S
shagrawal
Co-authors: Priya Kedia, Michael Tremeer
Contributors: Ranjani Mani
Phi-3 Vision, a lightweight and state-of-the-art open multimodal model, is a significant advancement in Microsoft's AI offerings. Developed with a focus on producing a high-quality, reasoning focused model, Phi-3 Vision utilizes synthetic data and curated publicly available web data to ensure its robustness and versatility. At only 4.2 billion parameters, it strikes an impressive balance between performance and efficiency, making it an attractive option for a wide range of applications.
As the first multimodal model in the Phi-3 family, Phi-3 Vision extends beyond the capabilities of its predecessors – Phi-3-mini, Phi-3-small, and Phi-3-medium – by seamlessly blending language and visual input. It boasts a context length of 128K tokens, allowing it to support complex and nuanced interactions. Designed with the intention to run on devices, Phi-3 Vision provides the benefits of offline operation, cost-effectiveness, and user privacy.
Phi-3 Vision has demonstrated versatility across various use cases, including Optical Character Recognition (OCR), Image Captioning, Table Parsing, and Reading Comprehension on Scanned Documents, among others. Its ability to provide high-quality reasoning with both visual and text input capabilities will drive innovation and lead to the development of new applications that are both transformative and sustainable. As an example, here is a quick demo showcasing how car footage can be analyzed to assess vehicle damages on an edge device, giving instant feedback to end user. When paired together with a larger LLM like GPT-4o, Phi-3 can form part of hybrid workflow that combines the efficiencies of Phi-3 for simpler tasks with the power of GPT-4o for more challenges tasks, unlocking the best of both worlds in a multi-step pipeline.
The landscape of artificial intelligence (AI) is in a state of rapid evolution, and within this space,
Microsoft's Phi-3-Vision emerges as a noteworthy trendsetter. Phi-3-Vision, a member of Microsoft's broader Phi-3 family, represents a significant leap in multimodal AI capabilities, blending language and vision processing.
Multimodal AI models, such as the Phi-3-Vision, are increasingly gaining attention due to their ability to interpret and analyze both textual and visual data. This dual capability not only enhances user interaction with digital content but also opens up new avenues for data analysis and accessibility. As businesses and consumers alike demand more intuitive and capable AI solutions, the prominence of multimodal models is expected to grow.
Phi-3-Vision's open-source nature stands out as a key trend in the AI market. By allowing developers to access and build upon the model freely, Microsoft is fostering a community-driven ecosystem where innovation can thrive. This approach is likely to inspire other AI developers and companies to adopt and build upon the model, potentially leading to a surge in collaborative AI advancements.
Another significant trend is the shift towards more efficient AI models that can operate on devices with limited computational power, such as smartphones and edge devices. Phi-3-Vision's compact yet powerful architecture exemplifies this trend, which is driven by the need for cost-effective and less compute-intensive AI services. As a result, the market is witnessing a growing interest in AI models that are optimized for on-device, edge, and offline inference scenarios.
The Phi-3 project's goal to democratize AI through smaller, efficient models aligns with a broader market trend towards making AI more accessible to everyday users and developers. By making the model available on Azure AI Studio, Azure AI model catalog as well as on hugging face, Microsoft has simplified the adoption and integration of AI capabilities into various applications.
Phi-3-Vision's adaptability and performance indicate a trend towards integrating advanced AI models into a wide array of industries. From document digitization to advanced automation solutions, Phi-3-Vision and similar models are set to transform various sectors by enhancing productivity and reducing operational costs.
Despite its relatively compact size, Phi-3-Vision demonstrates impressive performance that is on par with much larger models, and it is one of the smallest LLMs with multimodal capabilities. This efficiency makes it particularly suitable for deployment on devices with limited computational resources, such as smartphones. In addition, the optimized versions of the model in ONNX format ensure accelerated inference on both CPU and GPU across different platforms, including server, desktop, and mobile environments.
Phi-3 Vision is based on the Transformer model architecture, which has demonstrated remarkable success in various NLP tasks. It contains an image encoder, connector, projector, and Phi-3 Mini language model. The model's ability to support up to 128K context length in tokens with just 4.2 billion parameters allows for extensive multimodal reasoning, making it adept at understanding and generating content from complex visual inputs like charts, graphs, and tables. Its integration into the development version (4.40.2) of the industry-standard transformers python library further simplifies its adoption in AI-driven applications.
