Upload 4 files

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+output.mp4 filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2025-09-02[[:space:]]at[[:space:]]4.10.14 PM.png filter=lfs diff=lfs merge=lfs -text

download_GPT_OSS_120B_MXFP4_Q4_Model.py

@@ -0,0 +1,323 @@
+#!/usr/bin/env python3
+"""
+MLX GPT-OSS-120B-MXFP4-Q4 Model Downloader
+This script downloads the mlx-community/gpt-oss-120b-MXFP4-Q4 model from Hugging Face Hub
+with various download options and verification features.
+"""
+
+import argparse
+import os
+import json
+import logging
+import shutil
+from datetime import datetime
+from pathlib import Path
+from huggingface_hub import snapshot_download, hf_hub_download, HfApi, ModelCard
+import torch
+import mlx.core as mx
+import mlx.nn as nn
+from transformers import AutoConfig, AutoTokenizer
+import numpy as np
+
+# Set up logging
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+def get_model_info(repo_id):
+    """Get information about the model from Hugging Face Hub."""
+    try:
+        api = HfApi()
+        model_info = api.model_info(repo_id)
+        
+        logger.info(f"📋 Model Information:")
+        logger.info(f"   Name: {model_info.id}")
+        logger.info(f"   Downloads: {model_info.downloads:,}")
+        logger.info(f"   Likes: {model_info.likes}")
+        logger.info(f"   Last Modified: {model_info.lastModified}")
+        logger.info(f"   Library: {model_info.library_name}")
+        logger.info(f"   Tags: {', '.join(model_info.tags)}")
+        
+        # Try to get model card
+        try:
+            card = ModelCard.load(repo_id)
+            logger.info(f"   Model Card: {card.data.get('model_name', 'N/A')}")
+        except:
+            logger.info("   Model Card: Not available")
+            
+        return model_info
+    except Exception as e:
+        logger.warning(f"⚠️ Could not fetch model info: {e}")
+        return None
+
+def calculate_disk_space_required(repo_id, revision="main"):
+    """Calculate approximate disk space required for the model."""
+    try:
+        api = HfApi()
+        files = api.list_repo_files(repo_id, revision=revision)
+        
+        total_size = 0
+        model_files = []
+        
+        for file in files:
+            if any(file.endswith(ext) for ext in ['.safetensors', '.npz', '.json', '.txt', '.model', '.py']):
+                file_info = api.hf_hub_url(repo_id, file, revision=revision)
+                # This is approximate - actual download might use more space due to temp files
+                if 'safetensors' in file or 'npz' in file:
+                    model_files.append(file)
+        
+        # GPT-OSS-120B-MXFP4-Q4 is approximately 60-70GB in MXFP4-Q4 format
+        logger.info(f"💾 Estimated download size: ~60-70GB (MXFP4-Q4 format)")
+        logger.info(f"   Model files: {len(model_files)} weight files")
+        
+        return model_files
+    except Exception as e:
+        logger.warning(f"⚠️ Could not calculate disk space: {e}")
+        return []
+
+def download_model(args):
+    """Download the model with specified options."""
+    repo_id = "mlx-community/gpt-oss-120b-MXFP4-Q4"
+    
+    logger.info("=" * 70)
+    logger.info("🤗 MLX GPT-OSS-120B-MXFP4-Q4 Model Downloader")
+    logger.info("=" * 70)
+    
+    # Get model information
+    model_info = get_model_info(repo_id)
+    calculate_disk_space_required(repo_id, args.revision)
+    
+    download_kwargs = {
+        "repo_id": repo_id,
+        "revision": args.revision,
+        "local_dir": args.output_dir,
+        "local_dir_use_symlinks": False,  # Always copy files, don't symlink
+        "resume_download": True,
+        "force_download": args.force_download,
+    }
+    
+    if args.allow_patterns:
+        download_kwargs["allow_patterns"] = args.allow_patterns
+    if args.ignore_patterns:
+        download_kwargs["ignore_patterns"] = args.ignore_patterns
+    
+    try:
+        logger.info(f"🚀 Starting download of {repo_id}")
+        logger.info(f"📁 Output directory: {args.output_dir}")
+        logger.info(f"🔖 Revision: {args.revision}")
+        logger.info(f"💾 Cache dir: {args.cache_dir}")
+        
+        if args.cache_dir:
+            download_kwargs["cache_dir"] = args.cache_dir
+        
+        # Download the model
+        model_path = snapshot_download(**download_kwargs)
+        
+        logger.info(f"✅ Download completed successfully!")
+        logger.info(f"📦 Model saved to: {model_path}")
+        
+        return model_path
+        
+    except Exception as e:
+        logger.error(f"❌ Download failed: {e}")
+        raise
+
+def verify_model_download(model_path):
+    """Verify that the model download is complete and valid."""
+    logger.info(f"🔍 Verifying model download...")
