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# Alex's shocking cost calculation
def calculate_fine_tuning_costs():
    """Alex discovers why full fine-tuning is so expensive"""
    
    # LLaMA 7B model parameters
    total_parameters = 7_000_000_000
    
    # Full fine-tuning updates ALL parameters
    parameters_updated = total_parameters
    
    # Memory requirements (rough calculation)
    # Model weights + gradients + optimizer states + activations
    memory_per_param = 20  # bytes (fp16 + gradients + Adam states)
    total_memory_gb = (parameters_updated * memory_per_param) / (1024**3)
    
    # Hardware costs (AWS p4d.24xlarge)
    hourly_cost = 32.77  # USD
    training_hours = 168  # 1 week
    total_cost = hourly_cost * training_hours
    
    print("Alex's Full Fine-Tuning Reality Check:")
    print(f"Parameters to update: {parameters_updated:,}")
    print(f"Memory required: {total_memory_gb:.1f} GB")
    print(f"Training time: {training_hours} hours")
    print(f"Total cost: ${total_cost:,.2f}")
    
    return total_cost

# Alex's expensive discovery
full_tuning_cost = calculate_fine_tuning_costs()

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import numpy as np
import matplotlib.pyplot as plt

def alex_visualizes_parameter_importance():
    """Alex's breakthrough visualization"""
    
    # Simulate parameter importance for customer service task
    np.random.seed(42)
    all_layers = 32  # LLaMA layers
    
    # Most parameters don't need significant updates
    base_importance = np.random.exponential(0.1, all_layers)
    
    # Only specific layers are crucial for the task
    task_specific_layers = [20, 22, 24, 26, 28, 30]  # Later layers
    for layer in task_specific_layers:
        base_importance[layer] *= 10  # Much more important
    
    plt.figure(figsize=(12, 6))
    plt.bar(range(all_layers), base_importance, alpha=0.7)
    plt.axhline(y=np.mean(base_importance), color='r', linestyle='--', 
                label='Average Importance')
    plt.title("Alex's Parameter Importance Discovery")
    plt.xlabel("Model Layer")
    plt.ylabel("Importance for Customer Service Task")
    plt.legend()
    plt.show()
    
    # Alex's insight
    critical_layers = np.sum(base_importance > np.mean(base_importance))
    efficiency_gain = all_layers / critical_layers
    
    print(f"Alex realizes: Only {critical_layers} out of {all_layers} layers are critical")
    print(f"Potential efficiency gain: {efficiency_gain:.1f}x")
    
    return efficiency_gain

# Alex's parameter efficiency discovery
efficiency_potential = alex_visualizes_parameter_importance()

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import torch
import torch.nn as nn
import numpy as np

class AlexUnderstandsLoRA:
    """Alex's step-by-step LoRA comprehension"""
    
    def __init__(self, original_dim=4096, rank=16):
        self.original_dim = original_dim  # Original weight matrix size
        self.rank = rank  # LoRA rank (much smaller)
        
    def demonstrate_traditional_approach(self):
        """What Alex was doing before (expensive)"""
        # Full fine-tuning: update entire weight matrix
        original_weights = torch.randn(self.original_dim, self.original_dim)
        updated_weights = original_weights + torch.randn_like(original_weights) * 0.01
        
        parameters_updated = original_weights.numel()
        print(f"Traditional approach updates: {parameters_updated:,} parameters")
        
        return updated_weights
    
    def demonstrate_lora_approach(self):
        """Alex's LoRA breakthrough"""
        # LoRA: represent updates as low-rank decomposition
        # Instead of updating full matrix, use two smaller matrices
        
        # A matrix: (original_dim, rank)
        lora_A = torch.randn(self.original_dim, self.rank) * 0.01
        
        # B matrix: (rank, original_dim)  
        lora_B = torch.randn(self.rank, self.original_dim) * 0.01
        
        # The update is A @ B (matrix multiplication)
        lora_update = lora_A @ lora_B
        
        # Total parameters in LoRA
        lora_parameters = lora_A.numel() + lora_B.numel()
        
        print(f"LoRA approach updates: {lora_parameters:,} parameters")
        print(f"Reduction factor: {(self.original_dim**2) / lora_parameters:.1f}x")
        
        return lora_A, lora_B, lora_update
    
    def alex_sees_the_magic(self):
        """Alex's complete understanding"""
        print("Alex's LoRA Discovery:")
        print("=" * 50)
        
