TroglodyteDerivations/GPT_OSS_120B_Chat_Interface_PyQt5

Model Card: GPT-OSS-120B Chat Interface

---
license: apache-2.0
tags:
- mlx
- chat-ui
- local-ai
- gpt
- python
- pyqt5
---

# GPT-OSS-120B Chat Interface

## Model Description

This is a modern, feature-rich chat interface for the GPT-OSS-120B model running on Apple MLX framework. The interface provides a user-friendly way to interact with the 120-billion parameter open-source language model locally on Apple Silicon hardware.

## Model Overview

- **Model Name:** GPT-OSS-120B (4-bit quantized)
- **Framework:** Apple MLX
- **Interface:** PyQt5-based desktop application
- **Hardware Requirements:** Apple Silicon with sufficient RAM (recommended: M3 Ultra with 512GB RAM)

## Features

- 🎨 Modern, responsive UI with PyQt5
- 💬 Real-time chat interface with message history
- ⚡ Local inference on Apple Silicon
- 📝 Markdown support with syntax highlighting
- 💾 Conversation export functionality
- ⚙️ Adjustable generation parameters
- 🎯 Code block detection and formatting
- 📊 Performance monitoring

## UI Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────┐ │ MAIN WINDOW │ ├───────────────────────────────┬─────────────────────────────────────────┤ │ LEFT PANEL │ CHAT AREA │ │ │ │ │ ┌─────────────────────────┐ │ ┌───────────────────────────────────┐ │ │ │ MODEL INFO │ │ │ │ │ │ │ - Model details │ │ │ ┌─────────────────────────────┐ │ │ │ │ - Hardware specs │ │ │ │ CHAT MESSAGE (User) │ │ │ │ │ - Performance metrics │ │ │ │ - Avatar + timestamp │ │ │ │ │ │ │ │ │ - Formatted content │ │ │ │ └─────────────────────────┘ │ │ └─────────────────────────────┘ │ │ │ │ │ │ │ │ ┌─────────────────────────┐ │ │ ┌─────────────────────────────┐ │ │ │ │ GENERATION SETTINGS │ │ │ │ CHAT MESSAGE (Assistant) │ │ │ │ │ - Max tokens control │ │ │ │ - Avatar + timestamp │ │ │ │ │ │ │ │ │ - Formatted content │ │ │ │ └─────────────────────────┘ │ │ │ - Generation time │ │ │ │ │ │ └─────────────────────────────┘ │ │ │ │ │ ... │ │ │ ┌─────────────────────────┐ │ │ │ │ │ │ CONVERSATION TOOLS │ │ │ ┌─────────────────────────────┐ │ │ │ │ - Clear conversation │ │ │ │ INPUT AREA │ │ │ │ │ - Export chat │ │ │ │ - Multi-line text input │ │ │ │ │ │ │ │ │ - Character counter │ │ │ │ └─────────────────────────┘ │ │ │ - Send button │ │ │ │ │ │ └─────────────────────────────┘ │ │ │ ┌─────────────────────────┐ │ │ │ │ │ │ STATUS INDICATOR │ │ │ │ │ │ │ - Loading/ready state │ │ │ │ │ │ │ │ │ │ │ │ │ └─────────────────────────┘ │ │ │ │ │ │ │ │ │ └───────────────────────────────┴───────────────────────────────────────┘

## Development

This interface was developed using PyQt5 and integrates with the MLX-LM library for efficient inference on Apple Silicon. The UI features a responsive design with:

1. **Threaded Operations:** Model loading and text generation run in background threads
2. **Custom Widgets:** Specialized chat message widgets with formatting
3. **Syntax Highlighting:** Code detection and highlighting in responses
4. **Modern Styling:** Clean, professional interface with appropriate spacing and colors

## DeepSeek Involvement

The `gpt_oss_ui.py` Python script was created with assistance from DeepSeek's AI models, which helped design the architecture, implement the PyQt5 interface components, and ensure proper integration with the MLX inference backend.

## Usage

1. Install requirements: `pip install PyQt5 markdown mlx-lm`
2. Run the application: `python gpt_oss_ui.py`
3. Wait for model to load (first time will download the model)
4. Start chatting with the GPT-OSS-120B model

## Performance

On an M3 Ultra with 512GB RAM:
- Model load time: ~2-3 minutes (first time)
- Inference speed: ~95 tokens/second
- Memory usage: Optimized with 4-bit quantization

## Limitations

- Requires significant RAM for the 120B parameter model
- Currently only supports Apple Silicon hardware
- Model loading can be time-consuming on first run

## Ethical Considerations

This interface is designed for local use, ensuring privacy as all processing happens on-device. Users should still follow responsible AI practices when using the model.