AI Engineering: Building Applications with Foundation Models

# Pseudo-code for perplexity-based filtering
def filter_high_quality_data(dataset, model, threshold=50):
    filtered_data = []
    for sample in dataset:
        perplexity = model.compute_perplexity(sample)
        if perplexity < threshold:  # High quality = low perplexity
            filtered_data.append(sample)
    return filtered_data

def evaluate_code_generation(problem, generated_code):
    """
    Test functional correctness of generated code
    """
    try:
        # Execute generated code
        exec(generated_code)
        
        # Run test cases
        for test_input, expected_output in problem.test_cases:
            actual_output = solution(test_input)
            if actual_output != expected_output:
                return False
        return True
    except:
        return False

def update_elo_ratings(rating_a, rating_b, result, k=32):
    """
    Update Elo ratings based on comparison result
    result: 1 if A wins, 0 if B wins, 0.5 if tie
    """
    expected_a = 1 / (1 + 10**((rating_b - rating_a) / 400))
    expected_b = 1 - expected_a
    
    new_rating_a = rating_a + k * (result - expected_a)
    new_rating_b = rating_b + k * ((1 - result) - expected_b)
    
    return new_rating_a, new_rating_b

def evaluate_factual_consistency(context, response):
    """
    Multi-level factual consistency check
    """
    # Local consistency: sentence-level facts
    local_score = check_local_facts(context, response)
    
    # Global consistency: document-level coherence  
    global_score = check_global_coherence(context, response)
    
    # External consistency: world knowledge
    external_score = verify_against_knowledge_base(response)
    
    return {
        'local': local_score,
        'global': global_score, 
        'external': external_score,
        'overall': (local_score + global_score + external_score) / 3
    }

def calculate_tco(api_calls_per_month, api_cost_per_call, 
                  engineer_salary, infrastructure_cost):
    """
    Total Cost of Ownership comparison
    """
    # API approach
    api_monthly_cost = api_calls_per_month * api_cost_per_call
    api_annual_cost = api_monthly_cost * 12
    
    # Self-hosted approach
    engineering_cost = engineer_salary * 1.5  # Including benefits
    self_hosted_annual_cost = engineering_cost + infrastructure_cost
    
    return {
        'api_cost': api_annual_cost,
        'self_hosted_cost': self_hosted_annual_cost,
        'break_even_calls': self_hosted_annual_cost / (api_cost_per_call * 12)
    }

# Evaluate each component independently
components = {
    'retriever': evaluate_retrieval_accuracy,
    'generator': evaluate_generation_quality,
    'ranker': evaluate_ranking_performance
}

for component, evaluator in components.items():
    score = evaluator(test_data)
    print(f"{component}: {score}")

def design_evaluation_data(task_type, sample_size=1000):
    """
    Create balanced evaluation dataset
    """
    if task_type == "QA":
        return {
            'factual': generate_factual_qa(sample_size // 4),
            'reasoning': generate_reasoning_qa(sample_size // 4), 
            'opinion': generate_opinion_qa(sample_size // 4),
            'edge_cases': generate_edge_cases(sample_size // 4)
        }

def select_optimal_shots(task, examples, max_shots=5):
    """
    Select most informative examples for few-shot learning
    """
    # Diversity-based selection
    diverse_examples = select_diverse_examples(examples, max_shots//2)
    
    # Difficulty-based selection  
    challenging_examples = select_challenging_examples(examples, max_shots//2)
    
    return diverse_examples + challenging_examples

# Llama 2 Template
"<s>[INST] <<SYS>>\n{system_prompt}\n<</SYS>>\n\n{user_prompt} [/INST]"

# ChatML Template  
"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>"

# Alpaca Template
"### Instruction:\n{instruction}\n\n### Response:\n"

def complex_task_pipeline(input_data):
    """
    Break complex task into manageable steps
    """
    # Step 1: Information extraction
    extracted_info = simple_model.extract_key_facts(input_data)
    
    # Step 2: Analysis  
    analysis = reasoning_model.analyze(extracted_info)
    
    # Step 3: Synthesis
    final_output = generation_model.synthesize(analysis)
    
    return final_output

def evaluate_prompt_tool(tool, requirements):
    """
    Score prompt engineering tools
    """
    criteria = {
        'version_control': weight_importance(requirements, 'versioning'),
        'collaboration': weight_importance(requirements, 'team_work'),
        'testing': weight_importance(requirements, 'experimentation'),
        'monitoring': weight_importance(requirements, 'production_monitoring'),
        'integration': weight_importance(requirements, 'existing_tools')
    }
    
    return sum(score_tool_on_criterion(tool, criterion) * weight 
               for criterion, weight in criteria.items())

def sanitize_input(user_input):
    """
    Clean potentially malicious input
    """
    # Remove instruction-like patterns
    dangerous_patterns = [
        r"ignore.*previous.*instruction",
        r"system.*prompt",
        r"you are now",
        r"pretend.*to be"
    ]
    
    for pattern in dangerous_patterns:
        if re.search(pattern, user_input, re.IGNORECASE):
            return sanitize_or_reject(user_input)
    
    return user_input

def filter_output(response):
    """
    Check response for leaked information
    """
    sensitive_patterns = [
        r"system.*prompt",
        r"instruction.*ignore",
        r"<\|.*\|>",  # Template tokens
        r"###.*###"   # Special delimiters
    ]
    
    for pattern in sensitive_patterns:
        if re.search(pattern, response, re.IGNORECASE):
            return "I can't provide that information."
    
