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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

OpenAI’s o1-mini: A Game-Changing Model for STEM with Cost-Efficient Reasoning

Nitika Sharma
Nitika Sharma 16 Sep, 2024
4 min read

OpenAI introduces o1-mini, a cost-efficient reasoning model with a focus on STEM subjects. The model demonstrates impressive performance in math and coding, closely resembling its predecessor, OpenAI o1, on various evaluation benchmarks. OpenAI anticipates that o1-mini will serve as a swift and economical solution for applications demanding reasoning capabilities without extensive global knowledge.The launch of o1-mini is targeted at Tier 5 API users, offering an 80% cost reduction compared to OpenAI o1-preview. Let’s have a deeper look at the working of o1 Mini. 

Overview

  • OpenAI’s o1-mini is a cost-efficient STEM reasoning model, outperforming its peers.
  • Specialized training makes o1-mini an expert in STEM, excelling in math and coding.
  • Human evaluations showcase o1-mini’s strengths in reasoning, favoring it over GPT-4o.
  • Safety measures ensure o1-mini’s responsible use, with enhanced jailbreak robustness.
  • OpenAI’s innovation with o1-mini offers a reliable and transparent STEM tool.

Table of contents

o1-mini vs Other LLMs

LLMs are usually pre-trained on large text datasets. But here’s the catch; while they have this vast knowledge, it can sometimes be a bit of a burden. You see, all this information makes them a bit slow and expensive to use in real-world scenarios. 

What sets apart o1-mini from other LLMs is the fact that its trained for STEM. This specialized training makes o1-mini an expert in STEM-related tasks. The model is efficient and cost-effective, perfect for STEM applications. Its performance is impressive, especially in math and coding. O1-mini is optimized for speed and accuracy in STEM reasoning. It’s a valuable tool for researchers and educators. 

o1-mini excels in intelligence and reasoning benchmarks, outperforming o1-preview and o1, but struggles with non-STEM factual knowledge tasks.

o1-mini vs Other LLMs

Also Read: o1: OpenAI’s New Model That ‘Thinks’ Before Answering Tough Problems

GPT 4o vs o1 vs o1-mini 

The comparison of responses on a word reasoning question highlights the performance disparity. While GPT-4o struggled, o1-mini and o1-preview excelled, providing accurate answers. Notably, o1-mini’s speed was remarkable, answering approximately 3-5 times faster.

How to Use o1-mini?

How to use o1-mini?
  • ChatGPT Plus and Team Users: Access o1-mini from the model picker today, with weekly limits 50 messages.
  • ChatGPT Enterprise and Education Users: Access to both models begins next week.
  • Developers: API tier 5 users can experiment with these models today, but features like function calling and streaming aren’t available yet.
  • ChatGPT Free Users: o1-mini will soon be available to all free users.

o1-mini’s Stellar Performance: Math, Coding, and Beyond

The OpenAI o1-mini model has been put to the test in various competitions and benchmarks, and its performance is quite impressive. Let’s look at different components one by one: 

Math

In the high school AIME math competition, o1-mini scored 70.0%, which is on par with the more expensive o1 model (74.4%) and significantly better than o1-preview (44.6%). This score places o1-mini among the top 500 US high school students, a remarkable achievement.

Coding

Moving on to coding, o1-mini shines on the Codeforces competition website, achieving an Elo score of 1650. This score is competitive with o1 (1673) and surpasses o1-preview (1258). This places o1-mini in the 86th percentile of programmers who compete on the Codeforces platform. Additionally, o1-mini performs well on the HumanEval coding benchmark and high-school-level cybersecurity capture-the-flag challenges (CTFs), further solidifying its coding prowess.

o1 Coding

STEM

o1-mini has proven its mettle in various academic benchmarks that require strong reasoning skills. In benchmarks like GPQA (science) and MATH-500, o1-mini outperformed GPT-4o, showcasing its excellence in STEM-related tasks. However, when it comes to tasks that require a broader range of knowledge, such as MMLU, o1-mini may not perform as well as GPT-4o. This is because o1-mini is optimized for STEM reasoning and may lack the extensive world knowledge that GPT-4o possesses.

STEM

Human Preference Evaluation

Human raters actively compared o1-mini’s performance against GPT-4o on challenging prompts across various domains. The results showed a preference for o1-mini in reasoning-heavy domains, but GPT-4o took the lead in language-focused areas, highlighting the models’ strengths in different contexts.

Safety Component in o1-mini 

The safety and alignment of the o1-mini model are of utmost importance to ensure its responsible and ethical use. Here’s an explanation of the safety measures implemented:

  • Training Techniques: o1-mini’s training approach mirrors that of its predecessor, o1-preview, focusing on alignment and safety. This strategy ensures the model’s outputs align with human values and mitigate potential risks, a crucial aspect of its development.
  • Jailbreak Robustness: One of the key safety features of o1-mini is its enhanced jailbreak robustness. On an internal version of the StrongREJECT dataset, o1-mini demonstrates a 59% higher jailbreak robustness compared to GPT-4o. Jailbreak robustness refers to the model’s ability to resist attempts to manipulate or misuse its outputs, ensuring that it remains aligned with its intended purpose.
  • Safety Assessments: Before deploying o1-mini, a thorough safety assessment was conducted. This assessment followed the same approach used for o1-preview, which included preparedness measures, external red-teaming, and comprehensive safety evaluations. External red-teaming involves engaging independent experts to identify potential vulnerabilities and security risks.
  • Detailed Results: The results of these safety evaluations are published in the accompanying system card. This transparency allows users and researchers to understand the model’s safety measures and make informed decisions about its usage. The system card provides insights into the model’s performance, limitations, and potential risks, ensuring responsible deployment and usage.

End Note

OpenAI’s o1-mini is a game-changer for STEM applications, offering cost-efficiency and impressive performance. Its specialized training enhances reasoning abilities, particularly in math and coding. With robust safety measures, o1-mini excels in STEM benchmarks, providing a reliable and transparent tool for researchers and educators.

Stay tuned to Analytics Vidhya blog to know more about the uses of o1 mini!

OpenAIOpenAI o1OpenAI's o1-mini
Nitika Sharma
Nitika Sharma 16 Sep, 2024

Hello, I am Nitika, a tech-savvy Content Creator and Marketer. Creativity and learning new things come naturally to me. I have expertise in creating result-driven content strategies. I am well versed in SEO Management, Keyword Operations, Web Content Writing, Communication, Content Strategy, Editing, and Writing.

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