+    
+    required_files = [
+        "config.json",
+        "tokenizer.json",
+        "tokenizer_config.json",
+        "model.npz",  # MLX models use .npz files
+        "generation_config.json"
+    ]
+    
+    missing_files = []
+    for file in required_files:
+        if not os.path.exists(os.path.join(model_path, file)):
+            missing_files.append(file)
+    
+    if missing_files:
+        logger.warning(f"⚠️ Missing files: {missing_files}")
+    else:
+        logger.info("✅ All required files present")
+    
+    # Try to load config
+    try:
+        config = AutoConfig.from_pretrained(model_path)
+        logger.info(f"✅ Config loaded successfully")
+        logger.info(f"   Architecture: {config.architectures[0] if config.architectures else 'N/A'}")
+        logger.info(f"   Vocab size: {config.vocab_size:,}")
+        logger.info(f"   Hidden size: {config.hidden_size}")
+        logger.info(f"   Num layers: {config.num_hidden_layers}")
+        logger.info(f"   Model type: {config.model_type}")
+    except Exception as e:
+        logger.warning(f"⚠️ Could not load config: {e}")
+    
+    return len(missing_files) == 0
+
+def load_model_for_verification(model_path, args):
+    """Optionally load the model to verify it works (memory intensive)."""
+    if not args.verify_load:
+        return None
+    
+    logger.info("🧪 Loading model for verification (this may take a while and require significant RAM)...")
+    
+    try:
+        # Load tokenizer first
+        tokenizer = AutoTokenizer.from_pretrained(model_path)
+        logger.info("✅ Tokenizer loaded successfully")
+        
+        # For MLX models, we need to check if the weights can be loaded
+        try:
+            # Check if we can load the weights
+            weights = mx.load(os.path.join(model_path, "model.npz"))
+            logger.info(f"✅ Model weights loaded successfully")
+            logger.info(f"   Number of weight arrays: {len(weights)}")
+            
+            # Test a simple inference if requested
+            if args.test_inference:
+                logger.info("🧪 Testing tokenizer and basic functionality...")
+                test_text = "The capital of France is"
+                inputs = tokenizer(test_text, return_tensors="np")
+                
+                logger.info(f"📝 Tokenized input: {inputs}")
+                logger.info(f"   Input shape: {inputs['input_ids'].shape}")
+                
+        except Exception as e:
+            logger.warning(f"⚠️ Model weight loading failed: {e}")
+        
+        return None, tokenizer
+        
+    except Exception as e:
+        logger.warning(f"⚠️ Model loading failed: {e}")
+        return None, None
+
+def create_readme(model_path, args):
+    """Create a README file with download information."""
+    readme_content = f"""# GPT-OSS-120B-MXFP4-Q4 Model Download
+## Download Information
+- **Model**: mlx-community/gpt-oss-120b-MXFP4-Q4
+- **Download Date**: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}
+- **Revision**: {args.revision}
+- **Output Directory**: {args.output_dir}
+## Download Options Used
+- Allow patterns: {args.allow_patterns or 'All files'}
+- Ignore patterns: {args.ignore_patterns or 'None'}
+- Force download: {args.force_download}
+- Verify load: {args.verify_load}
+- Test inference: {args.test_inference}
+## Model Details
+- **Architecture**: Transformer-based causal language model
+- **Parameters**: 120 billion
+- **Context Length**: 4096 tokens
+- **Quantization**: MXFP4-Q4 (4-bit quantization optimized for MLX)
+- **Framework**: MLX (Apple Silicon optimized)
+- **Languages**: Primarily English
+## Usage with MLX
+```python
+import mlx.core as mx
+import mlx.nn as nn
+from transformers import AutoTokenizer
+
+# Load weights
+weights = mx.load("{model_path}/model.npz")
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("{model_path}")
+
+# Note: You'll need to implement the model architecture to use the weights
+Usage with Transformers (for tokenizer only)
+
+python
+from transformers import AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained("{model_path}")
+"""
+    readme_path = os.path.join(model_path, "DOWNLOAD_INFO.md")
+    with open(readme_path, 'w') as f:
+        f.write(readme_content)
+
+    logger.info(f"📝 Created README: {readme_path}")
+
+def main():
+    parser = argparse.ArgumentParser(description="Download mlx-community/gpt-oss-120b-MXFP4-Q4 model")
+    # Download options
+    parser.add_argument("--output-dir", type=str, default="./gpt_oss_120b_mxfp4_q4",
+               help="Directory to save the model")
+    parser.add_argument("--cache-dir", type=str, default="./hf_cache",
+               help="Cache directory for Hugging Face")
+    parser.add_argument("--revision", type=str, default="main",
+               help="Model revision/branch to download")
+    parser.add_argument("--force-download", action="store_true",
+               help="Force re-download even if files exist")
+
+    # Filter options
+    parser.add_argument("--allow-patterns", nargs="+",
+               help="Only download files matching these patterns")
+    parser.add_argument("--ignore-patterns", nargs="+",
+               default=["*.h5", "*.ot", "*.msgpack", "*.tflite", "*.bin"],
+               help="Skip files matching these patterns")
+
+    # Verification options
+    parser.add_argument("--verify-load", action="store_true",
+               help="Load model after download to verify it works")
+    parser.add_argument("--test-inference", action="store_true",
+               help="Run a test inference after loading")
+
+    args = parser.parse_args()
+
+    # Create output directory
+    os.makedirs(args.output_dir, exist_ok=True)
+
+    try:
+        # Download the model
+        model_path = download_model(args)
+
+        # Verify download
+        verify_model_download(model_path)
+
+        # Optionally load and test the model
+        if args.verify_load:
+            model, tokenizer = load_model_for_verification(model_path, args)
+
+        # Create readme
+        create_readme(model_path, args)
+
+        logger.info("🎉 Model download and verification completed successfully!")
