The Qwen2-VL-Math-Prase-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.

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.

File Name	Size	Description	Upload Status
`.gitattributes`	1.52 kB	Configures LFS tracking for specific model files.	Initial commit
`README.md`	203 Bytes	Minimal details about the uploaded model.	Updated
`added_tokens.json`	408 Bytes	Additional tokens used by the model tokenizer.	Uploaded
`chat_template.json`	1.05 kB	Template for chat-based model input/output.	Uploaded
`config.json`	1.24 kB	Model configuration metadata.	Uploaded
`generation_config.json`	252 Bytes	Configuration for text generation settings.	Uploaded
`merges.txt`	1.82 MB	BPE merge rules for tokenization.	Uploaded
`model.safetensors`	4.42 GB	Serialized model weights in a secure format.	Uploaded (LFS)
`preprocessor_config.json`	596 Bytes	Preprocessing configuration for input data.	Uploaded
`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(
    "prithivMLmods/Qwen2-VL-Math-Prase-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-Math-Prase-2B-Instruct",
#     torch_dtype=torch.bfloat16,
#     attn_implementation="flash_attention_2",
#     device_map="auto",
# )

# default processer
processor = AutoProcessor.from_pretrained("prithivMLmods/Qwen2-VL-Math-Prase-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

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 high accuracy.
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.
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.