        # Traditional approach
        traditional = self.demonstrate_traditional_approach()
        
        print("\nLoRA Approach:")
        lora_A, lora_B, lora_update = self.demonstrate_lora_approach()
        
        # Show the mathematical equivalence
        print(f"\nMatrix dimensions:")
        print(f"Original update: {traditional.shape}")
        print(f"LoRA A matrix: {lora_A.shape}")
        print(f"LoRA B matrix: {lora_B.shape}")
        print(f"LoRA update (A @ B): {lora_update.shape}")
        
        # Calculate efficiency gains
        original_params = traditional.numel()
        lora_params = lora_A.numel() + lora_B.numel()
        
        print(f"\nEfficiency Analysis:")
        print(f"Parameter reduction: {original_params / lora_params:.1f}x fewer")
        print(f"Memory reduction: ~{original_params / lora_params:.1f}x less")
        print(f"Training speed: ~{original_params / lora_params:.1f}x faster")

# Alex's LoRA understanding session
alex_lora = AlexUnderstandsLoRA()
alex_lora.alex_sees_the_magic()

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def alex_grasps_low_rank_concept():
    """Alex's intuitive understanding of low-rank adaptation"""
    
    # Customer service skills can be decomposed
    base_skills = [
        "language_understanding", "grammar", "reasoning", "memory"
    ]
    
    customer_service_skills = [
        "politeness", "problem_solving", "product_knowledge", "empathy"
    ]
    
    print("Alex's Skill Decomposition Analogy:")
    print(f"Base skills (frozen): {base_skills}")
    print(f"New skills (LoRA): {customer_service_skills}")
    print()
    print("LoRA Insight: Instead of retraining all skills,")
    print("we add specialized modules for new capabilities!")
    
    # Mathematical representation
    print("\nMathematical Representation:")
    print("Full fine-tuning: W_new = W_original + ΔW (huge)")
    print("LoRA: W_new = W_original + A @ B (tiny A and B matrices)")
    print("Where A @ B approximates ΔW with far fewer parameters")

alex_grasps_low_rank_concept()

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# Alex's production-ready LoRA implementation
import torch
from transformers import (
    AutoModelForCausalLM, 
    AutoTokenizer, 
    TrainingArguments,
    Trainer
)
from peft import (
    LoraConfig, 
    get_peft_model, 
    TaskType,
    prepare_model_for_kbit_training
)
from datasets import Dataset
import json

class AlexLoRATrainer:
    """Alex's complete LoRA fine-tuning solution"""
    
    def __init__(self, model_name="meta-llama/Llama-2-7b-hf"):
        self.model_name = model_name
        self.device = "cuda" if torch.cuda.is_available() else "cpu"
        print(f"Alex's setup: Using {self.device}")
        
    def setup_model_and_tokenizer(self):
        """Alex's model preparation"""
        print("Loading model and tokenizer...")
        
        # Load tokenizer
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        self.tokenizer.pad_token = self.tokenizer.eos_token
        
        # Load model with optimizations
        self.model = AutoModelForCausalLM.from_pretrained(
            self.model_name,
            torch_dtype=torch.float16,  # Memory optimization
            device_map="auto",          # Automatic device placement
            load_in_8bit=True          # Further memory reduction
        )
        
        # Prepare for LoRA training
        self.model = prepare_model_for_kbit_training(self.model)
        
        print("✅ Model and tokenizer loaded successfully")
        
    def configure_lora(self, rank=16, alpha=32, dropout=0.1):
        """Alex's LoRA configuration"""
        
        lora_config = LoraConfig(
            r=rank,                    # Rank of adaptation
            lora_alpha=alpha,          # LoRA scaling parameter
            target_modules=[           # Which modules to adapt
                "q_proj", "k_proj", "v_proj", "o_proj",
                "gate_proj", "up_proj", "down_proj"
            ],
            lora_dropout=dropout,      # LoRA dropout
            bias="none",               # Don't adapt bias
            task_type=TaskType.CAUSAL_LM
        )
        
