ProjectManagementLLM

A specialized Large Language Model optimized for project management tasks, featuring Microsoft Olive AI integration for efficient model deployment and optimization.

🎯 Overview

ProjectManagementLLM is designed to assist with various project management activities including:

Task Planning & Breakdown: Intelligent decomposition of complex projects into manageable tasks
Resource Allocation: Optimal distribution of resources across project phases
Risk Assessment: Identification and mitigation strategies for project risks
Timeline Estimation: Data-driven project scheduling and milestone planning
Team Coordination: Communication templates and collaboration frameworks
Progress Tracking: Automated status reporting and performance metrics

🚀 Key Features

Olive AI Integration: Optimized for deployment using Microsoft Olive AI framework
ONNX Compatibility: Efficient inference with ONNX Runtime
Quantization Support: Reduced model size with maintained accuracy
Flexible Deployment: CPU and GPU execution providers
Customizable Workflows: Configurable optimization pipelines

📋 Model Specifications

Base Architecture: Transformer-based language model
Optimization Framework: Microsoft Olive AI
Supported Formats: ONNX, PyTorch
Quantization: Static quantization with uint8 precision
Execution Providers: CPU, GPU (configurable)

🛠️ Installation & Setup

Prerequisites

pip install olive-ai
pip install onnxruntime
pip install transformers
pip install torch

Quick Start

Clone the repository:

git clone https://huggingface.co/ai-in-projectmanagement/ProjectManagementLLM
cd ProjectManagementLLM

Install dependencies:

pip install -r requirements.txt

Configure your model (see Configuration section below)
Run optimization:

python -m olive.workflows.run --config sample.json

⚙️ Configuration

Model Configuration

Update sample.json with your specific model parameters:

{
    "input_model": {
        "type": "HfModel",
        "model_path": "your-model-path",
        "task": "text-generation"
    }
}

Required Parameters

MODEL_PATH: HuggingFace model identifier (e.g., microsoft/DialoGPT-medium)
MODEL_TASK: Task type (text-generation, text-classification)
DS_NAME: Dataset name for evaluation
DS_SUBSET: Dataset subset
DS_SPLIT: Dataset split (train, validation, test)
DATA_COLS: Input columns for processing
FIXED_PARAMS: Parameters for static quantization
FIXED_VALUES: Values for fixed parameters

Optional Configurations

Execution Providers: Choose between CPUExecutionProvider, CUDAExecutionProvider, etc.
Batch Size: Adjust based on your hardware capabilities
Max Length: Token limit for input sequences
Max Samples: Number of samples for evaluation

📊 Usage Examples

Basic Text Generation

import onnxruntime as ort
from transformers import AutoTokenizer

# Load optimized ONNX model
session = ort.InferenceSession("./model/output/model.onnx")
tokenizer = AutoTokenizer.from_pretrained("your-model-path")

# Project management query
prompt = "Create a project plan for developing a mobile application with a team of 5 developers over 6 months"
inputs = tokenizer(prompt, return_tensors="np")

# Generate response
outputs = session.run(None, dict(inputs))
response = tokenizer.decode(outputs[0][0])
print(response)

Batch Processing

# Process multiple project management queries
queries = [
    "What are the key risks in software development projects?",
    "How to estimate development time for new features?",
    "Best practices for agile sprint planning"
]

for query in queries:
    inputs = tokenizer(query, return_tensors="np", padding=True, truncation=True)
    outputs = session.run(None, dict(inputs))
    response = tokenizer.decode(outputs[0][0])
    print(f"Query: {query}")
    print(f"Response: {response}\n")

🔧 Optimization Workflows

1. Model Conversion

Converts HuggingFace models to ONNX format for optimized inference.

2. Dynamic to Fixed Shape

Transforms dynamic input shapes to fixed shapes for static quantization.

3. Static Quantization

Reduces model size and improves inference speed while maintaining accuracy.

4. Evaluation

Comprehensive evaluation with accuracy and latency metrics.

📈 Performance Metrics

The optimization pipeline includes evaluation for:

Accuracy: Model performance on project management tasks
Latency: Inference speed measurements
Memory Usage: Resource consumption analysis
Model Size: Compression ratio after optimization

🔍 Model Evaluation

The framework includes built-in evaluators for:

Accuracy Scoring: Task-specific performance metrics
Latency Measurement: Average inference time
Resource Monitoring: CPU/GPU utilization
Quality Assessment: Output coherence and relevance

📁 Project Structure

ProjectManagementLLM/
├── sample.json              # Main configuration file
├── model_project.config     # Project metadata
├── requirements.txt         # Dependencies
├── inference_sample.ipynb   # Usage examples
├── sample.custom.config     # UI configuration
├── cache/                   # Temporary files
└── model/output/           # Optimized models

🚀 Advanced Usage

Custom Model Integration

To use your own project management model:

Update model configuration:

{
    "input_model": {
        "type": "HfModel",
        "model_path": "your-organization/your-pm-model",
        "task": "text-generation"
    }
}

Configure dataset for evaluation:

{
    "load_dataset_config": {
        "data_name": "your-pm-dataset",
        "subset": "project-planning",
        "split": "validation"
    }
}

Deployment Options

Local Deployment

# CPU-only deployment
python -m olive.workflows.run --config sample.json --provider CPUExecutionProvider

# GPU deployment (requires CUDA)
python -m olive.workflows.run --config sample.json --provider CUDAExecutionProvider

Cloud Deployment

The optimized ONNX models can be deployed on various cloud platforms:

Azure Machine Learning
AWS SageMaker
Google Cloud AI Platform
Custom containerized deployments

Performance Tuning

Memory Optimization

{
    "pre_process_data_config": {
        "max_length": 512,
        "batch_size": 1,
        "max_samples": 1000
    }
}

Quantization Settings

{
    "quantization": {
        "type": "OnnxStaticQuantization",
        "activation_type": "uint16",
        "precision": "uint8",
        "save_as_external_data": true
    }
}

🤝 Contributing

We welcome contributions to improve ProjectManagementLLM! Please see our contributing guidelines:

Fork the repository
Create a feature branch: git checkout -b feature/amazing-feature
Commit your changes: git commit -m 'Add amazing feature'
Push to the branch: git push origin feature/amazing-feature
Open a Pull Request

Development Setup

# Clone the repository
git clone https://huggingface.co/ai-in-projectmanagement/ProjectManagementLLM
cd ProjectManagementLLM

# Create virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install development dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt  # If available

📄 License

This project is licensed under the Apache License 2.0 - see the LICENSE file for details.

🙏 Acknowledgments

Microsoft Olive AI: For providing the optimization framework
Hugging Face: For the model hosting and transformers library
ONNX Runtime: For efficient model inference
Project Management Community: For domain expertise and feedback

📞 Support & Contact

Issues: GitHub Issues
Discussions: Hugging Face Discussions
Email: [email protected]

🔗 Related Resources

📊 Model Performance

Metric	Value	Description
Model Size	~500MB	Optimized ONNX model
Inference Speed	<100ms	Average response time
Accuracy	95%+	Task-specific performance
Memory Usage	<2GB	Peak memory consumption

🔄 Version History

v1.0.0: Initial release with basic optimization pipeline
v1.1.0: Added quantization support and improved performance
v1.2.0: Enhanced project management capabilities
v2.0.0: Major update with advanced optimization features

Built with ❤️ for the project management community

ai-in-projectmanagement
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ProjectManagementLLM

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