Update README.md

README.md

@@ -18,4 +18,114 @@ tags:
 - text-generation-inference
 - VLM
 - Callisto
----
+---
+# **Callisto-OCR3-2B-Instruct**
+
+The **Callisto-OCR3-2B-Instruct** model is a fine-tuned version of **Qwen/Qwen2-VL-2B-Instruct**, specifically optimized for **messy handwriting recognition**, **Optical Character Recognition (OCR)**, **English language understanding**, and **math problem solving with LaTeX formatting**. This model integrates a conversational approach with visual and textual understanding to handle multi-modal tasks effectively.
+
+#### Key Enhancements:
+
+* **SoTA understanding of images of various resolution & ratio**: Callisto-OCR3 achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.
+
+* **Enhanced Handwriting OCR**: Optimized for recognizing and interpreting **messy handwriting** with high accuracy, making it ideal for digitizing handwritten documents and notes.
+
+* **Understanding videos of 20min+**: Callisto-OCR3 can process long videos, enabling high-quality video-based question answering, transcription, and content generation.
+
+* **Agent that can operate your mobiles, robots, etc.**: With advanced reasoning and decision-making, Callisto-OCR3 can be integrated with mobile phones, robots, and other devices to perform automated tasks based on visual and textual input.
+
+* **Multilingual Support**: Besides English and Chinese, Callisto-OCR3 supports text recognition inside images in multiple languages, including European languages, Japanese, Korean, Arabic, and Vietnamese.
+
+### How to Use
+
+```python
+from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
+from qwen_vl_utils import process_vision_info
+
+# Load the model on the available device(s)
+model = Qwen2VLForConditionalGeneration.from_pretrained(
+    "prithivMLmods/Callisto-OCR3-2B-Instruct", torch_dtype="auto", device_map="auto"
+)
+
+# Enable flash_attention_2 for better acceleration and memory optimization
+# model = Qwen2VLForConditionalGeneration.from_pretrained(
+#     "prithivMLmods/Callisto-OCR3-2B-Instruct",
+#     torch_dtype=torch.bfloat16,
+#     attn_implementation="flash_attention_2",
+#     device_map="auto",
+# )
+
+# Default processor
+processor = AutoProcessor.from_pretrained("prithivMLmods/Callisto-OCR3-2B-Instruct")
+
+# Customize visual token range for speed-memory balance
+# 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": "Recognize the handwriting in 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: Generate 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)
+```
+
+### Buffering Output
+```python
+buffer = ""
+for new_text in streamer:
+    buffer += new_text
+    # Remove <|im_end|> or similar tokens from the output
+    buffer = buffer.replace("<|im_end|>", "")
+    yield buffer
+```
+
+### **Key Features**
+
+1. **Advanced Handwriting OCR:**  
+   - Excels at recognizing and transcribing **messy and cursive handwriting** into digital text with high accuracy.
+
+2. **Vision-Language Integration:**  
+   - Combines **image understanding** with **natural language processing** to convert images into text.  
+
+3. **Optical Character Recognition (OCR):**  
+   - Extracts and processes textual information from images with precision.
+
+4. **Math and LaTeX Support:**  
+   - Solves math problems and outputs equations in **LaTeX format**.
+
+5. **Conversational Capabilities:**  
+   - Designed to handle **multi-turn interactions**, providing context-aware responses.
+
+6. **Image-Text-to-Text Generation:**  
+   - Inputs can include **images, text, or a combination**, and the model generates descriptive or problem-solving text.