vllama 0.7.0

CLI tool to run local vision models like stabilityai/sd-turbo

Vllama: Vision Models Made Easy 🚀

Vllama is a comprehensive CLI tool that simplifies working with vision models and machine learning workflows. Whether you're preprocessing datasets, training models with AutoML, or generating images with state-of-the-art diffusion models, Vllama makes it easy - locally or on cloud GPUs.

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✨ Key Features

• 🔧 Autonomous Data Preprocessing: Intelligent data cleaning, encoding, scaling, and feature selection
• 🤖 AutoML Training: Train and compare multiple ML models automatically with hyperparameter tuning
• 🎨 Vision Model Inference: Generate images using pre-trained diffusion models (Stable Diffusion, SD-Turbo)
• ☁️ Cloud GPU Integration: Seamlessly offload computation to Kaggle GPUs
• 📊 Rich Visualizations: Automatic generation of insights, correlations, and performance metrics
• 💾 Smart Output Management: Organized folder structure with logs, models, and visualizations

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📦 Installation

1. Clone the Repository

git clone https://github.com/ManvithGopu13/Vllama.git
cd Vllama

2. Install Dependencies

pip install -r requirements.txt

3. Install Vllama CLI

pip install -e .

Now you can use vllama from anywhere in your terminal!

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🚀 Quick Start Guide

Data Preprocessing & Model Training Workflow

Step 1: Preprocess Your Dataset

Clean and prepare your data for machine learning:

vllama data --path dataset.csv --target price --test_size 0.2 --output_dir ./outputs

What it does:

• Automatically detects column types (numerical/categorical)
• Handles missing values intelligently (KNN imputation, median/mode filling)
• Removes duplicates and handles outliers
• Encodes categorical variables (label encoding, one-hot encoding, frequency encoding)
• Scales features using RobustScaler
• Performs feature selection (removes zero-variance and highly correlated features)
• Generates visualizations (missing values heatmap, correlation matrix, etc.)
• Splits data into train/test sets
• Saves processed data as train_data.csv and test_data.csv

Parameters:

• --path: Path to your dataset (supports CSV, Excel, JSON, Parquet)
• --target: Target column name (auto-detected if not specified)
• --test_size or -t: Test set proportion (default: 0.2)
• --output_dir or -o: Output directory (default: current directory)

Output Structure:

output_folder_YYYYMMDD_HHMMSS/
├── train_data.csv
├── test_data.csv
├── processed_full_data.csv
├── preprocessing_log.json
├── preprocessing_log.txt
├── summary_report.json
├── transformation_metadata.json
└── visualizations/
    ├── 01_missing_initial.png
    ├── 02_dtypes.png
    ├── 03_corr_processed.png
    ├── 04_target_processed.png
    └── 05_mi.png

Step 2: Train Models with AutoML

Automatically train and compare multiple ML models:

vllama train --path ./outputs/output_folder_YYYYMMDD_HHMMSS --target price

What it does:

• Auto-detects task type (classification or regression)
• Trains multiple models with hyperparameter tuning:
  • Classification: Logistic Regression, Random Forest, XGBoost, LightGBM, CatBoost, SVM, KNN, MLP, Naive Bayes
  • Regression: Random Forest, XGBoost, LightGBM, CatBoost, SVR, KNN, MLP
• Uses RandomizedSearchCV for efficient hyperparameter optimization
• Evaluates models on test set with comprehensive metrics
• Generates visualizations (confusion matrices, ROC curves, prediction plots)
• Saves all models and creates a leaderboard
• Identifies and saves the best performing model

Parameters:

• --path or -p: Path to folder containing train_data.csv and test_data.csv
• --target or -t: Target column name

Output Structure:

results/
├── model_summary.csv          # Leaderboard of all models
├── best_model.pkl             # Best performing model
├── best_model.txt             # Best model details
├── report.html                # HTML report with all results
└── per_model/
    ├── RandomForest/
    │   ├── RandomForest_best_model.pkl
    │   ├── RandomForest_tuning_results.csv
    │   ├── RandomForest_confusion_matrix.png
    │   └── RandomForest_roc_curve.png
    ├── XGBoost/
    └── ...

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Vision Model Inference Workflow

Step 1: Show Available Models

vllama show models

Lists all supported vision models with descriptions.

Step 2: Install a Model (Optional)

Pre-download model weights to cache:

vllama install stabilityai/sd-turbo

Step 3: Generate Images Locally

Single Prompt Mode:

vllama run stabilityai/sd-turbo --prompt "A serene mountain landscape at sunset" --output_dir ./images

Interactive Mode:

vllama run stabilityai/sd-turbo

Then enter prompts interactively. Type exit or quit to stop.

Parameters:

• model: Model name (e.g., stabilityai/sd-turbo)
• --prompt or -p: Text prompt for image generation
• --output_dir or -o: Directory to save generated images (default: current directory)
• --service or -s: Offload to cloud service (e.g., kaggle)

Features:

• Automatic GPU/CPU detection
• Low VRAM optimization (for GPUs with ≤3GB VRAM)
• Memory-efficient attention (xformers)
• Attention slicing and VAE tiling for better performance

Step 4: Generate Images on Kaggle GPU

vllama run stabilityai/sd-turbo --service kaggle --prompt "A cyberpunk city at night"

What it does:

• Creates a Kaggle kernel with GPU enabled
• Installs dependencies automatically
• Runs the model on Kaggle's GPU
• Downloads the generated image to your local machine

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📚 Complete Command Reference

Data & ML Commands

vllama data

Autonomous data preprocessing and cleaning.

vllama data --path <dataset> --target <column> [--test_size <float>] [--output_dir <dir>]

Examples:

# Basic usage with auto-detected target
vllama data --path sales_data.csv

# Specify target column and test size
vllama data --path housing.csv --target price --test_size 0.25

# Custom output directory
vllama data --path data.csv --target label -t 0.3 -o ./processed_data

vllama train

AutoML model training with hyperparameter tuning.

vllama train --path <data_folder> --target <column>

Examples:

# Train on preprocessed data
vllama train --path ./output_folder_20231124_143022 --target SalePrice

# Short form
vllama train -p ./data -t label

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Vision Model Commands

vllama show models

List all supported vision models.

vllama show models

vllama install

Download and cache a model.

vllama install <model_name>

Example:

vllama install stabilityai/sd-turbo

vllama run

Run a vision model for image generation.

vllama run <model_name> [--prompt <text>] [--service <service>] [--output_dir <dir>]

Examples:

# Single prompt
vllama run stabilityai/sd-turbo --prompt "A beautiful sunset"

# Interactive mode
vllama run stabilityai/sd-turbo

# Run on Kaggle GPU
vllama run stabilityai/sd-turbo --service kaggle --prompt "A dragon flying"

# Custom output directory
vllama run stabilityai/sd-turbo -p "A forest" -o ./my_images

vllama post

Send a prompt to an already running model session.

vllama post <prompt> [--output_dir <dir>]

Example:

vllama post "A magical castle" --output_dir ./outputs

vllama stop

Stop the currently running model session.

vllama stop

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Cloud Integration Commands

vllama login

Authenticate with a cloud GPU service.

vllama login --service <service> [--username <user>] [--key <api_key>]

Examples:

# Login to Kaggle with credentials
vllama login --service kaggle --username myusername --key abc123xyz

# Use existing Kaggle credentials from ~/.kaggle/kaggle.json
vllama login --service kaggle

vllama init gpu

Initialize a GPU session on a cloud service.

vllama init gpu --service <service>

Example:

vllama init gpu --service kaggle

vllama logout

Remove cloud service credentials.

vllama logout

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🎯 Common Workflows

Workflow 1: Complete ML Pipeline

# 1. Preprocess data
vllama data --path raw_data.csv --target price

# 2. Train models (use the output folder from step 1)
vllama train --path ./output_folder_20231124_143022 --target price

# 3. Review results in the results/ folder

Workflow 2: Local Image Generation

# 1. Install model (optional, first-time only)
vllama install stabilityai/sd-turbo

# 2. Generate images interactively
vllama run stabilityai/sd-turbo

# Enter prompts:
# Prompt> A serene lake with mountains
# Prompt> A futuristic city
# Prompt> exit

Workflow 3: Cloud GPU Image Generation

# 1. Login to Kaggle
vllama login --service kaggle --username myuser --key myapikey

# 2. Generate image on Kaggle GPU
vllama run stabilityai/sd-turbo --service kaggle --prompt "A magical forest"

# Image will be downloaded automatically

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📊 Understanding Outputs

Data Preprocessing Outputs

Logs:

• preprocessing_log.json: Detailed JSON log of all preprocessing steps
• preprocessing_log.txt: Human-readable text log
• summary_report.json: Summary statistics and metadata

Data Files:

• train_data.csv: Training dataset (80% by default)
• test_data.csv: Testing dataset (20% by default)
• processed_full_data.csv: Complete processed dataset
• transformation_metadata.json: Encoders and scalers metadata for future use

Visualizations:

• Missing values heatmap
• Data types distribution
• Correlation matrix (top 20 features)
• Target distribution
• Mutual information scores

Model Training Outputs

Model Files:

• best_model.pkl: Best performing model (can be loaded with joblib)
• model_summary.csv: Comparison of all trained models
• report.html: Interactive HTML report

Per-Model Outputs:

• {model}_best_model.pkl: Saved model
• {model}_tuning_results.csv: Hyperparameter search results
• {model}_confusion_matrix.png: Confusion matrix (classification)
• {model}_roc_curve.png: ROC curve (binary classification)
• {model}_pred_vs_true.png: Scatter plot (regression)

Vision Model Outputs

Generated images are saved as:

vllama_output_{timestamp}.png          # Local generation
vllama_kaggle_{timestamp}.png          # Kaggle generation

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🔧 Advanced Configuration

Environment Variables

Create a .env file for configuration:

# Kaggle API Credentials
KAGGLE_USERNAME=your_username
KAGGLE_KEY=your_api_key

# Model Cache Directory (optional)
HF_HOME=/path/to/cache

GPU Optimization

Vllama automatically optimizes for your GPU:

• High VRAM (>3GB): Uses float16, full resolution (512x512), more inference steps
• Low VRAM (≤3GB): Uses float32, reduced steps, memory-efficient attention
• CPU: Falls back to CPU inference (slower but works)

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🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

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📄 License

This project is licensed under the GNU General Public License v3.0.

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🆘 Troubleshooting

Common Issues

Issue: "Kaggle API credentials not found"

# Solution: Set up Kaggle credentials
vllama login --service kaggle --username YOUR_USERNAME --key YOUR_API_KEY

Issue: "CUDA out of memory"

# Solution: The tool automatically handles low VRAM, but you can also:
# 1. Close other GPU applications
# 2. Use CPU mode (automatic fallback)
# 3. Use Kaggle GPU instead
vllama run model --service kaggle --prompt "your prompt"

Issue: "Target column not found"

# Solution: Specify the target column explicitly
vllama data --path data.csv --target your_column_name

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📞 Support

• Documentation: GitHub Repository
• Issues: GitHub Issues
• Email: [e-mail]

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🌟 Acknowledgments

Built with:

• PyTorch
• Hugging Face Diffusers
• Scikit-learn
• XGBoost, LightGBM, CatBoost
• Kaggle API

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Made with ❤️ by Gopu Manvith