    return response

def build_rag_index(documents):
    """
    Create searchable index from documents
    """
    # Chunk documents
    chunks = [chunk_document(doc, chunk_size=512) 
              for doc in documents]
    
    # Generate embeddings
    embeddings = [embedding_model.encode(chunk) 
                  for chunk in chunks]
    
    # Build vector index
    index = VectorIndex()
    index.add(embeddings, chunks)
    
    return index

def retrieve_context(query, index, top_k=5):
    """
    Retrieve relevant context for query
    """
    # Encode query
    query_embedding = embedding_model.encode(query)
    
    # Search similar chunks
    results = index.search(query_embedding, top_k=top_k)
    
    # Rank and filter
    ranked_results = rerank_results(query, results)
    
    return ranked_results[:top_k]

def compute_tfidf(term, document, corpus):
    """
    Term Frequency - Inverse Document Frequency
    """
    tf = document.count(term) / len(document)
    idf = math.log(len(corpus) / sum(1 for doc in corpus if term in doc))
    return tf * idf

def hybrid_retrieval(query, term_index, vector_index, alpha=0.5):
    """
    Combine term-based and vector-based retrieval
    """
    # Term-based results
    term_results = bm25_search(query, term_index)
    
    # Vector-based results  
    vector_results = vector_search(query, vector_index)
    
    # Combine scores
    combined_results = []
    for doc_id in set(term_results.keys()) | set(vector_results.keys()):
        term_score = term_results.get(doc_id, 0)
        vector_score = vector_results.get(doc_id, 0)
        
        combined_score = alpha * term_score + (1 - alpha) * vector_score
        combined_results.append((doc_id, combined_score))
    
    return sorted(combined_results, key=lambda x: x[1], reverse=True)

def semantic_chunking(document, max_chunk_size=512):
    """
    Create semantically coherent chunks
    """
    sentences = split_into_sentences(document)
    chunks = []
    current_chunk = []
    current_size = 0
    
    for sentence in sentences:
        sentence_size = len(tokenize(sentence))
        
        if current_size + sentence_size > max_chunk_size and current_chunk:
            # Check semantic coherence
            if is_semantically_coherent(current_chunk):
                chunks.append(' '.join(current_chunk))
                current_chunk = [sentence]
                current_size = sentence_size
            else:
                # Add sentence to maintain coherence
                current_chunk.append(sentence)
                current_size += sentence_size
        else:
            current_chunk.append(sentence)
            current_size += sentence_size
    
    if current_chunk:
        chunks.append(' '.join(current_chunk))
    
    return chunks

def expand_query(original_query):
    """
    Enhance query for better retrieval
    """
    expansions = []
    
    # Synonym expansion
    synonyms = get_synonyms(original_query)
    expansions.extend(synonyms)
    
    # Domain-specific expansion
    domain_terms = get_domain_terms(original_query)
    expansions.extend(domain_terms)
    
    # Question reformulation
    reformulated = reformulate_question(original_query)
    expansions.append(reformulated)
    
    return f"{original_query} {' '.join(expansions)}"

def rerank_results(query, initial_results, reranker_model):
    """
    Rerank initial results using cross-encoder
    """
    # Score query-document pairs
    scores = []
    for doc in initial_results:
        input_pair = f"{query} [SEP] {doc['content']}"
        score = reranker_model.predict(input_pair)
        scores.append((doc, score))
    
    # Sort by relevance score
    reranked = sorted(scores, key=lambda x: x[1], reverse=True)
    
    return [doc for doc, score in reranked]

def multimodal_rag(query, text_index, image_index):
    """
    Retrieve from both text and image sources
    """
    # Determine modalities needed
    modalities = detect_required_modalities(query)
    
    results = {}
    
    if 'text' in modalities:
        text_results = text_index.search(query)
        results['text'] = text_results
    
    if 'image' in modalities:
        image_results = image_index.search(query)
        results['image'] = image_results
    
    # Combine and contextualize
    combined_context = combine_multimodal_context(results)
    
    return combined_context

def process_tabular_data(table, query):
    """
    Convert tables to searchable text format
    """
    # Schema understanding
    schema_description = describe_table_schema(table)
    
    # Row-wise text representation
    text_rows = []
    for row in table.rows:
        row_text = convert_row_to_text(row, table.headers)
        text_rows.append(row_text)
    
    # Query-relevant filtering
    relevant_rows = filter_relevant_rows(text_rows, query)
    
    return {
        'schema': schema_description,
        'relevant_data': relevant_rows
    }

def plan_task_execution(task, available_tools):
    """
    Generate step-by-step plan for complex task
    """
    # Decompose task
    subtasks = decompose_task(task)
    
    # Plan for each subtask
    plan = []
    for subtask in subtasks:
        # Select appropriate tools
        required_tools = select_tools(subtask, available_tools)
        
