# Example: Loading Mistral Tiny on edge device
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load quantized model optimized for edge deployment
model_name = "mistral-tiny-edge-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    load_in_8bit=True,
    trust_remote_code=True
)

# Set up model for edge optimization
model.eval()
model.half()  # FP16 precision for reduced memory usage

def edge_inference(prompt, max_tokens=100):
    """Run inference optimized for edge devices"""
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    
    # Generate with edge-specific parameters
    outputs = model.generate(
        inputs.input_ids,
        max_new_tokens=max_tokens,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test the model
result = edge_inference("Translate to French: Hello, how are you?")
print(result)

# Advanced quantization for edge deployment
import torch
import torch.nn.functional as F

def custom_quantize_model(model, bits=8):
    """Custom quantization function for edge optimization"""
    for name, param in model.named_parameters():
        if 'weight' in name:
            # Apply symmetric quantization
            qmin = -2**(bits-1)
            qmax = 2**(bits-1) - 1
            
            # Calculate scaling factor
            max_abs = torch.max(torch.abs(param))
            scale = (qmax - qmin) / max_abs
            
            # Quantize
            param_quantized = torch.round(param * scale)
            param_quantized = torch.clamp(param_quantized, qmin, qmax)
            
            # De-quantize for inference
            param.data = param_quantized / scale
    
    return model

# Apply quantization to Mistral Tiny
quantized_model = custom_quantize_model(model, bits=8)

# Automotive voice assistant using Mistral Tiny
import sounddevice as sd
import numpy as np

class EdgeVoiceAssistant:
    def __init__(self, model, threshold=0.5):
        self.model = model
        self.threshold = threshold
        self.active = False
        
    def audio_callback(self, indata, frames, time, status):
        """Real-time audio processing callback"""
        volume_norm = np.linalg.norm(indata) * 10
        
        if volume_norm > self.threshold and not self.active:
            self.active = True
            print("Voice activation detected")
            # Capture audio and process
            audio_data = self.record_audio(3.0)  # Record 3 seconds
            text = self.speech_to_text(audio_data)
            response = self.process_command(text)
            self.speak_response(response)
            self.active = False
    
    def process_command(self, command):
        """Process voice command with Mistral Tiny"""
        # Context-aware command processing
        if "navigate to" in command.lower():
            return self.model.generate(
                f"Navigation instructions: {command}",
                max_tokens=50
            )
        elif "play" in command.lower():
            return self.model.generate(
                f"Music selection: {command}",
                max_tokens=30
            )
        else:
            return self.model.generate(
                f"General response: {command}",
                max_tokens=40
            )

# Initialize and run
assistant = EdgeVoiceAssistant(model)
with sd.InputStream(callback=assistant.audio_callback):
    sd.sleep(int(1e6))  # Keep stream open

# Edge-optimized inference pipeline
import time
import psutil
from collections import deque

class EdgeOptimizer:
    def __init__(self, model, max_queue=5):
        self.model = model
        self.max_queue = max_queue
        self.request_queue = deque(maxlen=max_queue)
        self.latency_metrics = []
        
    def optimized_inference(self, prompt, priority="normal"):
        """Perform inference with edge optimizations"""
        start_time = time.time()
        
        # Check resource availability
        if not self.check_resources():
            return self.handle_resource_constraints(prompt)
        
        # Add to processing queue
        self.request_queue.append((prompt, priority))
        
        # Process based on priority
        if priority == "high":
            result = self.model.generate(prompt, max_new_tokens=50)
        else:
            result = self.model.generate(prompt, max_new_tokens=100, 
                                       do_sample=False)
        
        # Collect metrics
        latency = time.time() - start_time
        self.latency_metrics.append(latency)
        
        # Adaptive optimization
        if len(self.latency_metrics) > 10:
            self.adaptive_optimization()
        
        return result
    
    def check_resources(self):
        """Check if device has sufficient resources"""
        mem = psutil.virtual_memory()
        cpu = psutil.cpu_percent(interval=0.1)
        
        # Thresholds for edge device
        return mem.available > 100 * 1024 * 1024 and cpu < 80
    
    def adaptive_optimization(self):
        """Adjust model parameters based on performance"""
        avg_latency = sum(self.latency_metrics) / len(self.latency_metrics)
        
        if avg_latency > 100:  # ms
            # Reduce model complexity
            self.model.config.temperature = 0.9
            self.model.config.max_new_tokens = 80
        elif avg_latency < 20:
            # Increase quality
            self.model.config.temperature = 1.0
            self.model.config.max_new_tokens = 120

# Usage example
optimizer = EdgeOptimizer(model)
response = optimizer.optimized_inference(
    "What is the current temperature setting?", 
    priority="high"
)

# Dockerfile for Mistral Tiny edge deployment
FROM nvcr.io/nvidia/l4t-base:r35.2.0

# Install dependencies
RUN apt-get update && apt-get install -y \
    python3 \
    python3-pip \
    && rm -rf /var/lib/apt/lists/*

# Create app directory
WORKDIR /app

# Install Python packages
COPY requirements.txt .
RUN pip3 install --no-cache-dir -r requirements.txt

# Copy model and application code
COPY model /app/model
COPY edge_app.py /app/

# Expose port for monitoring
EXPOSE 8080

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python3 -c "import torch; torch.rand(1).to('cuda')"

CMD ["python3", "edge_app.py"]

# Advanced memory management for edge deployment
import gc
import torch

class MemoryManager:
    def __init__(self, model, max_memory_usage=0.8):
        self.model = model
        self.max_memory_usage = max_memory_usage
        self.swap_threshold = 0.7
        
    def optimize_memory(self):
        """Comprehensive memory optimization"""
        # 1. Gradient checkpointing
        torch.enable_grad()
        torch.autograd.set_grad_enabled(False)
        
        # 2. Activation checkpointing
        torch.utils.checkpoint.checkpoint_sequential(
            self.model, segments=2, inputs=self.model.input_ids
        )
        
        # 3. Memory swapping
        if self.get_memory_usage() > self.swap_threshold:
            self.swap_to_storage()
        
        # 4. Garbage collection
        gc.collect()
        torch.cuda.empty_cache()
    
    def get_memory_usage(self):
        """Calculate current memory usage"""
        mem = psutil.virtual_memory()
        return mem.percent / 100.0
    
    def swap_to_storage(self):
        """Swap model parameters to external storage"""
        for name, param in self.model.named_parameters():
            if param.is_cuda and torch.rand(1) > 0.5:  # Probabilistic swapping
                torch.save(param, f'/tmp/{name}.pt')
                param.data = torch.zeros_like(param.data)

# Usage
memory_manager = MemoryManager(model)
memory_manager.optimize_memory()