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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
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Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
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Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Microsoft Phi-3: From Language to Vision, this New AI Model is Transforming AI

NISHANT TIWARI
NISHANT TIWARI Last Updated : 22 May, 2024
7 min read

Introduction

Microsoft has pushed the boundaries with its latest AI offerings, the Phi-3 family of models. These compact yet mighty models were unveiled at the recent Microsoft Build 2024 conference and promise to deliver exceptional AI performance across diverse applications. The family includes the bite-sized Phi-3-mini, the slightly larger Phi-3-small, the midrange Phi-3-medium, and the innovative Phi-3-vision – a multimodal model that seamlessly blends language and vision capabilities. These models are designed for real-world practicality, offering top-notch reasoning abilities and lightning-fast responses while being lean in computational requirements.

The Phi-3 models are trained on high-quality datasets, including synthetic data, filtered public websites, and selected educational content. This ensures they excel in language understanding, reasoning, coding, and mathematical tasks. The Phi-3-vision model stands out with its ability to process text and images, supporting a 128K token context length and demonstrating impressive performance in tasks like OCR and chart understanding. Developed in line with Microsoft’s Responsible AI principles, the Phi-3 family offers a robust, safe, and versatile toolset for developers to build cutting-edge AI applications.

Phi-3 model

Table of contents

The Microsoft Phi-3 Family

The Microsoft Phi-3 family represents a series of advanced small language models (SLMs) developed by Microsoft. These models are designed to offer high performance and cost-effectiveness, outperforming other models of similar or larger sizes across various benchmarks. The Phi-3 family includes four distinct models: Phi-3-mini, Phi-3-small, Phi-3-medium, and Phi-3-vision. Each model is instruction-tuned and adheres to Microsoft’s responsible AI, safety, and security standards, ensuring they are ready for use in various applications.

Description of the Microsoft Phi-3 Models

Phi-3-mini

Parameters: 3.8 billion

 (128K and 4K).

Context Length: Available in 128K and 4K tokens

Applications: It is suitable for tasks requiring efficient reasoning and limited computational resources. It is ideal for content authoring, summarization, question-answering, and sentiment analysis.

Phi-3-small

Parameters: 7 billion

(128K and 8K).

Context Length: Available in 128K and 8K tokens

Applications: Excels in tasks needing strong language understanding and generation capabilities. Outperforms larger models like GPT-3.5T in language, reasoning, coding, and math benchmarks.

Phi-3-medium

Parameters: 14 billion

(128K and 4K).

Context Length: Available in 128K and 4K tokens

Applications: Suitable for more complex tasks requiring extensive reasoning capabilities. Outperforms models like Gemini 1.0 Pro in various benchmarks.

Phi-3-vision

Parameters: 4.2 billion

(128k)

Context Length: 128K tokens

Capabilities: This multimodal model integrates language and vision capabilities. It is suitable for OCR, general image understanding, and tasks involving charts and tables. It is built on a robust dataset of synthetic data and high-quality public websites.

Key Features and Benefits of Phi-3 Models

The Phi-3 models offer several key features and benefits that make them stand out in the field of AI:

  • High Performance: Outperform models of the same size and larger across various benchmarks, including language, reasoning, coding, and math.
  • Cost-Effective: It is designed to deliver high-quality results at a lower cost, making it accessible to a wider range of applications and organizations.
  • Multimodal Capabilities: Phi-3-vision integrates language and vision capabilities, enabling it to handle tasks that require understanding text and images.
  • Extensive Context Length: Supports context lengths up to 128K tokens, allowing for comprehensive understanding and processing of large text inputs.
  • Optimization for Various Hardware: It runs on various devices, from mobile to web deployments, and supports NVIDIA GPUs and Intel accelerators.
  • Responsible AI Standards: Developed and fine-tuned according to Microsoft’s standards, ensuring safety, reliability, and ethical considerations.

