Interview Prep
Career
GenAI
Prompt Engg
ChatGPT
LLM
Langchain
RAG
AI Agents
Machine Learning
Deep Learning
GenAI Tools
LLMOps
Python
NLP
SQL
AIML Projects

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

Top 6 LLMs for Coding

Aayush1
Aayush1 29 Jul, 2024
5 min read

Introduction

Coding is changing fast, and Large language models are a big part of that change. These LLMs help programmers in many ways, from finishing lines of code to finding bugs and even writing whole functions based on simple descriptions. As more companies and organizations invest in this technology, the options available to developers continue to grow.

In this article, we’ll look at the top 6 Large language models popular among coders.

Mistral Next: The Latest Language Model from Mistral AI

Table of contents

GPT 4

GPT-4 is a significant leap forward in the world of large language models (LLMs) and has proven to be an invaluable tool for developers. Its ability to understand and generate human-quality text, including code, has revolutionized the way programmers approach their tasks.

llm for coding

Key Capabilities for Coding

  • Code Generation: GPT-4 can generate code from natural language prompts, saving developers time and effort. For instance, you could describe a desired function or algorithm, and GPT-4 can produce the corresponding code in various programming languages.
  • Code Completion: The model can suggest code completions as you type, acting as a powerful auto-completion tool. This accelerates development and reduces errors.
  • Code Explanation: GPT-4 can explain complex code snippets or entire functions, making it easier to understand existing codebases and debug issues.
  • Code Refactoring: It can help improve code readability, efficiency, and maintainability by suggesting refactoring options.
  • Debugging Assistance: By analyzing code and error messages, GPT-4 can identify potential issues and suggest solutions, streamlining the debugging process.
  • Learning and Adaptability: GPT-4 is constantly learning and improving, making it increasingly adept at handling various coding challenges and adapting to new programming paradigms.

Mistral Codestral

Mistral Codestral is a specialized version of the Mistral language models, tailored specifically for coding tasks. Developed to enhance productivity and efficiency in software development, Codestral combines advanced language understanding with coding-specific features to assist developers in various programming activities.

mistral for coding

Key Features and Strengths

  • Efficient Code Generation: Generates high-quality code snippets quickly and accurately across multiple programming languages.
  • Multi-language Support: Supports a wide range of programming languages, including Python, JavaScript, Java, and C++.
  • Real-time Code Assistance: Provides real-time code suggestions and error detection to catch mistakes early and improve code quality.
  • Integration with Development Environments: Seamlessly integrates with popular IDEs and code editors like Visual Studio Code, IntelliJ IDEA, and PyCharm.
  • Collaborative Coding Support: Optimized for collaborative coding with features like version control integration and team collaboration tools.
  • Adaptability and Customization: Offers customization options to tailor suggestions and behavior to fit specific project needs and coding styles.

Claude 3.5

Claude 3.5, developed by Anthropic, is a state-of-the-art Large Language Model that excels in natural language understanding and coding tasks. It is designed to prioritize safety, ethical use, and alignment, making it an ideal choice for developers seeking a reliable and responsible AI partner.

large language model for coding

Claude 3.5 Key Features

  • Ethical and Safe AI: Focuses on responsible use, minimizing harmful or biased outputs, and aligning with user intentions.
  • Advanced Code Understanding: Maintains context and performs semantic analysis, providing accurate and meaningful code suggestions.
  • Code Generation and Completion: Supports multiple languages, offering context-aware code completions and intelligent snippets.
  • Debugging and Problem-Solving: Identifies and corrects errors, and tackles complex coding challenges with strong reasoning capabilities.
  • Collaborative Coding: Provides real-time assistance and integrates with various development tools for enhanced teamwork.
  • Learning and Adaptability: Continuously updated, customizable to specific needs, and stays current with the latest programming trends.

Llama 3.1

Llama 3.1 is a large language model (LLM) developed by Meta AI, specifically designed to excel at various tasks, including coding. It’s part of Meta’s commitment to open-source AI, making it accessible to developers worldwide.

llama 3.1 for coding

Key Features for Coding

  • Code Generation: Llama 3.1 can generate code snippets, functions, or even entire programs based on given prompts or requirements. This can significantly boost developer productivity and help explore different solutions.
  • Code Explanation: It can explain existing code, breaking down complex logic into simpler terms. This is invaluable for understanding legacy code or learning new programming concepts.
  • Code Debugging: The model can help identify errors in code and suggest potential fixes. This can save developers time and effort in troubleshooting.
  • Code Optimization: Llama 3.1 can analyze code and suggest improvements for efficiency, performance, or readability.
  • Code Translation: It can translate code from one programming language to another, facilitating collaboration and knowledge sharing across different language ecosystems.

