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Reading list

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?
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
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

How to use AutoGen Studio Locally using LM Studio or Text-Generation-Web-UI

Divya K
Divya K Last Updated : 14 Mar, 2024
10 min read

Introduction

AutoGen Studio, powered by the AutoGen framework, is a revolutionary platform for developing AI agents that seamlessly collaborate to accomplish tasks. In this blog post, we will explore the capabilities of AutoGen Studio and integrate it with free alternatives of OpenAI like Text Generation Web UI and LM Studio. By the end of this guide, you’ll have a comprehensive understanding of how to locally use AutoGen Studio with alternative language model tools.

Learning Objectives

  • Understand the fundamentals of AutoGen and AutoGen Studio.
  • Learn the installation process of AutoGen Studio.
  • Integrate AutoGen Studio with Text Generation Web UI for text generation tasks.
  • Explore the utilization of AutoGen Studio with LM Studio for language model interactions.

This article was published as a part of the Data Science Blogathon.

Table of contents

What is AutoGen?

AutoGen is a framework designed for developing applications that utilize Language Model (LLM) agents capable of collaborating to solve tasks. These agents, which are both customizable and conversational, seamlessly integrate human participation and operate in various modes, employing combinations of LLMs, human inputs, and tools.

AutoGen Studio serves as an AI application, featuring a user interface powered by the AutoGen framework. Its primary objective is to streamline the prototyping process of AI agents, empowering users to augment these agents with various skills, structure them into coherent workflows, and engage in interactive task completion.

What is the purpose AutoGen Studio?

Enhancing User Interaction

AutoGen Studio’s intuitive interface enables users to declaratively define and modify agents and multi-agent workflows effortlessly. The need to delve into complex code or intricate configurations is eliminated. Instead, users can leverage a point-and-click, drag-and-drop interface to specify the parameters of agents and their interactions. It’s akin to providing a digital canvas where users design the choreography, and AutoGen Studio ensures that the agents seamlessly follow their instructions.

Realizing Agent Potential: Adding Skills with Ease

Consider a scenario where a virtual team needs to acquire new skills to address evolving tasks. In a traditional setting, integrating these skills might involve navigating through intricate lines of code. However, AutoGen Studio simplifies the process of explicitly adding skills to agents. The interface provides a clear view of existing skills and offers an accessible platform for incorporating new ones. It’s like empowering a team with new tools, achieved with just a few clicks.

Making Collaboration Tangible: The Playground Section

Moving beyond the theoretical setup, AutoGen Studio introduces a Playground section where users can interact with the agent workflows defined earlier. This virtual space is where the magic happens – a digital environment where users engage in sessions, observe chat interactions, and witness the results of their orchestrated collaboration. It’s like having a virtual rehearsal room where users fine-tune the performance of their digital ensemble.

Sharing Success: The Gallery Section

Once users have perfected their agent choreography and witnessed successful sessions, AutoGen Studio provides a platform to share achievements through the Gallery section. This section serves as a repository of successful agent collaborations, similar to showcasing a digital masterpiece to a wider audience. It fosters collaboration and inspiration within the AutoGen community.

To learn more about Autogen Studio refer this blog

Step-by-Step Guide to Implement AutoGen Studio with Text-Generation-Web UI

Text Generation Web UI is a user-friendly web-based interface designed for creating text using various large language models, including transformers, GPTQ, llama.cpp, and others. It offers an array of features, such as model switching, notebook mode, chat mode, and more, making it versatile for applications like creative writing and chatbot development.

Step1: Installation Process

To run a large language model locally on your computer, follow these steps:

  • Clone the repository using the command:
git clone  https://github.com/oobabooga/text-generation-webui
  •  Navigate into the “text generation web UI” folder.
cd text-generation-webui
  • Choose the appropriate start script based on your operating system. 

Example: If your using Windows OS, run the following command:

.\\start_windows.bat --api --listen-port 7822 --extensions openai
  • Linux: ./start_linux.sh
  • Windows: start_windows.bat
  • macOS: ./start_macos.sh
  • WSL: ./start_wsl.bat

Press enter. This will automatically download and install the required packages for text generation web UI. The installation might take around a minute.