+        logger.info(f"📁 Model available at: {model_path}")
+        logger.info("💡 Next steps: Use the model with MLX framework:")
+        logger.info(f"   import mlx.core as mx")
+        logger.info(f"   weights = mx.load('{model_path}/model.npz')")
+        logger.info(f"   from transformers import AutoTokenizer")
+        logger.info(f"   tokenizer = AutoTokenizer.from_pretrained('{model_path}')")
+
+    except Exception as e:
+        logger.error(f"❌ Download failed: {e}")
+        return 1
+
+    return 0
+if __name__ == "__main__":
+    exit(main())
+
+
+
+## Key Differences for the GPT-OSS-120B-MXFP4-Q4 Model:
+
+## 1. **Model Format**: This model uses MLX's `.npz` format instead of PyTorch's `.safetensors` or `.bin` files
+## 2. **Framework**: Optimized for Apple's MLX framework rather than standard PyTorch
+## 3. **Quantization**: Uses MXFP4-Q4 quantization (4-bit) which is specific to MLX
+## 4. **Size**: At 120B parameters, this is a much larger model than SmolLM3-3B
+## 5. **Loading**: The model loading process is different for MLX models compared to standard Transformers models
+
+## Usage Notes:
+
+## 1. This script requires the `mlx` package to be installed for full functionality
+## 2. The model is optimized for Apple Silicon devices
+## 3. Due to the model's large size (60-70GB), ensure you have sufficient disk space
+## 4. The script includes special handling for MLX's file format and quantization
+
+## You can run this script with various options like:
+## ```bash
+## python download_gpt_oss_120b.py --output-dir ./my_model --verify-load --test-inference

gpt_oss_ui.py

@@ -0,0 +1,700 @@
+#!/usr/bin/env python3
+"""
+Enhanced Modern UI for GPT-OSS-120B Chat Interface
+"""
+
+import sys
+import time
+import threading
+import markdown
+from PyQt5.QtWidgets import (QApplication, QMainWindow, QWidget, QVBoxLayout, 
+                             QHBoxLayout, QTextEdit, QLineEdit, QPushButton, 
+                             QLabel, QScrollArea, QFrame, QGroupBox, QSpinBox,
+                             QSizePolicy, QProgressBar, QSplitter, QToolButton,
+                             QMenu, QAction, QFileDialog, QMessageBox)
+from PyQt5.QtCore import Qt, QThread, pyqtSignal, QTimer, QSize
+from PyQt5.QtGui import QFont, QTextCursor, QPalette, QColor, QIcon, QTextCharFormat, QSyntaxHighlighter, QTextDocument
+from mlx_lm import load, generate
+import logging
+import re
+import json
+from datetime import datetime
+from typing import List, Dict
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+class ModelLoaderThread(QThread):
+    """Thread for loading the model to prevent UI freezing"""
+    model_loaded = pyqtSignal()
+    model_error = pyqtSignal(str)
+    progress_update = pyqtSignal(str)
+    
+    def __init__(self):
+        super().__init__()
+    
+    def run(self):
+        try:
+            self.progress_update.emit("Downloading model files...")
+            logger.info("🚀 Loading GPT-OSS-120B...")
+            model, tokenizer = load("mlx-community/gpt-oss-120b-MXFP4-Q4")
+            logger.info("✅ Model loaded successfully!")
+            self.progress_update.emit("Model loaded successfully!")
+            self.model_loaded.emit()
+        except Exception as e:
+            logger.error(f"Failed to load model: {e}")
+            self.model_error.emit(str(e))
+
+
+class GenerationThread(QThread):
+    """Thread for generating responses to prevent UI freezing"""
+    response_ready = pyqtSignal(str, float)
+    error_occurred = pyqtSignal(str)
+    progress_update = pyqtSignal(str)
+    
+    def __init__(self, model, tokenizer, prompt, max_tokens):
+        super().__init__()
+        self.model = model
+        self.tokenizer = tokenizer
+        self.prompt = prompt
+        self.max_tokens = max_tokens
+    
+    def run(self):
+        try:
+            start_time = time.time()
+            
+            # Format prompt with chat template
+            self.progress_update.emit("Formatting prompt...")