Comparison with Other AI Models in the Market

When compared to other AI models in the market, the Phi-3 family showcases superior performance and versatility:

  • GPT-3.5T: While GPT-3.5T is a powerful model, Phi-3-small, with only 7 billion parameters, outperforms it across several benchmarks, including language and reasoning tasks.
  • Gemini 1.0 Pro: The Phi-3-medium model surpasses Gemini 1.0 Pro in performance, demonstrating better results in coding and math benchmarks.
  • Claude-3 Haiku and Gemini 1.0 Pro V: Phi-3-vision, with its multimodal capabilities, outperforms these models in visual reasoning tasks, OCR, and understanding charts and tables.

The Phi-3 models also offer the advantage of being optimized for efficiency, making them suitable for memory and compute-constrained environments. They are designed to provide quick responses in latency-bound scenarios, making them ideal for real-time applications. Furthermore, their responsible AI development ensures they are safer and more reliable for various uses.

Model Specifications and Capabilities

PHI-3

Here are the model specifications and capabilities:

Phi-3-mini: Parameters, Context Lengths, Applications

Phi-3-mini is designed as an efficient language model with 3.8 billion parameters. This model is available in two context lengths, 128K and 4K tokens, allowing for flexible application across different tasks. Phi-3-mini is well-suited for applications requiring efficient reasoning and quick response times, making it ideal for content authoring, summarization, question-answering, and sentiment analysis. Despite its relatively small size, Phi-3-mini outperforms larger models in specific benchmarks due to its optimized architecture and high-quality training data.

Phi-3-small: Parameters, Context Lengths, Applications

Phi-3-small features 7 billion parameters and is available in 128K and 8K context lengths. This model excels in tasks that demand strong language understanding and generation capabilities. Phi-3-small outperforms larger models, such as GPT-3.5T, across various language, reasoning, coding, and math benchmarks. Its compact size and high performance make it suitable for a broad range of applications, including advanced content creation, complex query handling, and detailed analytical tasks.

Phi-3-medium: Parameters, Context Lengths, Applications

Phi-3-medium is the largest model in the Phi-3 family, with 14 billion parameters. It offers context lengths of 128K and 4K tokens. This model is designed for more complex tasks that require extensive reasoning capabilities. Phi-3-medium outperforms models like Gemini 1.0 Pro, making it a powerful tool for applications that need deep analytical abilities, such as extensive document processing, advanced coding assistance, and comprehensive language understanding.

Phi-3-vision: Parameters, Multimodal Capabilities, Applications

Phi-3-vision is a unique multimodal model in the Phi-3 family, featuring 4.2 billion parameters and supporting a context length of 128K tokens. This model integrates language and vision capabilities, making it suitable for various applications requiring text and image processing. Phi-3-vision excels in OCR, general image understanding, and chart and table interpretation. It is built on high-quality datasets, including synthetic data and publicly available documents, ensuring robust performance in various multimodal scenarios.

Performance Benchmarks and Comparisons

The Microsoft Phi-3 models have been rigorously benchmarked against other prominent AI models, demonstrating superior performance across multiple metrics. Below is a detailed comparison highlighting how the Phi-3 models excel:

PHI-3
Phi-3-vision with 4.2B parameters
PHI-3
Phi-3-medium with 14B parameters
PHI-3
Phi-3-small with only 7B parameters

These benchmarks illustrate the superior performance of the Phi-3 models across various tasks, proving that they can outperform larger models while being more efficient and cost-effective. The Phi-3 family’s combination of high-quality training data, advanced architecture, and optimization for various hardware platforms makes them a formidable choice for developers and researchers seeking robust AI solutions.

Technical Aspects

Here are the technical nuances of Phi-3:

Training and Development Process

The Phi-3 family of models, including Phi-3 Vision, was developed through rigorous training and enhancement to maximize performance and safety.

High-Quality Training Data and Reinforcement Learning from Human Feedback (RLHF)

The training data for Phi-3 models was meticulously curated from a combination of publicly available documents, high-quality educational data, and newly created synthetic data. The sources included:

  • Publicly available documents that were rigorously filtered for quality.
  • Selected high-quality image-text interleaved data.
  • Newly created synthetic, “textbook-like” data focused on teaching math, coding, common sense reasoning, and general knowledge.
  • High-quality chat format supervised data to reflect human preferences on instruct-following, truthfulness, honesty, and helpfulness.

