cosine_similarity(encode_message(query), encode_message(text_1))
+sim2 = torch.cosine_similarity(encode_message(query), encode_message(text_2))
+
+print("Similarities:", sim1.item(), sim2.item())
+```
+
+📈 Image Document Retrieval (Image, Chart, PDF)
+```python
+MAX_IMAGE_SIZE = 784
+example_query = 'Query: Where can we find the animal llama?'
+example_image_1 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/animal-llama.png"
+example_image_2 = "https://huggingface.co/Tevatron/dse-phi3-docmatix-v2/resolve/main/meta-llama.png"
+query = [{'role': 'user', 'content': [{'type': 'text', 'text': example_query}]}]
+image_1 = [{'role': 'user', 'content': [{'type': 'image', 'image': example_image_1, 'resized_height': MAX_IMAGE_SIZE, 'resized_width': MAX_IMAGE_SIZE}]}]
+image_2 = [{'role': 'user', 'content': [{'type': 'image', 'image': example_image_2, 'resized_height': MAX_IMAGE_SIZE, 'resized_width': MAX_IMAGE_SIZE}]}]
+
+sim1 = torch.cosine_similarity(encode_message(query), encode_message(image_1))
+sim2 = torch.cosine_similarity(encode_message(query), encode_message(image_2))
+
+print("Similarities:", sim1.item(), sim2.item())
 ```
 
 ## Training Details