Testing Microsoft's New VLM - Phi-3 Vision

Sam Witteveen
7 Jun 202414:53

TLDRThe video discusses Microsoft's Phi-3 Vision model, a part of the new Phi models introduced at the Build conference. It's a multimodal AI capable of understanding images and text, with a focus on tasks like visual question answering and analyzing diagrams. The model has a 4.2 billion parameter version and is optimized for edge devices. The script explores its capabilities, including OCR, synthetic data training, and its performance in various tests, highlighting its potential for practical applications despite some inaccuracies.

Takeaways

  • 🌐 Microsoft introduced new Phi-3 models at the Build conference, focusing on the Phi-3 Vision model with 4.2 billion parameters.
  • 🔍 The Phi-3 Vision model is designed for multimodal tasks, including visual question answering and understanding diagrams.
  • 🔄 Microsoft has optimized these models for edge computing and has integrated them with Onyx runtimes.
  • 📈 The training of the Phi-3 Vision model involved 500 billion vision and text tokens and was completed in just 1.5 days using 512 H100s.
  • 📚 The model is based on the 'Sequence of Works' methodology and utilizes synthetic data for enhanced training results.
  • 🖼️ It features an image encoder, similar to PaliGemma, and uses a transformer-based language model with the Phi-3 mini 128K instruct.
  • 🤖 The model can process images and text in an interleaved manner without a fixed order for image and text tokens.
  • 📊 The Phi-3 Vision model demonstrated strong performance in visual question answering and OCR tasks during testing.
  • 🛠️ The model can be run in 4-bit, significantly reducing memory usage while maintaining reasonable performance.
  • 📝 Microsoft has released the model as open weights but not open source, requiring agreement to their terms for use.
  • 🔍 The model's capabilities in OCR and understanding visual content make it a promising tool for tasks like receipt processing.

Q & A

  • What was the main focus of Microsoft's Build conference?

    -The main focus of Microsoft's Build conference included the announcement of new Copilot+ PCs and the introduction of the Phi-3 Vision model among other things in the copilot ecosystem.

  • What is the Phi-3 Vision model?

    -The Phi-3 Vision model is a 4.2 billion parameter AI model launched by Microsoft, designed to understand visual data and perform tasks such as visual question answering and interpreting diagrams.

  • How does the Phi-3 Vision model differ from Google's PaliGemma?

    -The Phi-3 Vision model is optimized for edge computing and multimodality, focusing on visual tasks, while PaliGemma is more of a research release with less fine-tuning and is designed for further customization by users.

  • What is the context length of the Phi-3 Vision model?

    -One of the versions of the Phi-3 Vision model has a context length that extends up to 128,000 tokens, which is quite impressive.

  • How was the Phi-3 Vision model trained?

    -The Phi-3 Vision model was trained on 500 billion vision and text tokens, utilizing synthetic data for better training results and following the sequence of works initiated with the 'Textbooks are all you need' paper.

  • What is the significance of the synthetic data in training the Phi-3 Vision model?

    -Synthetic data allows for more control over the training process and is suspected to provide a large advantage in training AI models, as it seems to be the case with other models like GPT-4 and GPT 5 Gemini 2.

  • How does the Phi-3 Vision model handle multimodality?

    -The Phi-3 Vision model handles multimodality by interleaving image and text tokens without a specific order, allowing for a flexible input of both types of data.

  • What is the memory usage of the Phi-3 Vision model when running in full resolution?

    -When running in full resolution, the Phi-3 Vision model uses only 8 gigabytes of memory, making it relatively lightweight for its capabilities.

  • How does the Phi-3 Vision model perform in OCR tasks?

    -The Phi-3 Vision model demonstrates good performance in OCR tasks, accurately identifying text in images such as receipts and interpreting abbreviations correctly.

  • What is the memory usage of the Phi-3 Vision model when running in 4-bit?

    -When running in 4-bit, the memory usage of the Phi-3 Vision model is reduced to under three gigabytes, showing a significant reduction in resource requirements.

  • Why might the Phi-3 Vision model not be included in Ollama yet?

    -The reason for the Phi-3 Vision model not being included in Ollama yet could be due to various factors such as the model not being converted for Ollama or Microsoft waiting for the conversion to be done.

Outlines

00:00

🤖 Microsoft's Build Conference and Phi-3 Vision Model

The script discusses Microsoft's recent Build conference, where they unveiled several new technologies, including the new Copilot+ PCs and an expansion of the Copilot ecosystem. The focus is on the launch of the Phi-3 models, particularly the Phi-3 Vision, a 4.2 billion parameter model designed for multimodal tasks such as understanding diagrams and visual question answering. The model is optimized for edge deployment and has been trained on 500 billion vision and text tokens, with a training time of 1.5 days using 512 H100s. The script also compares the Phi-3 Vision with Google's PaliGemma, noting differences in their approaches to fine-tuning and multimodality.

05:04

🔍 Exploring the Phi-3 Vision Model's Capabilities

This section delves into the technical aspects of the Phi-3 Vision model, including its training process and architecture. It mentions the use of synthetic data to enhance training outcomes, similar to the approach taken with other models like GPT-4 and GPT 5 Gemini 2. The script describes the model's technical specifications, such as the use of a CLIP encoder for images and a transformer-based language model. It also highlights the model's ability to process text and images in an interleaved manner, as well as its potential for OCR tasks after fine-tuning. The script provides examples of the model's performance in vision question answering and OCR, demonstrating its accuracy in interpreting images and text.