        # Create action sequence
        actions = create_action_sequence(subtask, required_tools)
        plan.extend(actions)
    
    return plan

class ToolRegistry:
    def __init__(self):
        self.tools = {}
    
    def register_tool(self, name, tool_func, description):
        """Register a new tool with the agent"""
        self.tools[name] = {
            'function': tool_func,
            'description': description,
            'usage_count': 0
        }
    
    def execute_tool(self, tool_name, *args, **kwargs):
        """Execute a tool and track usage"""
        if tool_name not in self.tools:
            raise ValueError(f"Tool {tool_name} not found")
        
        result = self.tools[tool_name]['function'](*args, **kwargs)
        self.tools[tool_name]['usage_count'] += 1
        
        return result

def hierarchical_planning(goal, depth=3):
    """
    Break down goals into hierarchical subgoals
    """
    if depth == 0 or is_primitive_action(goal):
        return [goal]
    
    # Decompose into subgoals
    subgoals = decompose_goal(goal)
    
    # Recursively plan for each subgoal
    plan = []
    for subgoal in subgoals:
        subplan = hierarchical_planning(subgoal, depth - 1)
        plan.extend(subplan)
    
    return plan

def reactive_planning(current_state, goal, max_steps=10):
    """
    Plan step-by-step based on current state
    """
    plan = []
    state = current_state
    
    for step in range(max_steps):
        if is_goal_achieved(state, goal):
            break
        
        # Select next action based on current state
        action = select_next_action(state, goal)
        plan.append(action)
        
        # Simulate action effect
        state = simulate_action(state, action)
    
    return plan

def execute_with_reflection(plan, max_retries=3):
    """
    Execute plan with reflection and correction
    """
    for attempt in range(max_retries):
        try:
            # Execute plan
            result = execute_plan(plan)
            
            # Reflect on result
            issues = identify_issues(result)
            
            if not issues:
                return result
            
            # Correct plan based on issues
            plan = correct_plan(plan, issues)
            
        except Exception as e:
            # Handle execution errors
            plan = handle_execution_error(plan, e)
    
    raise Exception(f"Failed to complete task after {max_retries} attempts")

def detect_failure_modes(execution_trace):
    """
    Analyze execution trace for common failures
    """
    failures = []
    
    # Check for loops
    if has_repeated_actions(execution_trace):
        failures.append("infinite_loop")
    
    # Check for tool misuse
    if has_inappropriate_tool_usage(execution_trace):
        failures.append("tool_misuse")
    
    # Check for progress
    if not making_progress(execution_trace):
        failures.append("no_progress")
    
    return failures

class AgentMemory:
    def __init__(self):
        self.working_memory = {}
        self.short_term = deque(maxlen=100)
        self.long_term = VectorDatabase()
    
    def store_interaction(self, interaction):
        """Store interaction across memory levels"""
        # Working memory (immediate access)
        self.working_memory['last_interaction'] = interaction
        
        # Short-term memory (session context)
        self.short_term.append(interaction)
        
        # Long-term memory (persistent storage)
        if is_important(interaction):
            embedding = embed_interaction(interaction)
            self.long_term.add(embedding, interaction)
    
    def retrieve_relevant_memory(self, query):
        """Retrieve relevant memories for current task"""
        # Search long-term memory
        relevant_memories = self.long_term.search(query, top_k=5)
        
        # Combine with recent short-term memory
        recent_context = list(self.short_term)[-10:]
        
        return relevant_memories + recent_context

def resolve_memory_conflicts(conflicting_memories):
    """
    Handle conflicting information in memory
    """
    # Timestamp-based resolution
    latest_memory = max(conflicting_memories, key=lambda x: x.timestamp)
    
    # Source reliability scoring
    reliable_memories = [m for m in conflicting_memories 
                        if m.source_reliability > 0.8]
    
    if reliable_memories:
        return reliable_memories[0]
    else:
        return latest_memory

# Before Finetuning: Inconsistent JSON
{"name": "John", "age": 30}
{"person_name": "Jane", "person_age": "25"}
{"full_name": "Bob", "years_old": 35}

# After Finetuning: Consistent Structure
{"name": "John", "age": 30}
{"name": "Jane", "age": 25} 
{"name": "Bob", "age": 35}

def rag_plus_finetuning(query, knowledge_base, finetuned_model):
    """
    Combine RAG and finetuning for optimal performance
    """
    # RAG for current information
    relevant_context = retrieve_context(query, knowledge_base)
    
    # Finetuned model for consistent format/style
    enhanced_prompt = f"""
    Context: {relevant_context}
    Query: {query}
    
    Please provide a response in our standard format.
    """
    
    response = finetuned_model.generate(enhanced_prompt)
    return response

def calculate_memory_savings(num_params, trainable_ratio, precision_bits):
    """
    Calculate memory reduction from PEFT
    """
    base_memory = num_params * (precision_bits // 8)  # Model weights
    gradient_memory = num_params * trainable_ratio * (precision_bits // 8)
    optimizer_memory = num_params * trainable_ratio * 8  # AdamW states (FP32)
    
    total_training_memory = base_memory + gradient_memory + optimizer_memory
    
    return {
        'base_memory_gb': base_memory / 1e9,
        'training_memory_gb': total_training_memory / 1e9,
        'reduction_factor': (base_memory * 3) / total_training_memory
    }