        # Apply LoRA to the model
        self.model = get_peft_model(self.model, lora_config)
        
        # Show trainable parameters
        self.model.print_trainable_parameters()
        
        print("✅ LoRA configuration applied")
        return lora_config
        
    def prepare_customer_service_data(self):
        """Alex's customer service dataset"""
        
        # Alex's customer service training examples
        training_data = [
            {
                "instruction": "Handle a billing inquiry",
                "input": "Customer: I was charged twice for my subscription this month.",
                "output": "I sincerely apologize for the billing error. Let me look into your account immediately. I can see the duplicate charge and will process a refund within 2-3 business days. I'll also add a note to prevent this from happening again. Is there anything else I can help you with today?"
            },
            {
                "instruction": "Address a product complaint",
                "input": "Customer: Your product stopped working after just one week!",
                "output": "I'm very sorry to hear about the issue with your product. That's definitely not the experience we want for our customers. Let me arrange a replacement for you right away. I'll also escalate this to our quality team to investigate. Your replacement will ship today with expedited delivery at no charge."
            },
            {
                "instruction": "Handle a return request",
                "input": "Customer: I want to return this item, but I lost the receipt.",
                "output": "No problem at all! I can look up your purchase using your email or phone number. Once I locate the transaction, I can process your return. Our policy allows returns within 30 days, and I can see your purchase was recent. Would you like store credit or a refund to your original payment method?"
            },
            # Add more examples for comprehensive training
        ]
        
        # Format for training
        formatted_data = []
        for example in training_data:
            prompt = f"### Instruction:\n{example['instruction']}\n\n### Input:\n{example['input']}\n\n### Response:\n{example['output']}"
            formatted_data.append({"text": prompt})
        
        # Create dataset
        dataset = Dataset.from_list(formatted_data)
        
        print(f"✅ Prepared {len(formatted_data)} training examples")
        return dataset
        
    def tokenize_data(self, dataset):
        """Alex's data tokenization"""
        
        def tokenize_function(examples):
            return self.tokenizer(
                examples["text"],
                truncation=True,
                padding=True,
                max_length=512,
                return_tensors="pt"
            )
        
        tokenized_dataset = dataset.map(tokenize_function, batched=True)
        print("✅ Data tokenized successfully")
        return tokenized_dataset
        
    def train_with_lora(self, dataset, output_dir="./alex-customer-service-lora"):
        """Alex's LoRA training process"""
        
        # Training arguments optimized for efficiency
        training_args = TrainingArguments(
            output_dir=output_dir,
            per_device_train_batch_size=4,
            gradient_accumulation_steps=4,
            warmup_steps=100,
            max_steps=500,
            learning_rate=2e-4,
            fp16=True,                    # Memory optimization
            logging_steps=50,
            save_steps=250,
            evaluation_strategy="no",
            save_strategy="steps",
            load_best_model_at_end=False,
            report_to=None,               # Disable wandb
        )
        
        # Create trainer
        trainer = Trainer(
            model=self.model,
            args=training_args,
            train_dataset=dataset,
            tokenizer=self.tokenizer,
        )
        
        # Start training
        print("🚀 Starting LoRA training...")
        print(f"Training on {self.device}")
        
        # Track training time and cost
        import time
        start_time = time.time()
        
        trainer.train()
        
        training_time = time.time() - start_time
        print(f"✅ Training completed in {training_time/60:.1f} minutes")
        
        # Save the LoRA adapter
        self.model.save_pretrained(output_dir)
        self.tokenizer.save_pretrained(output_dir)
        
        print(f"✅ Model saved to {output_dir}")
        
        return training_time
        
    def calculate_cost_savings(self, training_time_minutes):
        """Alex's cost comparison"""
        
        # LoRA training costs (single GPU)
        gpu_hourly_cost = 0.50  # Consumer GPU equivalent
        lora_cost = (training_time_minutes / 60) * gpu_hourly_cost
        
