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Model: prithivMLmods/QvQ-Step-Tiny Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- Qwen/Qwen2-VL-2B-Instruct
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pipeline_tag: image-text-to-text
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library_name: transformers
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tags:
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- QvQ
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- Qwen
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- Contexr-Explainer
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---
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# **QvQ Step Tiny - [2B]**
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*QvQ-Step-Tiny* is a step-by-step context explainer Vision-Language model based on the Qwen2-VL architecture, fine-tuned using the VCR datasets for systematic step-by-step explanations. It is built on the Qwen2VLForConditionalGeneration framework with 2.21 billion parameters and uses BF16 (Brain Floating Point 16) precision.
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# **Quickstart with Transformers**
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Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
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```python
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor
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from qwen_vl_utils import process_vision_info
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# default: Load the model on the available device(s)
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model = Qwen2VLForConditionalGeneration.from_pretrained(
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"prithivMLmods/QvQ-Step-Tiny", torch_dtype="auto", device_map="auto"
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)
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messages = [
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{
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"role": "user",
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"content": [
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{
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"type": "image",
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"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
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},
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{"type": "text", "text": "Describe this image."},
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],
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}
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]
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text = processor.apply_chat_template(
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messages, tokenize=False, add_generation_prompt=True
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)
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image_inputs, video_inputs = process_vision_info(messages)
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inputs = processor(
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text=[text],
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images=image_inputs,
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videos=video_inputs,
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padding=True,
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return_tensors="pt",
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)
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inputs = inputs.to("cuda")
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# Inference: Generation of the output
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generated_ids = model.generate(**inputs, max_new_tokens=128)
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generated_ids_trimmed = [
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out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
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]
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output_text = processor.batch_decode(
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
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)
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print(output_text)
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```
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# **Key Enhancements of QvQ-Step-Tiny**
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1. **State-of-the-Art Visual Understanding**
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- QvQ-Step-Tiny inherits the state-of-the-art capabilities of Qwen2-VL for understanding images of various resolutions and aspect ratios.
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- It excels on visual reasoning benchmarks such as **MathVista**, **DocVQA**, **RealWorldQA**, and **MTVQA**, making it a powerful tool for detailed visual content analysis and question answering.
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2. **Extended Video Understanding**
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- With the ability to process and comprehend videos of over 20 minutes, QvQ-Step-Tiny supports high-quality video-based question answering, conversational dialogs, and video content generation.
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- It ensures a systematic, step-by-step explanation of video content, which is ideal for educational, entertainment, and professional applications.
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3. **Integration with Devices and Systems**
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- Thanks to its advanced reasoning and decision-making capabilities, QvQ-Step-Tiny can act as an intelligent agent for operating devices such as mobile phones, robots, and other automated systems.
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- It can process visual environments alongside textual instructions to enable seamless automation and intelligent control of devices.
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4. **Multilingual Support for Text in Images**
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- QvQ-Step-Tiny supports multilingual text recognition within images, handling English, Chinese, and a wide range of languages, including most European languages, Japanese, Korean, Arabic, and Vietnamese.
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- This makes it an effective model for global applications, from document analysis to multi-language accessibility solutions.
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# **Intended Use**
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1. **Step-by-Step Context Explanation**: Designed to provide detailed and systematic explanations for images and videos, making it ideal for educational, analytical, and instructional tasks.
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2. **Visual Content Understanding**: Effective for analyzing visual content across diverse resolutions, aspect ratios, and formats, including documents (DocVQA) and mathematical visuals (MathVista).
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3. **Video-based Reasoning**: Supports comprehension of long-form videos (20+ minutes) for tasks like video question answering, dialog generation, and instructional content creation.
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4. **Device Integration**: Can act as an intelligent agent to automate device operations (e.g., mobile phones, robots) by understanding visual environments and processing text-based instructions.
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5. **Multilingual Visual Text Support**: Recognizes and processes multilingual text within images, making it suitable for global applications like document processing and accessibility tools.
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6. **Advanced Question Answering**: Excels in question-answering tasks involving images, videos, and multimodal data, serving as a robust tool for interactive systems.
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7. **Accessibility Enhancements**: Assists visually impaired users by explaining visual and textual content in a clear, step-by-step manner.
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# **Limitations**
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1. **Model Size Constraints**: At 2.21 billion parameters, it may not perform as well as larger models for highly complex or nuanced tasks.
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2. **Accuracy with Low-Quality Inputs**: Performance may degrade when dealing with low-resolution images, poor lighting conditions, or noisy video/audio inputs.
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3. **Specialized Training Gaps**: While strong on general benchmarks, it might struggle with niche or highly specialized domains that require additional fine-tuning.
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4. **Multilingual Text Variability**: While multilingual text recognition is supported, performance may vary across less common or highly complex languages.
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5. **Context Length Tradeoffs**: Processing very long videos (e.g., over 20 minutes) or highly dense visual data might challenge its coherence or explanation accuracy.
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6. **Device Integration Complexity**: Deploying the model for operating devices or robots may require significant engineering efforts and robust integration pipelines.
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7. **Resource-Intensive for Long Contexts**: Despite BF16 precision, tasks with extended context lengths or high-resolution inputs could demand substantial computational resources.
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8. **Ambiguity in Prompts**: Ambiguously phrased or poorly structured input prompts may lead to incomplete or inaccurate explanations.
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9. **Static Model**: The model cannot learn dynamically from user interactions or adapt its behavior without retraining.
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# **Applications**
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- **Education**: Step-by-step explanations for visual and textual content in learning materials, including images and videos.
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- **Automation**: Integrating with robotics or smart devices for performing tasks based on visual and textual data.
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- **Content Creation**: Assisting in creating or analyzing video and image-based content, such as tutorials or product demos.
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- **Accessibility**: Enhancing accessibility tools for visually impaired or multilingual users by providing clear explanations of image or video content.
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- **Global Q&A Systems**: Supporting cross-lingual question answering in images and videos for diverse user bases.
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chat_template.json
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chat_template.json
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{
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"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}"
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}
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config.json
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config.json
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{
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"architectures": [
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"Qwen2VLForConditionalGeneration"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2_vl",
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configuration.json
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merges.txt
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model.safetensors
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preprocessor_config.json
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"do_convert_rgb": true,
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|
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3
tokenizer.json
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tokenizer.json
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||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{% set image_count = namespace(value=0) %}{% set video_count = namespace(value=0) %}{% for message in messages %}{% if loop.first and message['role'] != 'system' %}<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n{% endif %}<|im_start|>{{ message['role'] }}\n{% if message['content'] is string %}{{ message['content'] }}<|im_end|>\n{% else %}{% for content in message['content'] %}{% if content['type'] == 'image' or 'image' in content or 'image_url' in content %}{% set image_count.value = image_count.value + 1 %}{% if add_vision_id %}Picture {{ image_count.value }}: {% endif %}<|vision_start|><|image_pad|><|vision_end|>{% elif content['type'] == 'video' or 'video' in content %}{% set video_count.value = video_count.value + 1 %}{% if add_vision_id %}Video {{ video_count.value }}: {% endif %}<|vision_start|><|video_pad|><|vision_end|>{% elif 'text' in content %}{{ content['text'] }}{% endif %}{% endfor %}<|im_end|>\n{% endif %}{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n{% endif %}",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 32768,
|
||||
"pad_token": "<|vision_pad|>",
|
||||
"padding_side": "right",
|
||||
"processor_class": "Qwen2VLProcessor",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user