# Example: LoRA with 1% trainable parameters
savings = calculate_memory_savings(7e9, 0.01, 16)
print(f"Memory reduction: {savings['reduction_factor']:.1f}x")

def quantize_model_weights(model, target_bits=8):
    """
    Quantize model weights to lower precision
    """
    for layer in model.layers:
        weights = layer.weight.data
        
        # Calculate scale factor
        max_val = weights.abs().max()
        scale = max_val / (2**(target_bits-1) - 1)
        
        # Quantize
        quantized = torch.round(weights / scale).clamp(
            -(2**(target_bits-1)), 2**(target_bits-1) - 1
        )
        
        # Store quantized weights and scale
        layer.weight.data = quantized
        layer.scale = scale

def quantization_aware_training_step(model, batch, optimizer):
    """
    Training step with quantization simulation
    """
    # Forward pass with simulated quantization
    fake_quantized_weights = simulate_quantization(model.parameters())
    output = model.forward_with_weights(batch, fake_quantized_weights)
    
    # Backward pass on full precision
    loss = compute_loss(output, batch.labels)
    loss.backward()
    
    # Update full precision weights
    optimizer.step()
    optimizer.zero_grad()

class LoRALayer(nn.Module):
    def __init__(self, in_features, out_features, rank=16, alpha=32):
        super().__init__()
        self.rank = rank
        self.alpha = alpha
        
        # Original frozen weight
        self.weight = nn.Parameter(torch.randn(out_features, in_features))
        self.weight.requires_grad = False
        
        # LoRA matrices
        self.lora_A = nn.Parameter(torch.randn(rank, in_features))
        self.lora_B = nn.Parameter(torch.zeros(out_features, rank))
        
        # Scaling factor
        self.scaling = alpha / rank
    
    def forward(self, x):
        # Original transformation
        base_output = F.linear(x, self.weight)
        
        # LoRA adaptation
        lora_output = F.linear(F.linear(x, self.lora_A.T), self.lora_B.T)
        
        return base_output + lora_output * self.scaling

def select_lora_matrices(model_type, performance_budget):
    """
    Choose which matrices to apply LoRA to
    """
    if performance_budget == "low":
        return ["query", "value"]  # Most effective matrices
    elif performance_budget == "medium":
        return ["query", "key", "value", "output"]  # All attention
    else:
        return ["query", "key", "value", "output", "ffn"]  # All matrices

class QLoRAModel:
    def __init__(self, base_model_path, rank=64):
        # Load base model in 4-bit
        self.base_model = load_4bit_model(base_model_path)
        
        # Add LoRA adapters in FP16
        self.lora_adapters = add_lora_adapters(
            self.base_model, 
            rank=rank,
            dtype=torch.float16
        )
    
    def forward(self, inputs):
        # Dequantize for computation
        fp16_weights = dequantize_weights(self.base_model.weights)
        
        # Forward pass with LoRA
        base_output = self.base_model(inputs, weights=fp16_weights)
        lora_output = self.lora_adapters(inputs)
        
        return base_output + lora_output

def merge_models_linear(models, weights):
    """
    Merge models using weighted average
    """
    merged_params = {}
    
    for param_name in models[0].state_dict():
        merged_param = torch.zeros_like(models[0].state_dict()[param_name])
        
        for model, weight in zip(models, weights):
            merged_param += weight * model.state_dict()[param_name]
        
        merged_params[param_name] = merged_param
    
    return merged_params

def task_arithmetic_merge(base_model, task_models, coefficients):
    """
    Merge using task vector arithmetic
    """
    merged_params = base_model.state_dict().copy()
    
    for param_name in merged_params:
        for task_model, coeff in zip(task_models, coefficients):
            # Compute task vector
            task_vector = (task_model.state_dict()[param_name] - 
                          base_model.state_dict()[param_name])
            
            # Add scaled task vector
            merged_params[param_name] += coeff * task_vector
    
    return merged_params

def stack_layers(models, stacking_strategy="interleave"):
    """
    Combine models by stacking layers
    """
    if stacking_strategy == "interleave":
        # Alternate layers from different models
        merged_layers = []
        max_layers = max(len(model.layers) for model in models)
        
        for i in range(max_layers):
            for model in models:
                if i < len(model.layers):
                    merged_layers.append(model.layers[i])
        
        return create_model_from_layers(merged_layers)

def multi_task_training_step(model, task_batches, task_weights):
    """
    Single training step across multiple tasks
    """
    total_loss = 0
    optimizer.zero_grad()
    
    for task_name, batch in task_batches.items():
        # Task-specific forward pass
        output = model(batch.inputs, task=task_name)
        loss = compute_task_loss(output, batch.labels, task_name)
        
        # Weight task loss
        weighted_loss = task_weights[task_name] * loss
        weighted_loss.backward()
        
        total_loss += weighted_loss.item()
    
    optimizer.step()
    return total_loss

def suggest_learning_rate(model_size, task_type, data_size):
    """
    Suggest appropriate learning rate
    """
    base_lr = {
        "small": 5e-4,
        "medium": 1e-4, 
        "large": 5e-5,
        "xl": 1e-5
    }[model_size]
    