10:07

🛠 Testing the Phi-3 Vision Model with Various Tasks

The script moves on to practical demonstrations of the Phi-3 Vision model's capabilities, including its performance in handling image recognition tasks and OCR. It shows the model's ability to answer questions about images, such as identifying objects and their characteristics, as well as its OCR capabilities in reading receipts and interpreting abbreviations. The script also tests the model's performance in 4-bit mode, noting a reduction in memory usage and some degradation in the quality of answers. Despite this, the model still performs well in recognizing prices and items on a receipt, although it makes an error in counting the number of peanut butter items purchased.

Mindmap

Keywords

💡Microsoft Build Conference

The Microsoft Build Conference is an annual developer event organized by Microsoft, where they announce new products and technologies. In the script, it's mentioned as the platform where Microsoft introduced the new Copilot+ PCs and the Phi-3 Vision model, indicating its significance in the tech industry's innovation cycle.

💡Phi-3 Vision

Phi-3 Vision is a new model in Microsoft's Phi series of AI models. It is a 4.2 billion parameter model designed for multimodal tasks, such as understanding diagrams and visual question answering. The script discusses its capabilities and how it differs from Google's PaliGemma, emphasizing its optimization for edge devices and its unique approach to multimodality.

💡Multimodality

Multimodality in the context of AI refers to the ability of a system to process and understand multiple types of input data, such as text and images. The script mentions that the Phi-3 Vision model can handle multimodal inputs, which is crucial for tasks like visual question answering, where both the visual content and the accompanying text need to be understood.

💡Fine-tuning

Fine-tuning is a technique in machine learning where a pre-trained model is further trained on a specific task to improve its performance. The script contrasts the fine-tuning approach of the Phi-3 Vision with that of Google's PaliGemma, suggesting that Microsoft has taken a lighter fine-tuning approach, allowing for more customization by the users.

💡Synthetic Data

Synthetic data is artificially generated data used to train machine learning models. The script mentions that synthetic data was used in the training of the Phi-3 Vision model, hinting at a potential advantage in training AI with controllable data that can enhance the model's performance.

💡OCR (Optical Character Recognition)

OCR is a technology that converts various types of documents, such as scanned paper documents, PDF files, or images captured by a digital camera, into editable and searchable text. The script discusses the Phi-3 Vision's OCR capabilities, particularly its ability to interpret text from images, such as receipts, which is showcased in the video.

💡Transformer-based Language Model

A transformer-based language model is a type of AI model that uses the transformer architecture to process sequential data. The script explains that the Phi-3 Vision uses a transformer-based language model, specifically the Phi-3 mini 128 K instruct, to handle the text component of its multimodal tasks.

💡Flash Attention

Flash attention is a technique that can speed up the processing time of AI models by optimizing the attention mechanism, which is a key component of transformer models. The script mentions that the Phi-3 model can make use of flash attention, suggesting that it may offer performance benefits over other models.

💡4-bit

In the context of AI models, 4-bit refers to the precision at which the model's weights are stored and processed, which can affect memory usage and performance. The script discusses running the Phi-3 Vision model in 4-bit precision, which significantly reduces memory usage, making it more accessible for deployment on devices with limited resources.

💡Vision Question Answering (VQA)

VQA is a task in AI where the model must answer questions about the content of an image. The script demonstrates the Phi-3 Vision's VQA capabilities, showing how it can understand and interpret both the visual elements and the text in images to provide accurate answers to questions.

Highlights

Microsoft introduced new Phi-3 models at the Build conference, focusing on the Phi-3 Vision model.

Phi-3 Vision is part of a family of models with varying sizes, from tiny to the large Phi-3 medium with 14 billion parameters.

The Phi-3 Vision model has 4.2 billion parameters and is optimized for edge computing and multimodal capabilities.

Microsoft's approach to fine-tuning differs from Google's PaliGemma, with a focus on lighter fine-tuning and user customization.

Phi-3 Vision is designed for tasks such as understanding diagrams and visual question answering.

The model accepts text and image inputs, representing multimodality, with no audio input/output yet.

One version of Phi-3 Vision has an impressive context length of 128,000 tokens.

Training for Phi-3 Vision was completed in just 1.5 days using 512 H100s, indicating efficient training.

The model was trained on 500 billion vision and text tokens, showcasing a massive dataset.

Phi-3 Vision is an open weight release, not open source, requiring agreement to terms.

The training methodology includes the use of synthetic data for better results, similar to earlier Phi papers.

Technical specifications reveal an image encoder and a transformer-based language model, with interleaved image and text tokens.

The model's training process includes supervised fine-tuning and DPO for alignment on a smaller dataset of 15 billion tokens.

Phi-3 Vision can be loaded quickly using the Hugging Face transfer package, ideal for large models.

The model demonstrates full vision question answering and language processing capabilities.

Phi-3 Vision shows impressive OCR capabilities, accurately interpreting receipts and images.

In 4-bit mode, the model has a significantly reduced memory footprint, under 3 gigabytes.

Despite the reduced memory usage in 4-bit mode, the model maintains accuracy in tasks like interpreting receipts.

The model's performance in 4-bit mode shows some degradation but remains functional for practical applications.

Phi-3 Vision is recommended for tasks requiring vision and language processing, with a small footprint.

Casual Browsing

Stable Diffusion 3 API Tutorial | Testing the Power of This New Model by Stability AI

2024-06-14 21:15:00

Stable Diffusion vs Midjourney vs DALL-E 3: Testing Limits in the AI Art Prompt Battle!

2024-06-13 03:35:00

Stable Diffusion 3 vs ChatGPT Dalle-3 vs Midjourney [NEW Best Image Generator?]

2024-05-22 14:00:01

Create Consistent Characters Using DALLE 3 (New GPT's🤯)

2024-05-22 13:30:02

Flux Completely Destroys Stable Diffusion 3! The New Champion

2024-08-08 04:02:00