One of the factors that differentiates Phi-3 Vision is its training data. Unlike many other models that rely solely on human-generated data (such as from the web and published books etc.), the training datasets used to train the Phi-3 family of models are created using advanced synthetic data generation techniques, along with highly curated public web data. This approach aims to maximize the quality of the training data with a specific focus on helping the model to develop advanced reasoning skills and the ability to solve problems. This training dataset contributes to the model's robustness and versatility, enabling it to perform well beyond expectations in various visual reasoning tasks. It has demonstrated superior performance in a range of multimodal benchmarks, outperforming competitors such as Claude 3 Haiku and coming close to the capabilities of more much larger models like OpenAI's GPT-4V.
Performance Comparison of Phi-3 Vision
In the broader AI industry, there is a strong trend of replacing larger models like GPT-4o with more efficient models like Phi-3 as AI builders seek to optimize their GenAI use-cases. A common pattern is to launch a use case with a powerful LLM like GPT-4o, and once the solution is in production, look to incorporate a more efficient SLM like Phi-3 for some of the less complicated and more narrow parts of the problem. This also means that the initial batch of production data that is generated by GPT-4o can be used to fine-tune the Phi-3 model, offering comparable accuracy of the large model at a fraction of the cost. This approach has been documented as a reliable and effective technique for reducing the costs of LLM-powered solutions while maintaining similar performance.
Given this trend, Phi-3 offers a potential to be leveraged for many use cases involving memory/compute constrained environments, latency bound scenarios, general image understanding, OCR, chart and table understanding etc.
Phi-3 Vision Use Case Spectrum
Use Case: Combining text extraction and image classification to streamline the Know Your Customer (KYC) process. This helps in verifying customer identity and ensuring compliance with legal and regulatory standards in sectors like banking and financial services. Example: Automating the verification of identity documents such as passports and driving licenses by extracting text and checking the validity of images to expedite the KYC process.
Use Case: Using text and image analysis to enhance customer support operations, including the management of product returns. This approach helps in quickly identifying issues through customer descriptions and photos of returned items, thereby improving customer satisfaction and operational efficiency. Example: Automatically processing customer complaints that include photos of defective products, enabling rapid resolution through efficient handling of returns or exchanges.
Use Case: Integrating text and image analysis to identify and moderate inappropriate content on social media platforms. This helps in maintaining community standards and ensuring a safe environment for users. Example: Automatically detecting and removing posts with offensive language and harmful images, ensuring compliance with community guidelines and promoting a positive user experience.
Use Case: Analyzing video footage for assessing damages and verifying claims in auto and home insurance sectors. This capability allows for accurate evaluation of incidents and helps in processing claims more efficiently. Example: Processing video footage of a car accident to identify the cause and extent of damage, aiding in quick and accurate claim settlements. Similarly, evaluating home damage videos for insurance claim assessments.
Use Case: Utilizing text and image analysis to develop interactive and adaptive educational tools. This can enhance learning by providing customized content and feedback based on both text and visual inputs from students. Example: Creating adaptive learning platforms that analyze students' written responses and hand-drawn diagrams to offer personalized feedback and additional resources.
With the trend towards decentralized computing, users of edge devices such as smartphones, tablets, and IoT devices require lightweight AI models that can operate with limited computing resources. Phi-3 Vision's ability to run efficiently on smaller devices makes it attractive to this demographic. By leveraging ONNX Runtime Mobile and Web, Microsoft is working to enable Phi-3 Vision on a broad spectrum of devices, from smartphones to wearables. This has led to an interest in Phi-3 vision models from a wide demographic of customers.
Target Customer Demographics
Partnerships with industry players, as seen with DDI Labs’ integration of Phi-3 Vision, can lead to transformative applications in areas such as video analytics and automation. Its potential to improve operations, such as in dock automation, demonstrates the practical benefits of adopting such advanced AI tools that address real-world challenges.