    # Adjust for task type
    if task_type == "instruction_following":
        base_lr *= 0.5  # More conservative
    elif task_type == "domain_adaptation":
        base_lr *= 2.0  # More aggressive
    
    # Adjust for data size
    if data_size < 1000:
        base_lr *= 0.5  # Prevent overfitting
    
    return base_lr

def should_stop_training(metrics_history, patience=3):
    """
    Determine if training should stop early
    """
    if len(metrics_history) < patience + 1:
        return False
    
    recent_metrics = metrics_history[-patience:]
    best_metric = max(metrics_history[:-patience])
    
    # Check if recent performance is worse
    if all(metric < best_metric for metric in recent_metrics):
        return True
    
    return False

def curriculum_learning_scheduler(epoch, total_epochs):
    """
    Schedule data difficulty progression
    """
    # Start with easy examples
    if epoch < total_epochs * 0.3:
        return "easy"
    elif epoch < total_epochs * 0.7:
        return "medium"
    else:
        return "hard"

def layer_wise_unfreezing(model, current_epoch, unfreeze_schedule):
    """
    Gradually unfreeze layers during training
    """
    layers_to_unfreeze = unfreeze_schedule.get(current_epoch, [])
    
    for layer_name in layers_to_unfreeze:
        layer = getattr(model, layer_name)
        for param in layer.parameters():
            param.requires_grad = True

def assess_data_quality(dataset):
    """
    Comprehensive data quality assessment
    """
    quality_scores = {}
    
    # Alignment check
    quality_scores['alignment'] = check_task_alignment(dataset)
    
    # Consistency check
    quality_scores['consistency'] = measure_annotation_consistency(dataset)
    
    # Format validation
    quality_scores['format'] = validate_format_compliance(dataset)
    
    # Uniqueness analysis
    quality_scores['uniqueness'] = calculate_duplication_rate(dataset)
    
    # Compliance verification
    quality_scores['compliance'] = verify_policy_compliance(dataset)
    
    return quality_scores

def measure_data_coverage(dataset, target_distribution):
    """
    Assess how well data covers target distribution
    """
    coverage_metrics = {}
    
    # Topic coverage
    dataset_topics = extract_topics(dataset)
    target_topics = target_distribution['topics']
    coverage_metrics['topic_coverage'] = calculate_overlap(dataset_topics, target_topics)
    
    # Complexity coverage  
    complexity_dist = analyze_complexity_distribution(dataset)
    target_complexity = target_distribution['complexity']
    coverage_metrics['complexity_coverage'] = compare_distributions(
        complexity_dist, target_complexity
    )
    
    # Language coverage
    lang_dist = detect_languages(dataset)
    target_languages = target_distribution['languages']
    coverage_metrics['language_coverage'] = calculate_language_overlap(
        lang_dist, target_languages
    )
    
    return coverage_metrics

def calculate_agreement_metrics(annotations_1, annotations_2, task_type):
    """
    Calculate various agreement metrics
    """
    if task_type == "classification":
        # Cohen's Kappa for classification
        kappa = cohen_kappa_score(annotations_1, annotations_2)
        accuracy = accuracy_score(annotations_1, annotations_2)
        
        return {
            'kappa': kappa,
            'accuracy': accuracy,
            'interpretation': interpret_kappa(kappa)
        }
    
    elif task_type == "text_similarity":
        # Pearson correlation for continuous scores
        correlation = pearsonr(annotations_1, annotations_2)[0]
        
        return {
            'correlation': correlation,
            'interpretation': interpret_correlation(correlation)
        }

def rule_based_augmentation(text):
    """
    Apply various text augmentation techniques
    """
    augmented_samples = []
    
    # Synonym replacement
    synonyms = replace_with_synonyms(text, num_replacements=2)
    augmented_samples.append(synonyms)
    
    # Random deletion
    deleted = random_deletion(text, deletion_rate=0.1)
    augmented_samples.append(deleted)
    
    # Random insertion
    inserted = random_insertion(text, insertion_rate=0.1)
    augmented_samples.append(inserted)
    
    # Random swap
    swapped = random_swap(text, swap_rate=0.1)
    augmented_samples.append(swapped)
    
    return augmented_samples

def simulate_user_behavior(base_scenarios, num_variations=1000):
    """
    Generate synthetic user interaction data
    """
    simulated_data = []
    
    for scenario in base_scenarios:
        for _ in range(num_variations):
            # Vary user attributes
            user_profile = sample_user_profile()
            
            # Simulate interaction
            interaction = simulate_interaction(scenario, user_profile)
            
            # Add realistic noise
            noisy_interaction = add_realistic_noise(interaction)
            
            simulated_data.append(noisy_interaction)
    
    return simulated_data

def generate_instruction_data(seed_examples, target_count=10000):
    """
    Generate instruction-following training data
    """
    generated_data = []
    
    for _ in range(target_count):
        # Sample seed example
        seed = random.choice(seed_examples)
        
        # Generate variations
        instruction_variants = generate_instruction_variants(seed['instruction'])
        
        for instruction in instruction_variants:
            # Generate response
            response = generate_response(instruction, seed['context'])
            