+            messages = [{"role": "user", "content": self.prompt}]
+            formatted_prompt = self.tokenizer.apply_chat_template(
+                messages, add_generation_prompt=True
+            )
+            
+            # Generate response
+            self.progress_update.emit("Generating response...")
+            response = generate(
+                self.model,
+                self.tokenizer,
+                prompt=formatted_prompt,
+                max_tokens=self.max_tokens,
+                verbose=False
+            )
+            
+            # Extract and clean the final response
+            self.progress_update.emit("Processing response...")
+            final_response = self.extract_final_response(response)
+            generation_time = time.time() - start_time
+            
+            self.response_ready.emit(final_response, generation_time)
+            
+        except Exception as e:
+            self.error_occurred.emit(str(e))
+    
+    def extract_final_response(self, response: str) -> str:
+        """Extract the final assistant response from the chat template"""
+        # Look for the final assistant response
+        if "<|start|>assistant" in response:
+            parts = response.split("<|start|>assistant")
+            if len(parts) > 1:
+                final_part = parts[-1]
+                
+                # Remove all channel and message tags
+                final_part = re.sub(r'<\|channel\|>[^<]+', '', final_part)
+                final_part = final_part.replace('<|message|>', '')
+                final_part = final_part.replace('<|end|>', '')
+                
+                # Clean up any remaining tags or whitespace
+                final_part = re.sub(r'<[^>]+>', '', final_part)
+                final_part = final_part.strip()
+                
+                if final_part:
+                    return final_part
+        
+        # Fallback: return the original response cleaned up
+        cleaned = re.sub(r'<\|[^>]+\|>', '', response)
+        cleaned = re.sub(r'<[^>]+>', '', cleaned)
+        return cleaned.strip()
+
+
+class CodeHighlighter(QSyntaxHighlighter):
+    """Basic syntax highlighter for code blocks"""
+    def __init__(self, parent=None):
+        super().__init__(parent)
+        
+        self.highlighting_rules = []
+        
+        # Keyword format
+        keyword_format = QTextCharFormat()
+        keyword_format.setForeground(QColor("#569CD6"))
+        keyword_format.setFontWeight(QFont.Bold)
+        keywords = ["def", "class", "return", "import", "from", "as", "if", 
+                   "else", "elif", "for", "while", "try", "except", "finally"]
+        for word in keywords:
+            pattern = r'\b' + word + r'\b'
+            self.highlighting_rules.append((re.compile(pattern), keyword_format))
+        
+        # String format
+        string_format = QTextCharFormat()
+        string_format.setForeground(QColor("#CE9178"))
+        self.highlighting_rules.append((re.compile(r'\".*\"'), string_format))
+        self.highlighting_rules.append((re.compile(r'\'.*\''), string_format))
+        
+        # Comment format
+        comment_format = QTextCharFormat()
+        comment_format.setForeground(QColor("#6A9955"))
+        self.highlighting_rules.append((re.compile(r'#.*'), comment_format))
+    
+    def highlightBlock(self, text):
+        for pattern, format in self.highlighting_rules:
+            for match in pattern.finditer(text):
+                start, end = match.span()
+                self.setFormat(start, end - start, format)
+
+
+class ChatMessageWidget(QWidget):
+    """Custom widget for displaying chat messages"""
+    def __init__(self, is_user, message, timestamp=None, generation_time=None):
+        super().__init__()
+        self.is_user = is_user
+        
+        layout = QVBoxLayout()
+        layout.setContentsMargins(15, 8, 15, 8)
+        
+        # Header with sender info and timestamp
+        header_layout = QHBoxLayout()
+        
+        sender_icon = QLabel("👤" if is_user else "🤖")
+        sender_label = QLabel("You" if is_user else "GPT-OSS-120B")
+        sender_label.setStyleSheet("font-weight: bold; color: #2E86AB;" if is_user else "font-weight: bold; color: #A23B72;")
+        
+        time_text = timestamp if timestamp else datetime.now().strftime("%H:%M:%S")
+        time_label = QLabel(time_text)
+        time_label.setStyleSheet("color: #777; font-size: 11px;")
+        
+        header_layout.addWidget(sender_icon)
+        header_layout.addWidget(sender_label)
+        header_layout.addStretch()
+        header_layout.addWidget(time_label)
+        
+        if generation_time and not is_user:
+            speed_label = QLabel(f"{generation_time:.1f}s")
+            speed_label.setStyleSheet("color: #777; font-size: 11px;")
+            header_layout.addWidget(speed_label)
+        
+        layout.addLayout(header_layout)
+        
+        # Message content - use QTextEdit for proper text rendering