Mistral NEMO

Mistral NEMO is a powerful 12-billion parameter language model specifically designed to excel in coding tasks. Developed in collaboration with NVIDIA, it offers impressive capabilities for generating, explaining, and improving code.

Key Features and Benefits

  • State-of-the-art coding abilities: Mistral NEMO demonstrates exceptional performance in various coding benchmarks, making it a valuable tool for developers of all levels.
  • Large context window: With a context length of up to 128k tokens, it can process and generate longer code snippets, improving its ability to understand and generate complex code structures.
  • Multilingual support: Mistral NEMO excels in multiple languages, making it a versatile tool for developers working with different codebases.
  • Efficient tokenization: The model uses a specialized tokenizer called Tekken, which significantly improves code compression compared to previous models.
  • Optimized for inference: It’s packaged as an NVIDIA NIM inference microservice, ensuring fast and efficient deployment on various platforms

Gemini 1.5

Gemini 3.1 is a powerful tool for coding, offering advanced code understanding, contextual awareness, and integration with development environments. Its support for multiple languages, refactoring capabilities, debugging assistance, and adaptive learning make it a valuable asset for both individual developers and teams

llm for coding

Key Features of Gemini 3.1 for Coding

  • Advanced Code Understanding and Generation: Analyzes and generates code across various programming languages. Maintains context throughout coding tasks.
  • Integration with Development Environments: Seamlessly integrates with popular IDEs and code editors. Enhances productivity with in-editor code suggestions, autocomplete features, and error detection.
  • Code Refactoring and Optimization: Suggests improvements for code structure and performance. Helps maintain clean, efficient code by offering refactoring and optimization tips.
  • Learning and Adaptation: Adapts to specific coding styles and preferences over time. Provides increasingly tailored suggestions based on your coding patterns and preferences.
  • Support for Code Documentation: Assists in generating and maintaining code documentation. Automatically creates documentation from code comments and structure, keeping it accurate and up-to-date.vides increasingly tailored suggestions based on your coding patterns and preferences.

Conclusion

In conclusion, the evolution of large language models (LLMs) has brought transformative changes to the coding landscape. Each model discussed—GPT-4, Mistral Codestral, Claude 3.5, Llama 3.1, Mistral NEMO, and Gemini 1.5—offers unique strengths that cater to different aspects of software development. From generating and completing code to debugging and refactoring, these LLMs enhance productivity and streamline workflows. As technology continues to advance, the integration of these tools into development environments will likely become even more seamless, further revolutionizing the way programmers approach their work. Staying updated with these advancements can provide developers with the edge needed to excel in an increasingly competitive field.

AI for codinglarge language for codingllms for coding
Aayush1
Aayush1 29 Jul, 2024

Data Analyst with over 2 years of experience in leveraging data insights to drive informed decisions. Passionate about solving complex problems and exploring new trends in analytics. When not diving deep into data, I enjoy playing chess, singing, and writing shayari.

Free Courses

image.name 4.7

Generative AI - A Way of Life

Explore Generative AI for beginners: create text and images, use top AI tools, learn practical skills, and ethics.
image.name 4.5

Getting Started with Large Language Models

Master Large Language Models (LLMs) with this course, offering clear guidance in NLP and model training made simple.
image.name 4.6

Building LLM Applications using Prompt Engineering

This free course guides you on building LLM apps, mastering prompt engineering, and developing chatbots with enterprise data.
image.name 4.8

Building Your first RAG System using LlamaIndex

Build your first RAG model with LlamaIndex in this free course. Dive into Retrieval-Augmented Generation now!
image.name 4.7

Building Production Ready RAG systems using LlamaIndex

Learn Retrieval-Augmented Generation (RAG): learn how it works, the RAG framework, and use LlamaIndex for advanced systems.

Recommended Articles

Frequently Asked Questions

Lorem ipsum dolor sit amet, consectetur adipiscing elit,

Write for us

Write, captivate, and earn accolades and rewards for your work
  • Reach a Global Audience
  • Get Expert Feedback
  • Build Your Brand & Audience
  • Cash In on Your Knowledge
  • Join a Thriving Community
  • Level Up Your Data Science Game
imag
Rahul Shah img

Rahul Shah

27
Sion Chakrabarti img

Sion Chakrabarti

16
CHIRAG GOYAL img

CHIRAG GOYAL

87
kaustubh gupta img

kaustubh gupta

24
Barney Darlington img

Barney Darlington

5
Suvojit Hore img

Suvojit Hore

9
Arnab Mondal img

Arnab Mondal

15