Update README.md

README.md

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 ---
 ### Qwen2-VL-OCR-2B-Instruct
 
+The **Qwen2-VL-OCR-2B-Instruct** model is a fine-tuned version of **Qwen/Qwen2-VL-2B-Instruct**, tailored for tasks that involve **Optical Character Recognition (OCR)**, **image-to-text conversion**, and **math problem solving with LaTeX formatting**. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
+
 | **File Name**             | **Size**   | **Description**                                 | **Upload Status** |
 |---------------------------|------------|------------------------------------------------|-------------------|
 | `.gitattributes`          | 1.52 kB   | Configures LFS tracking for specific model files. | Initial commit    |
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 | `vocab.json`              | 2.78 MB   | Vocabulary file for tokenization.               | Uploaded          |
 
 ---
+### How to Use
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# default: Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "Qwen/Qwen2-VL-2B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
+# model = Qwen2VLForConditionalGeneration.from_pretrained(
+#     "Qwen/Qwen2-VL-2B-Instruct",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# default processer
+processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
+
+# The default range for the number of visual tokens per image in the model is 4-16384. You can set min_pixels and max_pixels according to your needs, such as a token count range of 256-1280, to balance speed and memory usage.
+# min_pixels = 256*28*28
+# max_pixels = 1280*28*28
+# processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "image",
+                "image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
+            },
+            {"type": "text", "text": "Describe this image."},
+        ],
+    }
+]
+
+# Preparation for inference
+text = processor.apply_chat_template(
+    messages, tokenize=False, add_generation_prompt=True
+)
+image_inputs, video_inputs = process_vision_info(messages)
+inputs = processor(
+    text=[text],
+    images=image_inputs,
+    videos=video_inputs,
+    padding=True,
+    return_tensors="pt",
+)
+inputs = inputs.to("cuda")
+
+# Inference: Generation of the output
+generated_ids = model.generate(**inputs, max_new_tokens=128)
+generated_ids_trimmed = [
+    out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
+]
+output_text = processor.batch_decode(
+    generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
+)
+print(output_text)
+```
+
+### **Key Features**
+
+1. **Vision-Language Integration:**  
+   - Combines **image understanding** with **natural language processing** to convert images into text.  
+
+2. **Optical Character Recognition (OCR):**  
+   - Extracts and processes textual information from images with high accuracy.
+
+3. **Math and LaTeX Support:**  
+   - Solves math problems and outputs equations in **LaTeX format**.
+
+4. **Conversational Capabilities:**  
+   - Designed to handle **multi-turn interactions**, providing context-aware responses.
+
+5. **Image-Text-to-Text Generation:**  
+   - Inputs can include **images, text, or a combination**, and the model generates descriptive or problem-solving text.
+
+6. **Secure Weight Format:**  
+   - Uses **Safetensors** for faster and more secure model weight loading.
+
+---
+
+### **Training Details**
+
+- **Base Model:** [Qwen/Qwen2-VL-2B-Instruct](#)  
+- **Model Size:**  
+   - 2.21 Billion parameters  
+   - Optimized for **BF16** tensor type, enabling efficient inference.
+
+- **Specializations:**  
+   - OCR tasks in images containing text.
+   - Mathematical reasoning and LaTeX output for equations.
+
+---