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Reading list

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?
Reading Data Files into Python Different Variable Datatypes
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
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
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
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 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

Try GitHub Models: Test AI Models like GPT-4o and Llama 3.1 for free

Pankaj Singh
Pankaj Singh 06 Aug, 2024
7 min read

Introduction

Picture this: You’re working on a cool project: a new app, website, or game. You want a place where you can not only store your code but also collaborate with others, keep track of changes, and maybe even show off your work to potential employers or developers. That’s where GitHub comes in!

But it is not just limited to that.

Now, GitHub is democratizing access to advanced AI technology with the launch of GitHub Models. This initiative allows over 100 million developers on GitHub to test and experiment with industry-leading AI models, such as GPT-4o and Llama 3.1, without cost. This new feature positions every developer to become an AI engineer capable of directly leveraging state-of-the-art machine learning models within their development environment. In this article, we will talk about what it offers and how you can access GitHub Models.

Read on!

GitHub Models

Overview

  1. GitHub’s new initiative, GitHub Models, offers developers access to advanced AI models like GPT-4o and Llama 3.1 for free, transforming them into AI engineers.
  2. GitHub Models includes an interactive playground for testing and experimenting with various AI models, enhancing developers’ productivity and innovation.
  3. Integration with GitHub Codespaces allows seamless AI application prototyping with pre-configured environments, SDKs, and sample code.
  4. The initiative supports real-world applications, such as in Harvard’s CS50 course, while ensuring data privacy and security for all user prompts and outputs.
  5. GitHub Models is in limited public beta, inviting developers to sign up and explore its potential for advancing AI and software development.

Table of contents

Why You Need to Explore GitHub Models?

Thomas Dohmke, CEO of GitHub, emphasizes the vision behind this initiative: “We believe every developer can be an AI engineer with the right tools and training. GitHub Models makes it simple to experiment with and deploy AI models, bringing the power of generative AI to our users’ fingertips.”

GitHub’s latest offerings provide you access to cutting-edge AI models like Llama 3.1, GPT-4o, GPT-4o Mini, Phi-3, and more. These can be tested using a built-in playground, bringing AI-powered coding and seamless integration to your fingertips.

You can elevate your development process with advanced AI and groundbreaking solutions. You can also Boost productivity, explore AI experimentation, and embrace AI-driven development. With an array of developer tools and AI resources, GitHub empowers you to push the boundaries of software development like never before.

Trying the Newest Model on GitHub Marketplace

Let’s Try Out the Newest Model on GitHub Marketplace:

1. Exploring the GitHub Marketplace

Browse the GitHub Marketplace: Visit the GitHub Marketplace to explore a collection of top AI models with entitlements attached to your GitHub account.

GitHub Marketplace

2. Trying Out a New Model

Trying Out a New Model - GPT-4o mini

Select a Model: Choose a model to try, such as the GPT-4o mini, and if you can’t access it then, join the waiting list to get early pass.

GitHub Model waitlist

Now, for a further understanding of how this works, watch the following video:

Interact with the Model: Use the playground to pass in an initial user prompt and interact with the model.

Adjust Parameters: Change the system prompt and experiment with parameters like temperature to control the randomness of the model’s responses.

Now, you can easily build an AI tutor for students with a single initial user prompt, create lessons, plan, and more.

3. Testing Different Models

Switch Models: Test how different models handle the same scenario. For instance, switch to Meta Llama 3.1 instruct and evaluate its response.

4. Accessing Model Details

Navigate to Model Details Page: Find more information about the model on its details page. Review the README, Evaluation, and Transparency tabs to understand the model better.

5. Getting Started with Code

Codespace

Access Getting Started Instructions: Click the code button to access instructions.

Use GitHub Codespace: Create a pre-configured development environment using GitHub Codespace, which comes ready with SDKs and sample code.

6. Making API Calls

Utilize Model API Calls: Make API calls using the entitlements from your GitHub account. Since the Codespace knows your identity, you don’t need to add an API key or sign up for additional services.

7. Experimenting with Models

Update Samples: Update the streaming sample to use a different model, such as phi-3-mini-4k-instruct. Modify the prompt and continue experimenting.

8. Using Models from GitHub CLI

Leverage GitHub CLI: Use the GitHub CLI to call AI models. For example, create questions for first-year computer science students to check their Git knowledge.

