Profile Builder with Local LLM

Why Build a Local LLM Profile Builder?

This project was driven by several key motivations:

• Privacy Concerns: When using AI services to generate professional profiles, users often need to share sensitive career information. By leveraging local LLM models, this tool keeps all data on the user's device.
• Time Efficiency: Creating and optimizing profiles for multiple platforms can take hours. Automation significantly reduces this workload.
• Platform Expertise: Different platforms have different content requirements and expectations. The tool is programmed with knowledge of these differences to optimize content for each destination.
• Explore Local LLM Capabilities: This project demonstrates how powerful local language models have become, capable of processing complex tasks without cloud-based APIs.

Technology Stack

For this project, I selected a stack that balances performance, privacy, and ease of deployment:

Development Process

1. System Architecture Design

The project is built with a modular architecture consisting of several key components:

• Portfolio Extractor Module: Extracts professional data from existing portfolio websites
• LLM Integration Layer: Communicates with the Ollama-hosted deepseek-r1 model
• Profile Generator: Creates platform-specific content from the extracted data
• Web Interface: Provides a user-friendly interface for the entire process

2. Local LLM Integration

A key challenge was integrating with Ollama to run the deepseek-r1 model locally. This involved:

• Setting up Ollama with the appropriate model configuration
• Creating a client to handle model communication
• Designing effective prompts for profile generation
• Optimizing token usage for efficient processing

# Example of generating a professional title with the LLMdef generate_title(self, portfolio_data, platform="general"): """Generate a professional title based on portfolio data.""" prompt = f""" Create a compelling professional title for a {platform} profile based on the following information. The title should be concise, impactful, and highlight key expertise. Experience: {portfolio_data.get('experience', [])}Skills: {portfolio_data.get('skills', {})}Professional Title: """ title = self.client.generate(prompt, max_tokens=50, temperature=0.7) return title.strip()

3. Platform-Specific Content Generation

Different platforms have different content requirements and expectations. The system recognizes key platforms and tailors the content generation:

• Upwork: Focused on specific services and achievements with ideal hourly rates
• LinkedIn: More comprehensive professional narrative with industry keywords
• Freelancer.com: Project-centric content highlighting specific deliverables
• Various Job Boards: Role-specific optimization for particular industries

4. User Interface Development

The web interface was designed to be straightforward and user-friendly:

• Input field for portfolio URL
• Platform selection interface with visuals
• Optional credential input for automation
• Results display with copy functionality
• Status indicators for system readiness

Key Features

Technical Challenges and Solutions

1. Local LLM Performance

Running large language models locally presents performance challenges, especially on systems with limited resources.

2. Extracting Structured Data

Portfolio websites vary greatly in structure, making consistent data extraction challenging.

3. Prompt Engineering for Quality

Getting consistent, high-quality outputs from LLMs requires careful prompt design.

Conclusion

The Profile Builder with Local LLM project demonstrates how locally-run AI models can deliver powerful, privacy-preserving solutions for practical problems. By combining modern LLM technology with targeted prompting and a user-friendly interface, the tool streamlines the often tedious process of maintaining professional profiles across multiple platforms.

This project also highlights the growing maturity of local LLM solutions, offering an alternative to cloud-based APIs for tasks that involve sensitive personal or professional information. As local models continue to improve in capabilities and efficiency, we can expect to see more applications taking this approach.

The code for this project is available on GitHub, with the aim of contributing to the growing ecosystem of privacy-focused, local AI solutions.