The development process incorporated Reinforcement Learning from Human Feedback (RLHF) to further enhance the model’s performance. This approach involves:

  • Supervised fine-tuning with high-quality data.
  • Direct preference optimization to ensure precise instruction adherence.
  • Automated testing and evaluations across dozens of harm categories.
  • Manual red-teaming to identify and mitigate potential risks.

These steps ensure that the Microsoft Phi-3 models are robust, reliable, and capable of handling complex tasks while maintaining safety and ethical standards.

Optimization for Different Hardware and Platforms

Microsoft Phi-3 models have been optimized for various hardware and platforms to ensure broad applicability and efficiency. This optimization allows for smooth deployment and performance across various devices and environments.

The optimization process includes:

  • ONNX Runtime: Provides efficient inference on a variety of hardware platforms.
  • DirectML: Enhances performance on devices using DirectML.
  • NVIDIA GPUs: The models are optimized for inference on NVIDIA GPUs, ensuring high performance and scalability.
  • Intel Accelerators: Support for Intel accelerators allows for efficient processing on Intel hardware.

These optimizations make Phi-3 models versatile and capable of running efficiently in diverse environments, from mobile devices to large-scale web deployments. The models are also available as NVIDIA NIM inference microservices with a standard API interface, further facilitating deployment and integration.

Safety and Ethical Considerations

Safety and ethical considerations are paramount in developing and deploying Phi-3 models. Microsoft has implemented comprehensive measures to ensure that these models adhere to high responsibility and safety standards.

Microsoft’s Responsible AI Standards guide the development of Phi-3 models. These standards include:

  • Safety Measurement and Evaluation: Rigorous testing to identify and mitigate potential risks.
  • Red-Teaming: Specialized teams evaluate the models for potential vulnerabilities and biases.
  • Sensitive Use Review: Ensuring the models are suitable for various applications without causing harm.
  • Adherence to Security Guidance: Aligning with Microsoft’s best practices for security to ensure safe deployment and use.

Phi-3 models also undergo post-training improvements, including reinforcement learning from human feedback (RLHF), automated testing, and evaluations to enhance safety further. Microsoft’s technical papers detailed the approach to safety training and evaluations, providing transparency and clarity on the methodologies used.

Developers using Phi-3 models can leverage a suite of tools available in Azure AI to build safer and more trustworthy applications. These tools include:

  • Safety Classifiers: Pre-built classifiers to identify and mitigate harmful outputs.
  • Custom Solutions: Tools to develop custom safety solutions tailored to specific use cases.

Conclusion

In this article, we explored the Phi-3 family of AI models Microsoft developed, including Phi-3-mini, Phi-3-small, Phi-3-medium, and Phi-3-vision. These models offer high performance with varying parameters and context lengths optimized for tasks ranging from content authoring to multimodal applications. Performance benchmarks indicate that Phi-3 models outperform larger models in various tasks, showcasing their efficiency and accuracy. The models are developed using high-quality data and RLHF, optimized for diverse hardware platforms, and adhere to Microsoft’s Responsible AI standards for safety and ethical considerations.

The Microsoft Phi-3 models represent a significant advancement in AI, making high-performance AI accessible and efficient. Their multimodal capabilities, particularly in Phi-3-vision, open new possibilities for integrated text and image processing applications across various sectors. By balancing performance, safety, and accessibility, the Phi-3 family sets a new standard in AI, poised to drive innovation and shape the future of AI solutions.

I hope you find this article informative. If you have any feedback or queries, then comment below. For more articles like this, explore our blog section today!!

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NISHANT TIWARI
NISHANT TIWARI

Seasoned AI enthusiast with a deep passion for the ever-evolving world of artificial intelligence. With a sharp eye for detail and a knack for translating complex concepts into accessible language, we are at the forefront of AI updates for you. Having covered AI breakthroughs, new LLM model launches, and expert opinions, we deliver insightful and engaging content that keeps readers informed and intrigued. With a finger on the pulse of AI research and innovation, we bring a fresh perspective to the dynamic field, allowing readers to stay up-to-date on the latest developments.

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