Getting Started
Deploy a quantized version of the model at the edge
ONNX Runtime | Blogs/accelerating-phi-3
Finetune the model for your domain specific use case
Phi-3CookBook/md/04.Fine-tuning/FineTuning_Qlora.md at main · microsoft/Phi-3CookBook (github.com)
Despite safety post-training, the potential for unfair or biased outcomes remains a concern due to societal biases reflected in training data. Ongoing efforts to mitigate these risks are critical to maintaining the integrity and social acceptability of AI technologies like Phi-3 Vision.
As AI models grow in complexity and capability, ensuring computational and energy efficiency becomes increasingly challenging. Striking a balance between performance and resource consumption is essential for sustainable AI development, especially for models intended for widespread use across various devices.
With the proliferation of AI in personal and professional domains, security and privacy concerns must be addressed. Protecting user data and preventing unauthorized access or misuse of AI technologies are paramount for maintaining user trust and complying with regulatory requirements.
In conclusion, the Phi-3 family, spearheaded by Phi-3-vision, exemplifies the progress and potential of AI. While there are challenges to be addressed, the opportunities these models present are vast and ripe for exploration. As AI continues to evolve, models like Phi-3 Vision will undoubtedly be instrumental in shaping innovative solutions that could redefine the way we interact with technology and process information in our digital world.
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Contributors: Ranjani Mani
Phi-3 Vision, a lightweight and state-of-the-art open multimodal model, is a significant advancement in Microsoft's AI offerings. Developed with a focus on producing a high-quality, reasoning focused model, Phi-3 Vision utilizes synthetic data and curated publicly available web data to ensure its robustness and versatility. At only 4.2 billion parameters, it strikes an impressive balance between performance and efficiency, making it an attractive option for a wide range of applications.
As the first multimodal model in the Phi-3 family, Phi-3 Vision extends beyond the capabilities of its predecessors – Phi-3-mini, Phi-3-small, and Phi-3-medium – by seamlessly blending language and visual input. It boasts a context length of 128K tokens, allowing it to support complex and nuanced interactions. Designed with the intention to run on devices, Phi-3 Vision provides the benefits of offline operation, cost-effectiveness, and user privacy.
Phi-3 Vision has demonstrated versatility across various use cases, including Optical Character Recognition (OCR), Image Captioning, Table Parsing, and Reading Comprehension on Scanned Documents, among others. Its ability to provide high-quality reasoning with both visual and text input capabilities will drive innovation and lead to the development of new applications that are both transformative and sustainable. As an example, here is a quick demo showcasing how car footage can be analyzed to assess vehicle damages on an edge device, giving instant feedback to end user. When paired together with a larger LLM like GPT-4o, Phi-3 can form part of hybrid workflow that combines the efficiencies of Phi-3 for simpler tasks with the power of GPT-4o for more challenges tasks, unlocking the best of both worlds in a multi-step pipeline.
Market Trends
The landscape of artificial intelligence (AI) is in a state of rapid evolution, and within this space,
Microsoft's Phi-3-Vision emerges as a noteworthy trendsetter. Phi-3-Vision, a member of Microsoft's broader Phi-3 family, represents a significant leap in multimodal AI capabilities, blending language and vision processing.
The Rise of Multimodal AI Models
Multimodal AI models, such as the Phi-3-Vision, are increasingly gaining attention due to their ability to interpret and analyze both textual and visual data. This dual capability not only enhances user interaction with digital content but also opens up new avenues for data analysis and accessibility. As businesses and consumers alike demand more intuitive and capable AI solutions, the prominence of multimodal models is expected to grow.
Open Source as a Catalyst for Innovation
Phi-3-Vision's open-source nature stands out as a key trend in the AI market. By allowing developers to access and build upon the model freely, Microsoft is fostering a community-driven ecosystem where innovation can thrive. This approach is likely to inspire other AI developers and companies to adopt and build upon the model, potentially leading to a surge in collaborative AI advancements.
Efficiency and Edge Computing
Another significant trend is the shift towards more efficient AI models that can operate on devices with limited computational power, such as smartphones and edge devices. Phi-3-Vision's compact yet powerful architecture exemplifies this trend, which is driven by the need for cost-effective and less compute-intensive AI services. As a result, the market is witnessing a growing interest in AI models that are optimized for on-device, edge, and offline inference scenarios.