            # Verify quality
            if meets_quality_criteria(instruction, response):
                generated_data.append({
                    'instruction': instruction,
                    'response': response,
                    'source': 'synthetic'
                })
    
    return generated_data

def score_synthetic_data(instruction, response):
    """
    Multi-dimensional quality scoring
    """
    scores = {}
    
    # Instruction clarity
    scores['clarity'] = score_instruction_clarity(instruction)
    
    # Response relevance
    scores['relevance'] = score_response_relevance(instruction, response)
    
    # Factual accuracy
    scores['accuracy'] = verify_factual_accuracy(response)
    
    # Diversity check
    scores['diversity'] = measure_diversity_contribution(instruction, existing_dataset)
    
    # Overall score
    scores['overall'] = weighted_average(scores, weights={
        'clarity': 0.25,
        'relevance': 0.35,
        'accuracy': 0.25,
        'diversity': 0.15
    })
    
    return scores

def comprehensive_data_analysis(dataset):
    """
    Perform comprehensive dataset analysis
    """
    analysis = {}
    
    # Basic statistics
    analysis['basic_stats'] = {
        'total_examples': len(dataset),
        'avg_input_length': np.mean([len(x['input']) for x in dataset]),
        'avg_output_length': np.mean([len(x['output']) for x in dataset])
    }
    
    # Length distributions
    input_lengths = [len(tokenize(x['input'])) for x in dataset]
    output_lengths = [len(tokenize(x['output'])) for x in dataset]
    
    analysis['length_distribution'] = {
        'input_stats': {
            'min': min(input_lengths),
            'max': max(input_lengths),
            'median': np.median(input_lengths),
            'std': np.std(input_lengths)
        },
        'output_stats': {
            'min': min(output_lengths),
            'max': max(output_lengths),
            'median': np.median(output_lengths),
            'std': np.std(output_lengths)
        }
    }
    
    # Content analysis
    analysis['content_analysis'] = {
        'language_distribution': detect_language_distribution(dataset),
        'topic_distribution': extract_topic_distribution(dataset),
        'complexity_distribution': analyze_complexity_distribution(dataset)
    }
    
    return analysis

def exact_deduplication(dataset):
    """
    Remove exact duplicates
    """
    seen = set()
    deduplicated = []
    
    for example in dataset:
        # Create hash of content
        content_hash = hash(example['input'] + example['output'])
        
        if content_hash not in seen:
            seen.add(content_hash)
            deduplicated.append(example)
    
    return deduplicated

def fuzzy_deduplication(dataset, similarity_threshold=0.85):
    """
    Remove near-duplicates using similarity scoring
    """
    embeddings = [embed_text(example['input']) for example in dataset]
    
    keep_indices = []
    for i, embedding in enumerate(embeddings):
        is_duplicate = False
        
        for j in keep_indices:
            similarity = cosine_similarity(embedding, embeddings[j])
            if similarity > similarity_threshold:
                is_duplicate = True
                break
        
        if not is_duplicate:
            keep_indices.append(i)
    
    return [dataset[i] for i in keep_indices]

def apply_quality_filters(dataset):
    """
    Apply comprehensive quality filtering
    """
    filtered_data = []
    
    for example in dataset:
        # Length filters
        if not is_reasonable_length(example):
            continue
        
        # Language detection
        if not is_target_language(example):
            continue
        
        # Content quality
        if not meets_content_standards(example):
            continue
        
        # Format validation
        if not is_properly_formatted(example):
            continue
        
        filtered_data.append(example)
    
    return filtered_data

def is_reasonable_length(example, min_tokens=5, max_tokens=2048):
    """Check if example has reasonable length"""
    input_length = len(tokenize(example['input']))
    output_length = len(tokenize(example['output']))
    
    return (min_tokens <= input_length <= max_tokens and 
            min_tokens <= output_length <= max_tokens)

def standardize_format(dataset, target_format="alpaca"):
    """
    Convert data to standardized format
    """
    standardized = []
    
    for example in dataset:
        if target_format == "alpaca":
            formatted = {
                'instruction': example['input'],
                'input': example.get('context', ''),
                'output': example['output']
            }
        elif target_format == "sharegpt":
            formatted = {
                'conversations': [
                    {'from': 'human', 'value': example['input']},
                    {'from': 'gpt', 'value': example['output']}
                ]
            }
        
        standardized.append(formatted)
    
    return standardized

def ensure_template_consistency(dataset, template):
    """
    Ensure all examples follow the same template
    """
    consistent_data = []
    
    for example in dataset:
        # Parse existing format
        parsed = parse_example(example)
        
        # Apply template
        formatted = template.format(
            instruction=parsed['instruction'],
            input=parsed.get('input', ''),
            output=parsed['output']
        )
        
        consistent_data.append(formatted)
    
    return consistent_data

def identify_bottleneck(model, hardware_spec):
    """
    Determine primary performance bottleneck
    """
    # Calculate arithmetic intensity
    flops_per_token = calculate_model_flops(model)
    bytes_per_token = calculate_memory_access(model)
    arithmetic_intensity = flops_per_token / bytes_per_token
    