Combine CLI Commands: Combine other CLI commands with GitHub CLI, like summarizing the last ten commits of a repository using Phi-3 mini instruct.

9. Building AI-powered Apps

Minimize Friction: GitHub Models helps minimize friction as you explore and experiment with AI models.

Join the waitlist for GitHub Models to start building AI-powered apps.

Following these steps, you can efficiently explore, test, and utilize AI models from the GitHub Marketplace to build and experiment with AI-powered applications. Once you get access to the GitHub model, start exploring it. Meanwhile, I will mention some features of the GitHub Model you must know about.

Also read: New to Git and GitHub? This Essential Beginners Guide is for you

Features of the GitHub Model

Here are the features of the GitHub model:

Empowering Developers with AI

GitHub Models

From its inception, GitHub has been a cornerstone for developers, fostering a collaborative environment for building, customizing, and deploying software. With the advent of GitHub Models, the platform extends its reach into AI, enabling developers to integrate machine learning models seamlessly into their projects.

The Model Playground

Model Playground

At the heart of GitHub Models is the interactive model playground. This feature allows users to explore a variety of AI models from leading providers such as Meta, Mistral, and Microsoft with just a few clicks. Whether you’re a student, hobbyist, or professional developer, the playground offers a space to test different prompts and model parameters, fostering a hands-on learning experience.

Testing and Comparison

Testing and comparison

Each AI model has unique capabilities, performance metrics, and costs. GitHub Models provides tools to test and compare these models, helping developers find the best fit for their specific needs. For instance, Mistral models are known for their low latency, while GPT-4o excels in building multimodal applications that require real-time audio, vision, and text processing.

Seamless Integration with Codespaces

GitHub Models goes beyond mere experimentation by integrating with Codespaces, GitHub’s cloud-based development environment. This integration allows developers to prototype AI applications without the typical setup hassles. Sample code for various languages and frameworks is readily available, making bringing AI-driven features into your projects easier.

Once a prototype is ready, transitioning to production is straightforward. GitHub Models supports deployment via Azure AI, offering enterprise-grade security, data privacy, and global availability across over 25 Azure regions.

Real-World Application

One notable application of GitHub Models is its inclusion in Harvard’s CS50 course, led by Professor David J. Malan. This incorporation will enable students to experiment with AI models more easily, providing a practical learning experience.

Also read: Introduction for Git and Github for Beginners

Conclusion

GitHub’s vision extends beyond just providing tools; it’s about creating a network of AI creators. With the rise of AI engineers, GitHub is poised to play a crucial role in the path towards artificial general intelligence (AGI). The platform’s emphasis on open-source collaboration and community-driven innovation is set to accelerate AI and software development breakthroughs.

As GitHub Models enters its limited public beta, developers worldwide are invited to sign up and start experimenting. This initiative is a step towards realizing AI’s potential in software development, making advanced AI tools accessible to a broader audience, and fostering a new generation of AI engineers.

For more information and to sign up for the beta, visit GitHub Models here.

Frequently Asked Questions

Q1. How do I get started with GitHub?

Ans. Sign up at GitHub, create a new repository, and explore GitHub’s documentation to learn about repositories, branches, commits, and pull requests.

Q2. How do I collaborate with others on a GitHub project?

Ans. Fork a repository, make your changes, and submit a pull request. Use branches for collaboration and issues to track tasks.

Q3. What are GitHub Actions, and how can they improve my workflow?

Ans. GitHub Actions automate your development workflow, allowing you to build, test, and deploy code automatically based on events like pushes or pull requests.

Q4. How do I secure my GitHub repository?

Ans. Enable two-factor authentication, set up branch protection rules, use encrypted secrets, review access permissions regularly, and use Dependabot for dependency security.

Q5. What is GitHub Copilot, and how can it assist me?

Ans. GitHub Copilot is an AI-powered code completion tool that suggests code as you type, helping you write code faster and explore new APIs. Install the Copilot extension in supported IDEs to start using it.

AI modelArtificial Intelligencedata scienceGithubGitHub CLIGitHub Command Line Interfacemachine learning
Pankaj Singh
Pankaj Singh 06 Aug, 2024

Hi, I am Pankaj Singh Negi - Senior Content Editor | Passionate about storytelling and crafting compelling narratives that transform ideas into impactful content. I love reading about technology revolutionizing our lifestyle.

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