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Model: prithivMLmods/QvQ-Step-Tiny
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2-VL-2B-Instruct
+pipeline_tag: image-text-to-text
+library_name: transformers
+tags:
+- QvQ
+- Qwen
+- Contexr-Explainer
+---
+# **QvQ Step Tiny - [2B]**
+
+*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.
+
+# **Quickstart with Transformers**
+
+Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
+
+```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/QvQ-Step-Tiny", torch_dtype="auto", device_map="auto"
+)
+
+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."},
+        ],
+    }
+]
+
+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 Enhancements of QvQ-Step-Tiny**  
+
+1. **State-of-the-Art Visual Understanding**  
+   - QvQ-Step-Tiny inherits the state-of-the-art capabilities of Qwen2-VL for understanding images of various resolutions and aspect ratios.  
+   - 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.  
+
+2. **Extended Video Understanding**  
+   - 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.  
+   - It ensures a systematic, step-by-step explanation of video content, which is ideal for educational, entertainment, and professional applications.  
+
+3. **Integration with Devices and Systems**  
+   - 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.  
+   - It can process visual environments alongside textual instructions to enable seamless automation and intelligent control of devices.  
+
+4. **Multilingual Support for Text in Images**  
+   - 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.  
+   - This makes it an effective model for global applications, from document analysis to multi-language accessibility solutions.  
+
+# **Intended Use**  
+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.  
+2. **Visual Content Understanding**: Effective for analyzing visual content across diverse resolutions, aspect ratios, and formats, including documents (DocVQA) and mathematical visuals (MathVista).  
+3. **Video-based Reasoning**: Supports comprehension of long-form videos (20+ minutes) for tasks like video question answering, dialog generation, and instructional content creation.  
+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.  
+5. **Multilingual Visual Text Support**: Recognizes and processes multilingual text within images, making it suitable for global applications like document processing and accessibility tools.  
+6. **Advanced Question Answering**: Excels in question-answering tasks involving images, videos, and multimodal data, serving as a robust tool for interactive systems.  
+7. **Accessibility Enhancements**: Assists visually impaired users by explaining visual and textual content in a clear, step-by-step manner.  
+
+# **Limitations**  
+1. **Model Size Constraints**: At 2.21 billion parameters, it may not perform as well as larger models for highly complex or nuanced tasks.  
+2. **Accuracy with Low-Quality Inputs**: Performance may degrade when dealing with low-resolution images, poor lighting conditions, or noisy video/audio inputs.  
+3. **Specialized Training Gaps**: While strong on general benchmarks, it might struggle with niche or highly specialized domains that require additional fine-tuning.  
+4. **Multilingual Text Variability**: While multilingual text recognition is supported, performance may vary across less common or highly complex languages.  
+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.  
+6. **Device Integration Complexity**: Deploying the model for operating devices or robots may require significant engineering efforts and robust integration pipelines.  
+7. **Resource-Intensive for Long Contexts**: Despite BF16 precision, tasks with extended context lengths or high-resolution inputs could demand substantial computational resources.  
+8. **Ambiguity in Prompts**: Ambiguously phrased or poorly structured input prompts may lead to incomplete or inaccurate explanations.  
+9. **Static Model**: The model cannot learn dynamically from user interactions or adapt its behavior without retraining.  
+
+# **Applications**  
+- **Education**: Step-by-step explanations for visual and textual content in learning materials, including images and videos.  
+- **Automation**: Integrating with robotics or smart devices for performing tasks based on visual and textual data.  
+- **Content Creation**: Assisting in creating or analyzing video and image-based content, such as tutorials or product demos.  
+- **Accessibility**: Enhancing accessibility tools for visually impaired or multilingual users by providing clear explanations of image or video content.  
+- **Global Q&A Systems**: Supporting cross-lingual question answering in images and videos for diverse user bases.  

added_tokens.json

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+}

chat_template.json

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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 %}"
+}

config.json

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+{
+  "architectures": [
+    "Qwen2VLForConditionalGeneration"
+  ],
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+  "bos_token_id": 151643,
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+  "hidden_act": "silu",
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+  "intermediate_size": 8960,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2_vl",
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+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
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+  "rms_norm_eps": 1e-06,
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+    "type": "default"
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+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
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+  "vision_config": {
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+    "in_chans": 3,
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+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "image-text-to-text", "allow_remote": true}

generation_config.json

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preprocessor_config.json

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+  "do_normalize": true,
+  "do_rescale": true,
+  "do_resize": true,
+  "image_mean": [
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+  "image_processor_type": "Qwen2VLImageProcessor",
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special_tokens_map.json

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+}