README.md

---
license: apache-2.0
datasets:
- linxy/LaTeX_OCR
- prithivMLmods/Img2Text-Plaintext-Retrieval
- prithivMLmods/Img2Text-Algorithm-Retrieval
- unsloth/LaTeX_OCR
- mychen76/invoices-and-receipts_ocr_v1
language:
- en
base_model:
- Qwen/Qwen2-VL-2B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- OCR
- KIE
- Key Information Extraction
- Messy Handwriting Recognition
- text-generation-inference
- VLM
- Callisto
- OCR#3
- RAG
- 2B
---

# **Callisto-OCR3-2B-Instruct [ VL / OCR ]**

![Callisto.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/8S9-RGoCAfSqzMHvuAWm-.png)

>  [!Note]
>  The **Callisto-OCR3-2B-Instruct** model is a fine-tuned version of *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.

[![Open Demo in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://huggingface.co/prithivMLmods/Callisto-OCR3-2B-Instruct/blob/main/Callisto-OCR3-2B-Instruct-Demo/Callisto_OCR3_2B_Instruct.ipynb)


#### 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.