+        message_display = QTextEdit()
+        message_display.setReadOnly(True)
+        
+        # Format message with basic markdown support
+        formatted_message = self.format_message(message)
+        message_display.setHtml(formatted_message)
+        
+        message_display.setVerticalScrollBarPolicy(Qt.ScrollBarAsNeeded)
+        message_display.setHorizontalScrollBarPolicy(Qt.ScrollBarAsNeeded)
+        message_display.setStyleSheet("""
+            QTextEdit {
+                background-color: %s;
+                border: 1px solid %s;
+                border-radius: 12px;
+                padding: 12px;
+                margin: 2px;
+                font-size: 14px;
+            }
+        """ % ("#E8F4F8" if is_user else "#F8F0F5", "#B8D8E8" if is_user else "#E8C6DE"))
+        
+        # Set size policy
+        message_display.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Preferred)
+        message_display.setMinimumHeight(50)
+        message_display.setMaximumHeight(600)
+        
+        # Add syntax highlighter for code blocks
+        if not is_user and self.contains_code(message):
+            highlighter = CodeHighlighter(message_display.document())
+        
+        layout.addWidget(message_display)
+        self.setLayout(layout)
+    
+    def format_message(self, message):
+        """Format message with basic HTML styling"""
+        # Convert markdown to basic HTML
+        html = markdown.markdown(message)
+        
+        # Add some basic styling
+        styled_html = f"""
+        <div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; 
+                   line-height: 1.4; color: #333;">
+            {html}
+        </div>
+        """
+        return styled_html
+    
+    def contains_code(self, message):
+        """Check if message contains code-like content"""
+        code_indicators = ["def ", "class ", "import ", "function ", "var ", "const ", "=", "()", "{}", "[]"]
+        return any(indicator in message for indicator in code_indicators)
+
+
+class GPTOSSChatUI(QMainWindow):
+    def __init__(self):
+        super().__init__()
+        self.model = None
+        self.tokenizer = None
+        self.conversation_history = []
+        self.max_tokens = 2048
+        self.generation_thread = None
+        self.model_loader_thread = None
+        
+        self.init_ui()
+        self.load_model_in_background()
+    
+    def init_ui(self):
+        """Initialize the user interface"""
+        self.setWindowTitle("GPT-OSS-120B Chat")
+        self.setGeometry(100, 100, 1400, 900)
+        
+        # Central widget
+        central_widget = QWidget()
+        self.setCentralWidget(central_widget)
+        
+        # Main layout
+        main_layout = QHBoxLayout(central_widget)
+        main_layout.setContentsMargins(15, 15, 15, 15)
+        main_layout.setSpacing(15)
+        
+        # Left panel for settings
+        left_panel = QFrame()
+        left_panel.setMinimumWidth(280)
+        left_panel.setMaximumWidth(350)
+        left_panel.setFrameShape(QFrame.StyledPanel)
+        left_panel_layout = QVBoxLayout(left_panel)
+        left_panel_layout.setContentsMargins(12, 12, 12, 12)
+        
+        # App title
+        title_label = QLabel("GPT-OSS-120B Chat")
+        title_label.setStyleSheet("font-size: 18px; font-weight: bold; color: #2E86AB; margin-bottom: 15px;")
+        title_label.setAlignment(Qt.AlignCenter)
+        left_panel_layout.addWidget(title_label)
+        
+        # Model info
+        model_info_group = QGroupBox("🤖 Model Information")
+        model_info_group.setStyleSheet("QGroupBox { font-weight: bold; }")
+        model_info_layout = QVBoxLayout()
+        
+        model_details = [
+            ("GPT-OSS-120B", "font-weight: bold; font-size: 14px; color: #333;"),
+            ("120B parameters, 4-bit quantized", "color: #666; font-size: 12px;"),
+            ("Apple M3 Ultra • 512GB RAM", "color: #666; font-size: 12px;"),
+            ("Performance: ~95 tokens/second", "color: #4CAF50; font-size: 12px; font-weight: bold;")
+        ]
+        
+        for text, style in model_details:
+            label = QLabel(text)
+            label.setStyleSheet(style)
+            label.setWordWrap(True)
+            model_info_layout.addWidget(label)
+        
+        model_info_group.setLayout(model_info_layout)
+        left_panel_layout.addWidget(model_info_group)
+        
+        # Generation settings
+        settings_group = QGroupBox("⚙️ Generation Settings")
+        settings_group.setStyleSheet("QGroupBox { font-weight: bold; }")
+        settings_layout = QVBoxLayout()
+        
+        # Max tokens setting
+        tokens_layout = QHBoxLayout()
+        tokens_label = QLabel("Max Tokens:")
+        tokens_label.setStyleSheet("font-weight: bold;")
+        self.tokens_spinner = QSpinBox()
+        self.tokens_spinner.setRange(128, 4096)
+        self.tokens_spinner.setValue(2048)
+        self.tokens_spinner.valueChanged.connect(self.update_max_tokens)