AI Accessibility and Democratization
The Phi-3 project's goal to democratize AI through smaller, efficient models aligns with a broader market trend towards making AI more accessible to everyday users and developers. By making the model available on Azure AI Studio, Azure AI model catalog as well as on hugging face, Microsoft has simplified the adoption and integration of AI capabilities into various applications.
Future Integration in Various Industries
Phi-3-Vision's adaptability and performance indicate a trend towards integrating advanced AI models into a wide array of industries. From document digitization to advanced automation solutions, Phi-3-Vision and similar models are set to transform various sectors by enhancing productivity and reducing operational costs.
Competitive Landscape
Despite its relatively compact size, Phi-3-Vision demonstrates impressive performance that is on par with much larger models, and it is one of the smallest LLMs with multimodal capabilities. This efficiency makes it particularly suitable for deployment on devices with limited computational resources, such as smartphones. In addition, the optimized versions of the model in ONNX format ensure accelerated inference on both CPU and GPU across different platforms, including server, desktop, and mobile environments.
Model Architecture and Capabilities
Phi-3 Vision is based on the Transformer model architecture, which has demonstrated remarkable success in various NLP tasks. It contains an image encoder, connector, projector, and Phi-3 Mini language model. The model's ability to support up to 128K context length in tokens with just 4.2 billion parameters allows for extensive multimodal reasoning, making it adept at understanding and generating content from complex visual inputs like charts, graphs, and tables. Its integration into the development version (4.40.2) of the industry-standard transformers python library further simplifies its adoption in AI-driven applications.
Training Data and Quality
One of the factors that differentiates Phi-3 Vision is its training data. Unlike many other models that rely solely on human-generated data (such as from the web and published books etc.), the training datasets used to train the Phi-3 family of models are created using advanced synthetic data generation techniques, along with highly curated public web data. This approach aims to maximize the quality of the training data with a specific focus on helping the model to develop advanced reasoning skills and the ability to solve problems. This training dataset contributes to the model's robustness and versatility, enabling it to perform well beyond expectations in various visual reasoning tasks. It has demonstrated superior performance in a range of multimodal benchmarks, outperforming competitors such as Claude 3 Haiku and coming close to the capabilities of more much larger models like OpenAI's GPT-4V.
Performance Comparison of Phi-3 VisionTarget Use Cases and Applicability
In the broader AI industry, there is a strong trend of replacing larger models like GPT-4o with more efficient models like Phi-3 as AI builders seek to optimize their GenAI use-cases. A common pattern is to launch a use case with a powerful LLM like GPT-4o, and once the solution is in production, look to incorporate a more efficient SLM like Phi-3 for some of the less complicated and more narrow parts of the problem. This also means that the initial batch of production data that is generated by GPT-4o can be used to fine-tune the Phi-3 model, offering comparable accuracy of the large model at a fraction of the cost. This approach has been documented as a reliable and effective technique for reducing the costs of LLM-powered solutions while maintaining similar performance.
Given this trend, Phi-3 offers a potential to be leveraged for many use cases involving memory/compute constrained environments, latency bound scenarios, general image understanding, OCR, chart and table understanding etc.
Phi-3 Vision Use Case SpectrumDocument and Image Analysis for KYC
Use Case: Combining text extraction and image classification to streamline the Know Your Customer (KYC) process. This helps in verifying customer identity and ensuring compliance with legal and regulatory standards in sectors like banking and financial services. Example: Automating the verification of identity documents such as passports and driving licenses by extracting text and checking the validity of images to expedite the KYC process.
Enhanced Customer Support and Product Returns
Use Case: Using text and image analysis to enhance customer support operations, including the management of product returns. This approach helps in quickly identifying issues through customer descriptions and photos of returned items, thereby improving customer satisfaction and operational efficiency. Example: Automatically processing customer complaints that include photos of defective products, enabling rapid resolution through efficient handling of returns or exchanges.
Content Moderation for Social Media
Use Case: Integrating text and image analysis to identify and moderate inappropriate content on social media platforms. This helps in maintaining community standards and ensuring a safe environment for users. Example: Automatically detecting and removing posts with offensive language and harmful images, ensuring compliance with community guidelines and promoting a positive user experience.
