Update README.md

Updated README with inference code.

README.md

@@ -7,4 +7,71 @@ base_model:
 tags:
 - ocr
 - vlm
----
+---
+
+Usage:
+
+```python
+model = AutoModelForVision2Seq.from_pretrained(
+    'sovitrath/receipt-ocr-full-ft',
+    device_map='auto',
+    torch_dtype=torch.bfloat16,
+    _attn_implementation='flash_attention_2' # Use `flash_attention_2` on Ampere GPUs and above and `eager` on older GPUs.
+    # _attn_implementation='eager', # Use `flash_attention_2` on Ampere GPUs and above and `eager` on older GPUs.
+)
+
+processor = AutoProcessor.from_pretrained('sovitrath/receipt-ocr-full-ft')
+
+test_image = Image.open('inference_data/image_1.jpeg').convert('RGB')
+
+def test(model, processor, image, max_new_tokens=1024, device='cuda'):
+    messages = [
+        {
+            'role': 'user',
+            'content': [
+                {'type': 'image'},
+                {'type': 'text', 'text': 'OCR this image accurately'}
+            ]
+        },
+    ]
+    
+    # Prepare the text input by applying the chat template
+    text_input = processor.apply_chat_template(
+        messages,  # Use the sample without the system message
+        add_generation_prompt=True
+    )
+
+    image_inputs = []
+    if image.mode != 'RGB':
+        image = image.convert('RGB')
+        
+    image_inputs.append([image])
+
+    # Prepare the inputs for the model
+    model_inputs = processor(
+        #text=[text_input],
+        text=text_input,
+        images=image_inputs,
+        return_tensors='pt',
+    ).to(device)  # Move inputs to the specified device
+
+    # Generate text with the model
+    generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens)
+
+    # Trim the generated ids to remove the input ids
+    trimmed_generated_ids = [
+        out_ids[len(in_ids):] for in_ids, out_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    # Decode the output text
+    output_text = processor.batch_decode(
+        trimmed_generated_ids, 
+        skip_special_tokens=True,
+        clean_up_tokenization_spaces=False
+    )
+
+    return output_text[0]  # Return the first decoded output text
+
+output = test(model, processor, test_image)
+print(output)
+```