        # Full fine-tuning costs (from Alex's earlier experience)
        full_tuning_cost = 10847  # Alex's AWS bill
        
        savings = full_tuning_cost - lora_cost
        savings_percentage = (savings / full_tuning_cost) * 100
        
        print("\n" + "="*50)
        print("ALEX'S COST REVOLUTION")
        print("="*50)
        print(f"Full fine-tuning cost: ${full_tuning_cost:,.2f}")
        print(f"LoRA fine-tuning cost: ${lora_cost:.2f}")
        print(f"Total savings: ${savings:,.2f}")
        print(f"Cost reduction: {savings_percentage:.1f}%")
        print(f"Training time: {training_time_minutes:.1f} minutes")
        print("="*50)
        
        return {
            "lora_cost": lora_cost,
            "full_tuning_cost": full_tuning_cost,
            "savings": savings,
            "savings_percentage": savings_percentage
        }

# Alex's complete LoRA training pipeline
def alex_trains_with_lora():
    """Alex's end-to-end LoRA success story"""
    
    trainer = AlexLoRATrainer()
    
    # Setup
    trainer.setup_model_and_tokenizer()
    trainer.configure_lora(rank=16, alpha=32)
    
    # Prepare data
    dataset = trainer.prepare_customer_service_data()
    tokenized_dataset = trainer.tokenize_data(dataset)
    
    # Train
    training_time = trainer.train_with_lora(tokenized_dataset)
    
    # Calculate savings
    cost_analysis = trainer.calculate_cost_savings(training_time)
    
    return cost_analysis

# Alex's transformation (run this when you have the setup)
# cost_results = alex_trains_with_lora()

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def alex_tests_lora_performance():
    """Alex validates LoRA effectiveness"""
    
    # Load the fine-tuned model
    from peft import PeftModel
    
    # Alex's testing framework
    test_cases = [
        {
            "scenario": "Billing Issue",
            "input": "I was charged for a service I cancelled last month.",
            "expected_tone": "apologetic and solution-focused"
        },
        {
            "scenario": "Product Defect",
            "input": "This product broke immediately after I bought it.",
            "expected_tone": "empathetic and proactive"
        }
    ]
    
    print("Alex's LoRA Performance Test:")
    print("=" * 40)
    
    for i, test in enumerate(test_cases, 1):
        print(f"\nTest {i}: {test['scenario']}")
        print(f"Input: {test['input']}")
        print(f"Expected: {test['expected_tone']}")
        print("LoRA Response: [Generated response would appear here]")
        print("✅ Tone and helpfulness: Excellent")
    
    # Performance metrics Alex tracks
    metrics = {
        "response_quality": "95% customer satisfaction",
        "training_cost": "$47.23",
        "training_time": "2.3 hours",
        "model_size": "16M additional parameters (0.2% of original)",
        "inference_speed": "Same as base model"
    }
    
    print(f"\nAlex's LoRA Success Metrics:")
    for metric, value in metrics.items():
        print(f"• {metric.replace('_', ' ').title()}: {value}")

alex_tests_lora_performance()

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class AlexAdvancedLoRA:
    """Alex's advanced LoRA techniques"""
    
    def create_task_specific_loras(self):
        """Alex creates specialized LoRA adapters"""
        
        lora_configs = {
            "customer_service": LoraConfig(
                r=16,
                lora_alpha=32,
                target_modules=["q_proj", "v_proj", "o_proj"],
                lora_dropout=0.1,
                task_type=TaskType.CAUSAL_LM
            ),
            
            "technical_support": LoraConfig(
                r=32,  # Higher rank for more complex task
                lora_alpha=64,
                target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
                lora_dropout=0.05,  # Lower dropout for precision
                task_type=TaskType.CAUSAL_LM
            ),
            
            "sales_assistant": LoraConfig(
                r=8,   # Lower rank for simpler task
                lora_alpha=16,
                target_modules=["q_proj", "v_proj"],
                lora_dropout=0.15,
                task_type=TaskType.CAUSAL_LM
            )
        }
        
        print("Alex's Multi-LoRA Strategy:")
        for task, config in lora_configs.items():
            params = config.r * 2 * 4096  # Approximate parameter count
            print(f"• {task}: {params:,} parameters (rank {config.r})")
        
        return lora_configs
    
    def optimize_lora_hyperparameters(self):
        """Alex's hyperparameter optimization"""
        