    # Hardware roof line analysis
    peak_flops = hardware_spec['peak_flops']
    memory_bandwidth = hardware_spec['memory_bandwidth']
    roofline_threshold = peak_flops / memory_bandwidth
    
    if arithmetic_intensity > roofline_threshold:
        return "memory_bound"
    else:
        return "compute_bound"

def calculate_throughput_metrics(inference_logs):
    """
    Calculate various throughput metrics
    """
    total_tokens = sum(log['output_tokens'] for log in inference_logs)
    total_time = max(log['end_time'] for log in inference_logs) - min(log['start_time'] for log in inference_logs)
    total_requests = len(inference_logs)
    
    return {
        'tokens_per_second': total_tokens / total_time,
        'requests_per_second': total_requests / total_time,
        'average_tokens_per_request': total_tokens / total_requests
    }

def calculate_utilization(model_config, hardware_spec, measured_throughput):
    """
    Calculate MFU and MBU
    """
    # Model FLOPs per token
    model_flops = 2 * model_config['parameters']  # Simplified
    actual_flops_per_second = model_flops * measured_throughput
    mfu = actual_flops_per_second / hardware_spec['peak_flops']
    
    # Memory bandwidth utilization
    model_bytes = model_config['parameters'] * model_config['precision_bytes']
    actual_bandwidth = model_bytes * measured_throughput
    mbu = actual_bandwidth / hardware_spec['memory_bandwidth']
    
    return {'mfu': mfu, 'mbu': mbu}

def optimize_memory_usage(model, batch_size, sequence_length):
    """
    Optimize memory allocation across hierarchy
    """
    # Calculate memory requirements
    model_memory = calculate_model_memory(model)
    activation_memory = calculate_activation_memory(batch_size, sequence_length)
    kv_cache_memory = calculate_kv_cache(batch_size, sequence_length)
    
    total_memory = model_memory + activation_memory + kv_cache_memory
    
    # Memory placement strategy
    if total_memory <= GPU_HBM_SIZE:
        placement = "all_gpu"
    elif model_memory <= GPU_HBM_SIZE:
        placement = "model_gpu_activations_cpu"
    else:
        placement = "offload_layers"
    
    return placement

class OptimizedKVCache:
    def __init__(self, max_batch_size, max_sequence_length, num_heads, head_dim):
        self.max_batch_size = max_batch_size
        self.max_seq_len = max_sequence_length
        
        # Pre-allocate cache
        cache_shape = (max_batch_size, num_heads, max_sequence_length, head_dim)
        self.k_cache = torch.zeros(cache_shape, dtype=torch.float16)
        self.v_cache = torch.zeros(cache_shape, dtype=torch.float16)
        
        # Track current positions
        self.current_length = torch.zeros(max_batch_size, dtype=torch.int32)
    
    def update(self, batch_idx, new_k, new_v):
        """Update cache with new key-value pairs"""
        current_pos = self.current_length[batch_idx]
        
        # Update cache at current position
        self.k_cache[batch_idx, :, current_pos] = new_k
        self.v_cache[batch_idx, :, current_pos] = new_v
        
        # Increment position
        self.current_length[batch_idx] += 1
    
    def get_kv(self, batch_idx):
        """Retrieve current key-value pairs"""
        length = self.current_length[batch_idx]
        return (
            self.k_cache[batch_idx, :, :length],
            self.v_cache[batch_idx, :, :length]
        )

def multi_query_attention(query, key, value, num_kv_heads, num_q_heads):
    """
    Implement multi-query attention for efficiency
    """
    # Reshape for grouped attention
    batch_size, seq_len, d_model = query.shape
    head_dim = d_model // num_q_heads
    
    # Reshape query
    q = query.view(batch_size, seq_len, num_q_heads, head_dim)
    
    # Key/Value shared across query heads
    k = key.view(batch_size, seq_len, num_kv_heads, head_dim)
    v = value.view(batch_size, seq_len, num_kv_heads, head_dim)
    
    # Repeat K,V for each query head group
    heads_per_kv = num_q_heads // num_kv_heads
    k = k.repeat_interleave(heads_per_kv, dim=2)
    v = v.repeat_interleave(heads_per_kv, dim=2)
    
    # Standard attention computation
    scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(head_dim)
    attention_weights = torch.softmax(scores, dim=-1)
    output = torch.matmul(attention_weights, v)
    
    return output.view(batch_size, seq_len, d_model)

def speculative_decoding(draft_model, target_model, prompt, k=4):
    """
    Implement speculative decoding for faster generation
    """
    tokens = tokenize(prompt)
    
    while len(tokens) < max_length:
        # Draft model generates k tokens quickly
        draft_tokens = draft_model.generate(tokens, num_tokens=k)
        
        # Target model evaluates all k+1 positions in parallel
        target_logits = target_model.get_logits(tokens + draft_tokens)
        draft_logits = draft_model.get_logits(tokens + draft_tokens[:-1])
        
