Add usage example

README.md

@@ -34,6 +34,64 @@ This model expects as input a single document image, rendered such that the long
 The prompt must then contain the additional metadata from the document, and the easiest way to generate this
 is to use the methods provided by the [olmOCR toolkit](https://github.com/allenai/olmocr).
 
+A simple way to infer using transformers is as follows:
+
+```python
+import torch from transformers import AutoModelForImageTextToText, AutoProcessor 
+
+model_id = "allenai/olmOCR-7B-0725" 
+processor = AutoProcessor.from_pretrained(model_id)
+model = AutoModelForImageTextToText.from_pretrained(model_id, torch_dtype=torch.float16 ).to("cuda").eval()
+
+PROMPT = """
+Below is the image of one page of a PDF document , as well as some raw textual content that
+was previously extracted for it that includes position information for each image and
+block of text ( The origin [0 x0 ] of the coordinates is in the lower left corner of the
+image ).
+Just return the plain text representation of this document as if you were reading it
+naturally .
+Turn equations into a LaTeX representation , and tables into markdown format . Remove the
+headers and footers , but keep references and footnotes .
+Read any natural handwriting .
+This is likely one page out of several in the document , so be sure to preserve any sentences
+that come from the previous page , or continue onto the next page , exactly as they are .
+If there is no text at all that you think you should read , you can output null .
+Do not hallucinate .
+RAW_TEXT_START
+{ base_text }
+RAW_TEXT_END
+"""
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/smolvlm_table.png",
+            },
+            {"type": "text", "text": "OCR"},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+inputs = processor.apply_chat_template(
+    messages,
+    video_fps=1,
+    add_generation_prompt=True,
+    tokenize=True,
+    return_dict=True,
+    return_tensors="pt"
+).to(model.device)
+
+output_ids = model.generate(**inputs, max_new_tokens=1000)
+generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(inputs.input_ids, output_ids)]
+output_text = processor.batch_decode(generated_ids, skip_special_tokens=True, clean_up_tokenization_spaces=True)
+print(output_text)
+```
 
 ## License and use