        # Alex's systematic approach to finding optimal settings
        hyperparameter_grid = {
            "rank": [4, 8, 16, 32, 64],
            "alpha": [8, 16, 32, 64, 128],
            "learning_rate": [1e-4, 2e-4, 5e-4, 1e-3],
            "dropout": [0.0, 0.05, 0.1, 0.2]
        }
        
        # Alex's findings from experimentation
        optimal_configs = {
            "general_purpose": {"rank": 16, "alpha": 32, "lr": 2e-4, "dropout": 0.1},
            "high_precision": {"rank": 32, "alpha": 64, "lr": 1e-4, "dropout": 0.05},
            "fast_adaptation": {"rank": 8, "alpha": 16, "lr": 5e-4, "dropout": 0.15}
        }
        
        print("Alex's Optimal LoRA Configurations:")
        for use_case, config in optimal_configs.items():
            print(f"\n{use_case.replace('_', ' ').title()}:")
            for param, value in config.items():
                print(f"  • {param}: {value}")
        
        return optimal_configs
    
    def implement_lora_merging(self):
        """Alex learns to merge LoRA adapters"""
        
        merge_strategies = {
            "weighted_average": "Combine multiple LoRA adapters with different weights",
            "task_routing": "Route inputs to appropriate LoRA adapter based on content",
            "hierarchical": "Stack LoRA adapters for complex multi-task scenarios"
        }
        
        print("Alex's LoRA Merging Strategies:")
        for strategy, description in merge_strategies.items():
            print(f"• {strategy.replace('_', ' ').title()}: {description}")
        
        # Example: Weighted merging for customer service + sales
        example_weights = {
            "customer_service_lora": 0.7,
            "sales_assistant_lora": 0.3
        }
        
        print(f"\nExample: Customer service with sales capability")
        for lora, weight in example_weights.items():
            print(f"  • {lora}: {weight*100}% weight")

# Alex's advanced techniques
alex_advanced = AlexAdvancedLoRA()
alex_advanced.create_task_specific_loras()
alex_advanced.optimize_lora_hyperparameters()
alex_advanced.implement_lora_merging()

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def alex_deploys_lora_production():
    """Alex's production deployment insights"""
    
    deployment_benefits = {
        "model_switching": "Swap LoRA adapters instantly for different tasks",
        "a_b_testing": "Test different LoRA versions with minimal overhead",
        "personalization": "Create user-specific LoRA adapters",
        "incremental_updates": "Update specific capabilities without full retraining"
    }
    
    print("Alex's Production LoRA Benefits:")
    for benefit, description in deployment_benefits.items():
        print(f"• {benefit.replace('_', ' ').title()}: {description}")
    
    # Alex's cost analysis for production
    production_metrics = {
        "model_storage": "16MB per LoRA adapter (vs 13GB full model)",
        "switching_time": "<1 second to change adapters",
        "memory_usage": "Same as base model + tiny adapter",
        "scaling_cost": "$50 to create new specialized version"
    }
    
    print(f"\nProduction Metrics:")
    for metric, value in production_metrics.items():
        print(f"• {metric.replace('_', ' ').title()}: {value}")

alex_deploys_lora_production()

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def alex_business_transformation():
    """Alex's complete business transformation through LoRA"""
    
    before_lora = {
        "project_cost": 10000,
        "training_time": "1-2 weeks",
        "hardware_needed": "Expensive cloud GPUs",
        "client_accessibility": "Enterprise only",
        "profit_margin": "10-20%",
        "projects_per_month": 1
    }
    
    after_lora = {
        "project_cost": 200,
        "training_time": "2-4 hours",
        "hardware_needed": "Consumer GPU",
        "client_accessibility": "SMBs and individuals",
        "profit_margin": "80-90%",
        "projects_per_month": 15
    }
    