Video Footage Analysis for Auto and Home Insurance
Use Case: Analyzing video footage for assessing damages and verifying claims in auto and home insurance sectors. This capability allows for accurate evaluation of incidents and helps in processing claims more efficiently. Example: Processing video footage of a car accident to identify the cause and extent of damage, aiding in quick and accurate claim settlements. Similarly, evaluating home damage videos for insurance claim assessments.
Visual Content Analysis for Educational Tools
Use Case: Utilizing text and image analysis to develop interactive and adaptive educational tools. This can enhance learning by providing customized content and feedback based on both text and visual inputs from students. Example: Creating adaptive learning platforms that analyze students' written responses and hand-drawn diagrams to offer personalized feedback and additional resources.
With the trend towards decentralized computing, users of edge devices such as smartphones, tablets, and IoT devices require lightweight AI models that can operate with limited computing resources. Phi-3 Vision's ability to run efficiently on smaller devices makes it attractive to this demographic. By leveraging ONNX Runtime Mobile and Web, Microsoft is working to enable Phi-3 Vision on a broad spectrum of devices, from smartphones to wearables. This has led to an interest in Phi-3 vision models from a wide demographic of customers.
Target Customer DemographicsPartnerships and Collaborations
Partnerships with industry players, as seen with DDI Labs’ integration of Phi-3 Vision, can lead to transformative applications in areas such as video analytics and automation. Its potential to improve operations, such as in dock automation, demonstrates the practical benefits of adopting such advanced AI tools that address real-world challenges.
Taking a Deep Dive into Code
Getting Started
With basics taken care of, what’s next?
Deploy a quantized version of the model at the edge
ONNX Runtime | Blogs/accelerating-phi-3
Finetune the model for your domain specific use case
Phi-3CookBook/md/04.Fine-tuning/FineTuning_Qlora.md at main · microsoft/Phi-3CookBook (github.com)
Considerations for the future for Phi-3 Vision Team
Ethical Considerations and Bias Mitigation
Despite safety post-training, the potential for unfair or biased outcomes remains a concern due to societal biases reflected in training data. Ongoing efforts to mitigate these risks are critical to maintaining the integrity and social acceptability of AI technologies like Phi-3 Vision.
Computational and Energy Efficiency
As AI models grow in complexity and capability, ensuring computational and energy efficiency becomes increasingly challenging. Striking a balance between performance and resource consumption is essential for sustainable AI development, especially for models intended for widespread use across various devices.
Security and Privacy
With the proliferation of AI in personal and professional domains, security and privacy concerns must be addressed. Protecting user data and preventing unauthorized access or misuse of AI technologies are paramount for maintaining user trust and complying with regulatory requirements.
Final Thoughts
In conclusion, the Phi-3 family, spearheaded by Phi-3-vision, exemplifies the progress and potential of AI. While there are challenges to be addressed, the opportunities these models present are vast and ripe for exploration. As AI continues to evolve, models like Phi-3 Vision will undoubtedly be instrumental in shaping innovative solutions that could redefine the way we interact with technology and process information in our digital world.
References
Microsoft Official and Tech Community
- New models added to the Phi-3 family, available on Microsoft Azure | Microsoft Azure Blog
- Microsoft announces Phi-3-vision, a new multimodal SLM for on-device AI scenarios
- Affordable Innovation: Unveiling the Pricing of Phi-3 SLMs on Models as a Service
- Using Phi-3 & C# with ONNX for text and vision samples
- Getting Started - Generative AI with Phi-3-mini: Running Phi-3-mini in Intel AI PC
- GitHub - microsoft/Phi-3CookBook: Samples for getting a quick understanding and exploration of Phi-3 models
Technical and AI-focused Publications
- Enjoy the Power of Phi-3 with ONNX Runtime on your device
- microsoft/Phi-3-medium-128k-instruct · Hugging Face
- https://huggingface.co/microsoft/Phi3 Vision
- https://huggingface.co/microsoft/Phi3 Vision-onnx-cpu
- https://huggingface.co/microsoft/Phi3 Vision-onnx-cuda
- Microsoft makes Phi-3 generally available, previews its Phi-3-vision multimodal small language model
- Microsoft Phi-3: From Language to Vision, this New AI Model is Transforming AI
- Medium
- Microsoft announced Phi-3 Vision, an AI model for phones that can analyse pictures
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