+        self.tokens_spinner.setStyleSheet("padding: 6px; border-radius: 4px;")
+        tokens_layout.addWidget(tokens_label)
+        tokens_layout.addWidget(self.tokens_spinner)
+        settings_layout.addLayout(tokens_layout)
+        
+        settings_group.setLayout(settings_layout)
+        left_panel_layout.addWidget(settings_group)
+        
+        # Conversation management
+        conv_group = QGroupBox("💬 Conversation")
+        conv_group.setStyleSheet("QGroupBox { font-weight: bold; }")
+        conv_layout = QVBoxLayout()
+        
+        clear_btn = QPushButton("🗑️ Clear Conversation")
+        clear_btn.clicked.connect(self.clear_conversation)
+        clear_btn.setStyleSheet("text-align: left; padding: 8px;")
+        conv_layout.addWidget(clear_btn)
+        
+        export_btn = QPushButton("💾 Export Conversation")
+        export_btn.clicked.connect(self.export_conversation)
+        export_btn.setStyleSheet("text-align: left; padding: 8px;")
+        conv_layout.addWidget(export_btn)
+        
+        conv_group.setLayout(conv_layout)
+        left_panel_layout.addWidget(conv_group)
+        
+        left_panel_layout.addStretch()
+        
+        # Status indicator
+        self.status_indicator = QLabel("🟡 Loading model...")
+        self.status_indicator.setStyleSheet("color: #666; font-size: 11px; margin-top: 10px;")
+        left_panel_layout.addWidget(self.status_indicator)
+        
+        # Right panel for chat
+        right_panel = QWidget()
+        right_panel_layout = QVBoxLayout(right_panel)
+        right_panel_layout.setContentsMargins(0, 0, 0, 0)
+        
+        # Chat history area
+        self.chat_scroll = QScrollArea()
+        self.chat_scroll.setWidgetResizable(True)
+        self.chat_scroll.setVerticalScrollBarPolicy(Qt.ScrollBarAsNeeded)
+        self.chat_scroll.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOff)
+        self.chat_scroll.setStyleSheet("background-color: #FAFAFA; border: none;")
+        
+        self.chat_container = QWidget()
+        self.chat_layout = QVBoxLayout(self.chat_container)
+        self.chat_layout.setAlignment(Qt.AlignTop)
+        self.chat_layout.setSpacing(10)
+        self.chat_layout.setContentsMargins(10, 10, 10, 10)
+        
+        self.chat_scroll.setWidget(self.chat_container)
+        right_panel_layout.addWidget(self.chat_scroll)
+        
+        # Input area
+        input_frame = QFrame()
+        input_frame.setStyleSheet("background-color: white; border-top: 1px solid #EEE;")
+        input_layout = QVBoxLayout(input_frame)
+        input_layout.setContentsMargins(15, 15, 15, 15)
+        
+        # Message input with character count
+        input_top_layout = QHBoxLayout()
+        self.message_input = QTextEdit()
+        self.message_input.setPlaceholderText("Type your message here... (Shift+Enter for new line)")
+        self.message_input.setMaximumHeight(100)
+        self.message_input.setStyleSheet("""
+            QTextEdit {
+                padding: 12px;
+                border: 2px solid #DDD;
+                border-radius: 8px;
+                font-size: 14px;
+            }
+            QTextEdit:focus {
+                border-color: #2E86AB;
+            }
+        """)
+        self.message_input.textChanged.connect(self.update_char_count)
+        input_top_layout.addWidget(self.message_input)
+        
+        self.send_btn = QPushButton("Send")
+        self.send_btn.setFixedSize(80, 50)
+        self.send_btn.clicked.connect(self.send_message)
+        self.send_btn.setStyleSheet("""
+            QPushButton {
+                background-color: #2E86AB;
+                color: white;
+                border: none;
+                border-radius: 8px;
+                font-weight: bold;
+            }
+            QPushButton:hover {
+                background-color: #1F5E7A;
+            }
+            QPushButton:disabled {
+                background-color: #CCCCCC;
+            }
+        """)
+        input_top_layout.addWidget(self.send_btn)
+        
+        input_layout.addLayout(input_top_layout)
+        
+        # Character count and controls
+        bottom_layout = QHBoxLayout()
+        self.char_count = QLabel("0 characters")
+        self.char_count.setStyleSheet("color: #777; font-size: 11px;")
+        bottom_layout.addWidget(self.char_count)
+        
+        bottom_layout.addStretch()
+        
+        # Add some utility buttons
+        clear_input_btn = QPushButton("Clear Input")
+        clear_input_btn.setStyleSheet("font-size: 11px; padding: 4px 8px;")
+        clear_input_btn.clicked.connect(self.clear_input)
+        bottom_layout.addWidget(clear_input_btn)
+        
+        input_layout.addLayout(bottom_layout)
+        right_panel_layout.addWidget(input_frame)
+        
+        # Add panels to main layout
+        main_layout.addWidget(left_panel)
+        main_layout.addWidget(right_panel)
+        
+        # Status bar
