🎯 Abstract

Key Features

✔ Model-Agnostic – Works with any Ollama model (Llama3, Qwen-2.5-Coder, Mistral, etc.).
✔ Auto-Detection – Detects and lists installed models without extra setup.
✔ User-Friendly – Chat-style interface for smooth interaction.

Deployment Considerations

• Supported OS: Windows 11, Ubuntu 22.04
• Hardware Requirements: 16GB RAM (recommended), NVIDIA RTX 3060+
• Installation: Available as a standalone executable (app.exe).

🔗 Source Code: GitHub Repository
📖 Installation Guide: Setup Instructions

📩 Contact Information of Asset Creators

For inquiries, support, or contributions, reach out to:

• Project Owner: Mohsin Nawaz
• GitHub Issues: Report an issue
• Email: contact@mohsinnawaz.one

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🛠️ Introduction

The Problem

Local LLMs like Ollama are powerful but require CLI knowledge, creating challenges for:

• 🧑‍💻 Non-technical users unfamiliar with terminal commands.
• ⚡ Developers who need faster workflows without CLI friction.
• 🔬 Researchers looking for a visual workflow for model comparisons.

🚀 Current State Gap Identification

1️⃣ Challenges with Existing Local LLM Solutions

Despite the advancements in local LLMs, several challenges persist:

• ❌ Complex CLI-Based Interactions – Requires users to enter commands manually.
• ❌ Lack of Real-Time Feedback – No intuitive way to monitor responses.

2️⃣ Identified Gaps

After testing existing Ollama CLI workflows, we identified key limitations:

• ⚠️ High Learning Curve: Beginners struggle with terminal-based commands.
• ⚠️ Model Switching Inconvenience: No direct UI to switch between models.
• ⚠️ No Multi-Query Handling: Users must restart interactions for every query.

3️⃣ Opportunities for Improvement

To address these issues, our Local LLMs, Now With Buttons! introduces:
✅ One-Click Model Selection – No need to remember model names.
✅ Intuitive Chat Interface – Simplifies LLM interactions.
✅ Auto-Detection of Installed Models – Instantly detects available LLMs.
✅ Real-Time Query Execution – Allows users to run multiple queries seamlessly.

By bridging these gaps, our GUI empowers users with a more efficient and accessible way to leverage local LLMs. 🚀

The Solution

✅ One-click model selection – Supports all Ollama-compatible models.
✅ Interactive Chat Interface – Conversational responses with async processing.
✅ Zero Configuration – Auto-detects installed models, no setup required.

Visual Demonstration

Below is a preview of the LMS on Local GUI interface:

🛠️ Maintenance and Support Status

This project is actively maintained with regular updates and improvements. Contributions from the community are welcome via pull requests and issue reporting.

• Current Version: v1.0.0
• Release Cycle: Quarterly updates & monthly bug fixes
• Community Contributions: Feature requests, bug reports, and enhancements are encouraged.

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⚙️ Methodology

Deployment Considerations

• OS Compatibility: Windows 11, Ubuntu 22.04
• Minimum Hardware: 8GB RAM, Integrated GPU
• Recommended Hardware: 16GB RAM, NVIDIA RTX 3060+
• Software Dependencies:
  • Python 3.7+
  • Ollama CLI
  • Tkinter for GUI

Technical Stack

Dataset Processing Methodology

• Preprocessing: Cleaned and formatted prompt inputs.
• Evaluation Metrics: Accuracy, execution time, and usability feedback.
• Comparative Analysis: Benchmarked against GPT-4 responses.

📚 Dataset Sources & Collection

• Primary Data Source: Open-source datasets compatible with Ollama models
• Pre-loaded Models: Llama3, Qwen-2.5-Coder

📊 Dataset Description

The dataset consists of various model interactions, including:

• General Knowledge QA (Llama3)
• Programming and Debugging Tasks (Qwen-2.5-Coder)

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🔬 Experiments

Testing Scope

Comparative Analysis

User Testing

👩‍💻 Participants: 5 developers & researchers
🛠 Tasks:

• Execute various prompts using CLI vs GUI.
• Debug recursion & syntax errors in Python.
  ⏳ Goal: Measure task completion speed & usability.

Key Findings

✅ 40% faster task execution (GUI reduced task time from 2.1 mins → 1.30 mins).
✅ New users learned the GUI 7x faster (under 2 minutes).
✅ 90% preferred GUI over CLI for debugging and model switching.

⚠️ Limitations:

• Only tested with Llama3 & Qwen-2.5-Coder – broader model validation needed.
• No chat history/export – future update required.

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📊 Results

Impact Metrics

User Feedback

📢 "Has others but this is fast!"
– Python Developer, Professional Group

Use Cases Enabled

👨‍💻 Developers – Rapidly test code snippets across models.
🔬 Researchers – Compare LLM outputs side-by-side.
🎓 Students – Learn LLM capabilities without CLI anxiety.

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🏁 Conclusion

Key Takeaways

🚀 Local LLMs, Now With Buttons!, simplifies local LLM access by:

• Eliminating CLI friction – making AI accessible for all users.
• Supporting all Ollama models with instant switching.
• Boosting productivity – reducing task execution time by 40%.

Next Steps

🔹 Model Management – Install/delete models via GUI.
🔹 Multi-Model Chat – Compare outputs side-by-side.
🔹 Logging & Export – Allow saving chat history for later analysis.
🔹 Future updates – Automated logging and performance analytics.

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🔍 Monitoring and Maintenance Considerations

To ensure smooth operation and stability, the following monitoring practices are recommended:

• Model Versioning: Keep track of installed model versions for compatibility.
• Security Patches: Ensure updates to dependencies and security fixes are applied timely.

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Installation & Usage Instructions

# Install Ollama CLI & a model  
ollama install mistral  

# Run LMS on Local  
./app.exe