    print("ALEX'S BUSINESS TRANSFORMATION")
    print("=" * 50)
    print(f"{'Metric':<20} {'Before LoRA':<15} {'After LoRA':<15} {'Improvement'}")
    print("-" * 65)
    
    improvements = {}
    for metric in before_lora:
        before = before_lora[metric]
        after = after_lora[metric]
        
        if metric in ["project_cost", "projects_per_month"]:
            if metric == "project_cost":
                improvement = f"{before/after:.0f}x cheaper"
            else:
                improvement = f"{after/before:.0f}x more"
        else:
            improvement = "Dramatically better"
        
        print(f"{metric.replace('_', ' '):<20} {str(before):<15} {str(after):<15} {improvement}")
    
    # Revenue calculation
    monthly_revenue_before = before_lora["projects_per_month"] * before_lora["project_cost"] * 0.15
    monthly_revenue_after = after_lora["projects_per_month"] * after_lora["project_cost"] * 0.85
    
    print(f"\nRevenue Impact:")
    print(f"Monthly revenue before: ${monthly_revenue_before:,.2f}")
    print(f"Monthly revenue after: ${monthly_revenue_after:,.2f}")
    print(f"Revenue increase: {monthly_revenue_after/monthly_revenue_before:.1f}x")

alex_business_transformation()

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def alex_client_success_stories():
    """Real impact Alex created for clients"""
    
    client_stories = [
        {
            "client": "Local Restaurant Chain",
            "challenge": "Needed multilingual customer service chatbot",
            "solution": "LoRA adapters for English, Spanish, French",
            "cost": "$150 per language",
            "outcome": "40% reduction in support tickets, 95% customer satisfaction"
        },
        {
            "client": "E-commerce Startup",
            "challenge": "Product description generation for 10,000+ items",
            "solution": "Category-specific LoRA adapters",
            "cost": "$300 total",
            "outcome": "Generated descriptions in 2 days vs 6 months manual work"
        },
        {
            "client": "Legal Firm",
            "challenge": "Document summarization for case research",
            "solution": "Legal-domain LoRA with privacy guarantees",
            "cost": "$500",
            "outcome": "Reduced research time by 70%, maintained confidentiality"
        }
    ]
    
    print("ALEX'S CLIENT SUCCESS STORIES")
    print("=" * 60)
    
    for i, story in enumerate(client_stories, 1):
        print(f"\nClient {i}: {story['client']}")
        print(f"Challenge: {story['challenge']}")
        print(f"LoRA Solution: {story['solution']}")
        print(f"Cost: {story['cost']}")
        print(f"Outcome: {story['outcome']}")
        print("-" * 40)

alex_client_success_stories()

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def alex_lora_mastery_checklist():
    """Alex's complete LoRA expertise framework"""
    
    mastery_levels = {
        "Beginner": [
            "Understand LoRA concept and benefits",
            "Implement basic LoRA with Hugging Face PEFT",
            "Fine-tune for single task with default parameters",
            "Calculate cost savings vs full fine-tuning"
        ],
        
        "Intermediate": [
            "Optimize LoRA hyperparameters for specific tasks",
            "Create multi-task LoRA configurations",
            "Implement efficient data preprocessing pipelines",
            "Monitor and evaluate LoRA performance"
        ],
        
        "Advanced": [
            "Design custom LoRA architectures",
            "Implement LoRA merging and switching strategies",
            "Create production deployment pipelines",
            "Develop domain-specific LoRA methodologies"
        ],
        
        "Expert": [
            "Research novel LoRA applications",
            "Contribute to open-source LoRA tools",
            "Teach and mentor other LoRA practitioners",
            "Build successful LoRA-based business solutions"
        ]
    }
    
    print("ALEX'S LORA MASTERY FRAMEWORK")
    print("=" * 50)
    
    for level, skills in mastery_levels.items():
        print(f"\n{level} Level:")
        for skill in skills:
            print(f"  ✅ {skill}")
    
    # Alex's current status
    print(f"\n🎯 Alex's Current Level: Expert")
    print(f"🚀 Alex's Next Goal: LoRA research publication")

alex_lora_mastery_checklist()