+        self.statusBar().showMessage("Ready")
+        
+        # Set styles
+        self.apply_styles()
+    
+    def apply_styles(self):
+        """Apply modern styling to the UI"""
+        self.setStyleSheet("""
+            QMainWindow {
+                background-color: #F5F5F7;
+            }
+            QGroupBox {
+                font-weight: bold;
+                border: 1px solid #E0E0E0;
+                border-radius: 8px;
+                margin-top: 10px;
+                padding-top: 20px;
+                background-color: white;
+            }
+            QGroupBox::title {
+                subcontrol-origin: margin;
+                left: 10px;
+                padding: 0 8px 0 8px;
+                color: #2E86AB;
+            }
+            QPushButton {
+                background-color: #2E86AB;
+                color: white;
+                border: none;
+                padding: 8px 16px;
+                border-radius: 6px;
+                font-weight: bold;
+            }
+            QPushButton:hover {
+                background-color: #1F5E7A;
+            }
+            QPushButton:disabled {
+                background-color: #CCCCCC;
+            }
+            QScrollArea {
+                border: none;
+                background-color: #FAFAFA;
+            }
+            QSpinBox {
+                padding: 6px;
+                border: 1px solid #DDD;
+                border-radius: 4px;
+                background-color: white;
+            }
+            QFrame {
+                background-color: white;
+                border-radius: 8px;
+            }
+        """)
+    
+    def update_char_count(self):
+        """Update character count label"""
+        text = self.message_input.toPlainText()
+        self.char_count.setText(f"{len(text)} characters")
+    
+    def clear_input(self):
+        """Clear the input field"""
+        self.message_input.clear()
+    
+    def load_model_in_background(self):
+        """Load the model in a separate thread to prevent UI freezing"""
+        self.statusBar().showMessage("Loading model...")
+        self.status_indicator.setText("🟡 Loading model...")
+        self.send_btn.setEnabled(False)
+        self.message_input.setEnabled(False)
+        self.tokens_spinner.setEnabled(False)
+        
+        self.model_loader_thread = ModelLoaderThread()
+        self.model_loader_thread.model_loaded.connect(self.model_loaded)
+        self.model_loader_thread.model_error.connect(self.model_error)
+        self.model_loader_thread.progress_update.connect(self.update_progress)
+        self.model_loader_thread.start()
+    
+    def update_progress(self, message):
+        """Update progress message"""
+        self.status_indicator.setText(f"🟡 {message}")
+    
+    def model_loaded(self):
+        """Called when model is successfully loaded"""
+        from mlx_lm import load, generate
+        # Load the model in the main thread
+        try:
+            self.model, self.tokenizer = load("mlx-community/gpt-oss-120b-MXFP4-Q4")
+            self.statusBar().showMessage("Model loaded and ready!")
+            self.status_indicator.setText("🟢 Model loaded and ready!")
+            self.send_btn.setEnabled(True)
+            self.message_input.setEnabled(True)
+            self.tokens_spinner.setEnabled(True)
+            
+            # Add welcome message
+            welcome_msg = """Hello! I'm GPT-OSS-120B, running locally on your M3 Ultra. 
+
+I'm a 120 billion parameter open-source language model, and I'm here to assist you with:
+- Answering questions
+- Generating creative content
+- Explaining complex concepts
+- Writing and analyzing code
+- And much more!
+
+How can I help you today?"""
+            self.add_message(False, welcome_msg, 0.0)
+            
+            # Scroll to bottom after a short delay to ensure UI is rendered
+            QTimer.singleShot(100, self.scroll_to_bottom)
+        except Exception as e:
+            self.model_error(str(e))
+    
+    def model_error(self, error_msg):
+        """Called when model loading fails"""
+        self.statusBar().showMessage(f"Error loading model: {error_msg}")
+        self.status_indicator.setText(f"🔴 Error: {error_msg}")
+        error_widget = ChatMessageWidget(False, f"Error loading model: {error_msg}")
+        self.chat_layout.addWidget(error_widget)
+        self.send_btn.setEnabled(False)
+        self.message_input.setEnabled(False)
+    
+    def send_message(self):
+        """Send the current message"""
+        message = self.message_input.toPlainText().strip()
+        if not message or not self.model:
+            return
+        
+        # Add user message to chat
+        self.add_message(True, message)
+        self.message_input.clear()
+        
+        # Disable input while generating
+        self.send_btn.setEnabled(False)
+        self.message_input.setEnabled(False)
+        self.tokens_spinner.setEnabled(False)
+        self.statusBar().showMessage("Generating response...")
