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

👁 Callisto.png

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

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

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

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

Advanced Handwriting OCR:
- Excels at recognizing and transcribing messy and cursive handwriting into digital text with high accuracy.
Vision-Language Integration:
- Combines image understanding with natural language processing to convert images into text.
Optical Character Recognition (OCR):
- Extracts and processes textual information from images with precision.
Math and LaTeX Support:
- Solves math problems and outputs equations in LaTeX format.
Conversational Capabilities:
- Designed to handle multi-turn interactions, providing context-aware responses.
Image-Text-to-Text Generation:
- Inputs can include images, text, or a combination, and the model generates descriptive or problem-solving text.