Qwen2-VL-OCR-2B-Instruct [ VL / OCR ]

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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.

👁 Open Demo in Colab

Key Enhancements:

• SoTA understanding of images of various resolution & ratio: Qwen2-VL achieves state-of-the-art performance on visual understanding benchmarks, including MathVista, DocVQA, RealWorldQA, MTVQA, etc.

• Understanding videos of 20min+: Qwen2-VL can understand videos over 20 minutes for high-quality video-based question answering, dialog, content creation, etc.

• Agent that can operate your mobiles, robots, etc.: with the abilities of complex reasoning and decision making, Qwen2-VL can be integrated with devices like mobile phones, robots, etc., for automatic operation based on visual environment and text instructions.

• Multilingual Support: to serve global users, besides English and Chinese, Qwen2-VL now supports the understanding of texts in different languages inside images, including most European languages, Japanese, Korean, Arabic, Vietnamese, etc.

Sample Inference

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How to Use

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(
 "prithivMLmods/Qwen2-VL-OCR-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(
# "prithivMLmods/Qwen2-VL-OCR-2B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )

# default processer
processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-OCR-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)

Buf

 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. 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.

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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.

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Base model

Qwen/Qwen2-VL-2B

Qwen/Qwen2-VL-2B-Instruct

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