+        self.status_indicator.setText("🟡 Generating response...")
+        
+        # Generate response in a separate thread
+        self.generation_thread = GenerationThread(
+            self.model, self.tokenizer, message, self.max_tokens
+        )
+        self.generation_thread.response_ready.connect(self.handle_response)
+        self.generation_thread.error_occurred.connect(self.handle_error)
+        self.generation_thread.progress_update.connect(self.update_progress)
+        self.generation_thread.start()
+    
+    def handle_response(self, response, generation_time):
+        """Handle the generated response"""
+        self.add_message(False, response, generation_time)
+        
+        # Re-enable input
+        self.send_btn.setEnabled(True)
+        self.message_input.setEnabled(True)
+        self.tokens_spinner.setEnabled(True)
+        self.statusBar().showMessage("Ready")
+        self.status_indicator.setText("🟢 Ready")
+        
+        # Scroll to bottom
+        self.scroll_to_bottom()
+    
+    def handle_error(self, error_msg):
+        """Handle generation errors"""
+        self.add_message(False, f"Error: {error_msg}", 0.0)
+        
+        # Re-enable input
+        self.send_btn.setEnabled(True)
+        self.message_input.setEnabled(True)
+        self.tokens_spinner.setEnabled(True)
+        self.statusBar().showMessage("Error occurred")
+        self.status_indicator.setText("🔴 Error occurred")
+        
+        # Scroll to bottom
+        self.scroll_to_bottom()
+    
+    def add_message(self, is_user, message, generation_time=0.0):
+        """Add a message to the chat history"""
+        # Add to conversation history
+        self.conversation_history.append({
+            "is_user": is_user,
+            "message": message,
+            "timestamp": datetime.now().strftime("%H:%M:%S"),
+            "generation_time": generation_time
+        })
+        
+        # Create and add message widget
+        message_widget = ChatMessageWidget(is_user, message, datetime.now().strftime("%H:%M:%S"), generation_time)
+        self.chat_layout.addWidget(message_widget)
+    
+    def clear_conversation(self):
+        """Clear the conversation history"""
+        # Clear history
+        self.conversation_history = []
+        
+        # Remove all message widgets
+        for i in reversed(range(self.chat_layout.count())):
+            widget = self.chat_layout.itemAt(i).widget()
+            if widget:
+                widget.setParent(None)
+        
+        # Add welcome message again
+        welcome_msg = "Hello! I'm GPT-OSS-120B. How can I assist you today?"
+        self.add_message(False, welcome_msg, 0.0)
+        
+        # Scroll to bottom
+        self.scroll_to_bottom()
+    
+    def export_conversation(self):
+        """Export the conversation to a file"""
+        try:
+            options = QFileDialog.Options()
+            file_path, _ = QFileDialog.getSaveFileName(
+                self, "Save Conversation", "conversation.json", "JSON Files (*.json)", options=options
+            )
+            
+            if file_path:
+                if not file_path.endswith('.json'):
+                    file_path += '.json'
+                
+                with open(file_path, 'w', encoding='utf-8') as f:
+                    json.dump(self.conversation_history, f, indent=2, ensure_ascii=False)
+                
+                QMessageBox.information(self, "Success", f"Conversation exported to {file_path}")
+        except Exception as e:
+            QMessageBox.critical(self, "Error", f"Failed to export conversation: {str(e)}")
+    
+    def update_max_tokens(self, value):
+        """Update the maximum tokens for generation"""
+        self.max_tokens = value
+    
+    def scroll_to_bottom(self):
+        """Scroll the chat area to the bottom"""
+        scrollbar = self.chat_scroll.verticalScrollBar()
+        scrollbar.setValue(scrollbar.maximum())
+    
+    def keyPressEvent(self, event):
+        """Handle key press events"""
+        if event.key() == Qt.Key_Return and event.modifiers() & Qt.ShiftModifier:
+            # Allow Shift+Enter for new lines
+            self.message_input.insertPlainText("\n")
+        elif event.key() == Qt.Key_Return:
+            # Send message on Enter (without Shift)
+            self.send_message()
+        else:
+            super().keyPressEvent(event)
+
+
+def main():
+    app = QApplication(sys.argv)
+    
+    # Set application style and font
+    app.setStyle('Fusion')
+    font = QFont("SF Pro Text", 12)  # Use system font
+    app.setFont(font)
+    
+    # Create and show the main window
+    chat_ui = GPTOSSChatUI()
+    chat_ui.show()
+    
+    sys.exit(app.exec_())
+
+
+if __name__ == "__main__":
+    main()

output.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:91d93386ebf345500857f3eadda6317c5cc2c70774f790b9e6a290db3f2df01e
+size 9746422