Step2: Selecting GPU

  • During installation, it will prompt you to select your GPU. Choose the appropriate GPU, for example, ‘N’ in the case if you don’t have GPU and want to run the model in CPU mode, and press enter.
  • The script will install the necessary packages according to the specified GPU.

Step3: Copy OpenAI-compatible API-URL

Copy the generated OpenAI-compatible URL. You will need this in a later step.

Step4: Opening Web UI

  • Navigate to the provided Local URL mentioned in the output.
  • Open your browser and enter the URL. The web interface should appear.

Step5: Downloading Models

  • In the interface, go to the “models” tab.
  • Download the desired model.
    • Enter the hugging face username/model path in the first box. Example: TheBloke/Mistral-7B-Instruct-v0.2-GGUF
    • To download a single file( for GGUF models ), enter its name in the second box. Example: In hugging face this the model i choose to work TheBloke/Mistral-7B-Instruct-v0.2-GGUF. if you navigate to the provided files section, you will see the different models of TheBloke/Mistral-7B-Instruct-v0.2-GGUF. I choose mistral-7b-instruct-v0.2.Q4_K_M.gguf

Monitor the terminal to confirm the successful download of the model. It may take a minute to complete.

Step6: Refreshing Interface

  • After downloading, refresh the web interface by clicking the refresh🔃 button.
  • The downloaded model should now be listed in the drop-down menu.

Step7: Choosing the Model

  • Choose the desired model from the drop-down list.
  • Click the “load” button to load the selected model.

Step8: Model Loaded

  • Upon successful loading, a confirmation message will appear, indicating that the model has been successfully loaded.
  • Then click the “save settings” , now we can see the settings got saved

Step9: Running Autogen Studio

Open new terminal and execute following commands

The next step is to install AutoGen Studio. For a smooth experience, I recommend using a virtual environment (e.g., conda) to avoid conflicts with existing Python packages. With Python 3.10 or newer active in your virtual environment, use the following pip command:

pip install autogenstudio
autogenstudio ui --port 8080

output server_autogenstudio.log

INFO:     Started server process [11050]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8080 (Press CTRL+C to quit)

Step10: Setting up an LLM Provider in Autogen UI

  • In the AutoGen Studio UI, navigate to the top and locate the “Build” section. Click on it, then choose “Model”
  • Click on “New Model” to insert a new model. Enter the Model Name, base URL (paste the OpenAI-compatible API URL that you copied.) E.g. http://0.0.0.0:5000/v1) and set the API key as “NoApi (type any string, don’t leave it empty).
  • Once done, click “OK.”

Step11: Setting the workflow

Configure an agent workflow that can be used to handle tasks.

  • Navigate to workflow section then click General Agent Workflow
  • Click the user_proxy agent and Click on “Add” under the model section and choose the model you want to use from the dropdown.
  • Follow the same steps for the primary_assistant. Ensure that the name of your chosen model is displayed next to the model section in green color. Finally, click “OK” again to confirm.
image.png

Example usage

Click playground section and create new section by clicking the “new” button

Choose the general agent workflow and press Create button

When the session is successfully created. Now execute the query and press the send btton

Now consider this query: List out the top 5 rivers in Africa and their length and return that as a markdown table. Do not try to write any code, just write the table.

Text generation web UI Log

To verify LLM starts generating output, After a few minutes you will be able to see the log in Text-generation-webui that the tokens are getting generated. This proves that autogen UI is using LLM through Text-generation-webui.

web UI

Autogen Studio Log in Terminal

Autogen Studio

Result

Step-by-Step Guide to Implement AutoGen Studio with LM Studio

LM Studio, a groundbreaking project, stands at the forefront of enabling users to interact with open-source LLMs seamlessly. Developed around the llama.cpp library, this tool facilitates the installation, management, and utilization of various LLMs on desktop environments. 

Step1: Download LM Studio

Navigate to the official LM Studio website (https://lmstudio.ai/) and choose the version compatible with your operating system (Windows or Mac or Linux).Click on the download link to initiate the download process.

Step2: Search and Choose a Model

In the middle of the main screen, locate the search bar. Enter keywords or a specific model name to explore available options. Choose a model that aligns with your exploration goals.

Step3: Start Local Server

Local Server – Locate the double-arrow icon on the left, click it, and start the local server. 
Copy the base url and paste it in autogen studio.

Step4: Running autogen studio

The next step is to install AutoGen Studio. For a smooth experience, I recommend using a virtual environment (e.g., conda) to avoid conflicts with existing Python packages. With Python 3.10 or newer active in your virtual environment, use the following pip command:

pip install autogenstudio
autogenstudio ui --port 8080

output server_autogenstudio.log

INFO:     Started server process [11050]
INFO:     Waiting for application startup.
INFO:     Application startup complete.
INFO:     Uvicorn running on http://127.0.0.1:8080 (Press CTRL+C to quit)

Step5: Setting up an LLM Provider in Autogen UI

  • In the AutoGen Studio UI, navigate to the top and locate the “Build” section. Click on it, then choose “Model”
  • Click on “New Model” to insert a new model. Enter the Model Name, base URL (paste the OpenAI-compatible API URL that you copied.) E.g. http://0.0.0.0:5000/v1) and set the API key as “NoApi (type any string, don’t leave it empty).
  • Once done, click “OK.”
Autogen UI

Step6: Setting the workflow

Configure an agent workflow that can be used to handle tasks.

  • Navigate to workflow section then click General Agent Workflow
  • Click the user_proxy agent and Click on “Add” under the model section and choose the model you want to use from the dropdown.

Important Tip: Also Set Human Input Mode as “ALWAYS”( otherwise you won’t get an output in autogen studio)

  • Follow the same steps for the primary_assistant. Ensure that the name of your chosen model is displayed next to the model section in green color. Finally, click “OK” again to confirm.
workflow

Example usage

Now consider this query: List out the top 5 rivers in Africa and their length and return that as a markdown table. Do not try to write any code, just write the table.

Click playground section and create new section by clicking the “new” button

Choose the general agent workflow and press Create button

When the session is successfully created. Now execute the query and press the send button.

workflow

Important: Once you give any task in autogen studio in playground section, go to the autogen studio terminal and there you will see this “Provide feedback to userproxy. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation:”. now press “Enter”.

LM Studio Logs

To verify LLM starts generating output, you will be able to see the log in LM studio that the tokens are getting generated. This proves that autogen UI is using LLM through LM Studio.

AutoGen Studio Log

Result

Conclusion

In conclusion, AutoGen Studio emerges as a powerful tool for developing and interacting with AI agents. By combining it with free alternatives like Text Generation Web UI and LM Studio, users can extend the functionalities and explore various language models locally. This guide equips you with the knowledge to harness the potential of AutoGen Studio alongside alternative tools, opening doors to innovative AI applications.

Key Takeaways

  • AutoGen Studio simplifies the development of AI agents through an intuitive user interface.
  • The Playground and Gallery sections in AutoGen Studio provide virtual spaces for interaction and collaboration.
  • Installing and setting up Text Generation Web UI allows local text generation using various language models.
  • LM Studio enables the seamless integration of open-source language models for diverse applications.
  • The integration of AutoGen Studio with alternative tools offers flexibility and expands the capabilities of AI development.

Frequently Asked Questions

Q1. Why should I choose AutoGen Studio for AI development?

A. AutoGen Studio stands out for its user-friendly interface, allowing you to effortlessly prototype and improve AI agents. Its intuitive design eliminates the need for complex coding, making it accessible for both beginners and seasoned developers.

Q2. How challenging is the installation process for AutoGen Studio?

A. Installing AutoGen Studio is a breeze. Simply use the recommended pip command within a virtual environment, ensuring a smooth experience. The process is well-documented, making it accessible for users with varying levels of technical expertise.

Q3. Can I integrate Text Generation Web UI with AutoGen Studio for diverse applications?

A. Absolutely! By following the outlined steps, you can seamlessly integrate Text Generation Web UI with AutoGen Studio. This combination opens up a world of possibilities for tasks ranging from creative writing to chatbot development.

Q4. What distinguishes LM Studio, and how does it complement AutoGen Studio?

A. LM Studio is a project that simplifies the interaction with open-source language models. When integrated with AutoGen Studio.

The media shown in this article is not owned by Analytics Vidhya and is used at the Author’s discretion.

blogathonlarge language modelLLMtext generation
Divya K
Divya K

Hello! I'm Divya, "I have a deep passion for constantly expanding my knowledge and skills in the ever-evolving world of technology. Also love to share my knowledge and insights with others, fostering a culture of continuous learning and growth.

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