        # Accept/reject each token
        accepted_tokens = []
        for i in range(k):
            acceptance_prob = min(1.0, 
                target_logits[i][draft_tokens[i]] / draft_logits[i][draft_tokens[i]]
            )
            
            if random.random() < acceptance_prob:
                accepted_tokens.append(draft_tokens[i])
            else:
                # Reject and resample from target distribution
                adjusted_probs = adjust_distribution(target_logits[i], draft_logits[i])
                new_token = sample_from_distribution(adjusted_probs)
                accepted_tokens.append(new_token)
                break
        
        tokens.extend(accepted_tokens)
    
    return tokens

def parallel_decoding(model, prompts, max_new_tokens=100):
    """
    Decode multiple sequences in parallel with different lengths
    """
    batch_size = len(prompts)
    
    # Tokenize and pad prompts
    tokenized_prompts = [tokenize(prompt) for prompt in prompts]
    max_input_length = max(len(tokens) for tokens in tokenized_prompts)
    
    # Create attention masks
    attention_masks = create_attention_masks(tokenized_prompts, max_input_length)
    
    # Parallel generation
    for step in range(max_new_tokens):
        # Forward pass for all sequences
        logits = model.forward_batch(tokenized_prompts, attention_masks)
        
        # Sample next tokens
        next_tokens = sample_next_tokens(logits)
        
        # Update sequences (handle different completion times)
        for i, (tokens, next_token) in enumerate(zip(tokenized_prompts, next_tokens)):
            if not is_finished(tokens):
                tokens.append(next_token)
                attention_masks[i] = update_attention_mask(attention_masks[i])
        
        # Check if all sequences are complete
        if all(is_finished(tokens) for tokens in tokenized_prompts):
            break
    
    return [detokenize(tokens) for tokens in tokenized_prompts]

def dynamic_quantization(model, calibration_data):
    """
    Apply dynamic quantization based on activation statistics
    """
    # Collect activation statistics
    activation_stats = {}
    
    with torch.no_grad():
        for batch in calibration_data:
            _ = model(batch)
            
            # Record min/max for each layer
            for name, module in model.named_modules():
                if hasattr(module, 'activation_stats'):
                    stats = module.activation_stats
                    if name not in activation_stats:
                        activation_stats[name] = {'min': stats.min(), 'max': stats.max()}
                    else:
                        activation_stats[name]['min'] = min(activation_stats[name]['min'], stats.min())
                        activation_stats[name]['max'] = max(activation_stats[name]['max'], stats.max())
    
    # Apply quantization based on statistics
    for name, module in model.named_modules():
        if name in activation_stats:
            stats = activation_stats[name]
            module.quantization_scale = (stats['max'] - stats['min']) / 255
            module.quantization_zero_point = stats['min']
    
    return model

# Using Triton for custom kernels
import triton
import triton.language as tl

@triton.jit
def fused_attention_kernel(
    Q, K, V, Out,
    stride_qz, stride_qh, stride_qm, stride_qk,
    stride_kz, stride_kh, stride_kn, stride_kk,
    stride_vz, stride_vh, stride_vn, stride_vk,
    stride_oz, stride_oh, stride_om, stride_on,
    Z, H, N_CTX, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr
):
    """
    Fused attention kernel for better memory efficiency
    """
    # Program IDs
    start_m = tl.program_id(0)
    off_hz = tl.program_id(1)
    
    # Compute attention for this block
    offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
    offs_n = tl.arange(0, BLOCK_N)
    
    # Load Q block
    q_ptrs = Q + off_hz * stride_qh + offs_m[:, None] * stride_qm + tl.arange(0, BLOCK_M)[:, None] * stride_qk
    q = tl.load(q_ptrs)
    
    # Initialize output accumulator
    acc = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
    
    # Compute attention scores and apply softmax
    for start_n in range(0, N_CTX, BLOCK_N):
        offs_n = start_n + tl.arange(0, BLOCK_N)
        
        # Load K, V blocks
        k_ptrs = K + off_hz * stride_kh + offs_n[None, :] * stride_kn + tl.arange(0, BLOCK_M)[None, :] * stride_kk
        v_ptrs = V + off_hz * stride_vh + offs_n[:, None] * stride_vn + tl.arange(0, BLOCK_N)[:, None] * stride_vk
        
        k = tl.load(k_ptrs)
        v = tl.load(v_ptrs)
        
        # Compute attention scores
        qk = tl.dot(q, k.T)
        
        # Apply softmax
        weights = tl.softmax(qk, axis=1)
        
        # Accumulate weighted values
        acc += tl.dot(weights, v)
    
    # Store output
    o_ptrs = Out + off_hz * stride_oh + offs_m[:, None] * stride_om + tl.arange(0, BLOCK_N)[None, :] * stride_on
    tl.store(o_ptrs, acc)

class DynamicBatcher:
    def __init__(self, max_batch_size=32, max_wait_time=10):
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
        self.pending_requests = []
        
    async def add_request(self, request):
        """Add request to batch queue"""
        self.pending_requests.append(request)
        
        # Process batch if full or timeout
        if (len(self.pending_requests) >= self.max_batch_size or 
            self._should_process_batch()):
            await self._process_batch()
    
    def _should_process_batch(self):
        """Determine if batch should be processed"""
        if not self.pending_requests:
            return False
            
        oldest_request_time = self.pending_requests[0].timestamp
        return (time.time() - oldest_request_time) > self.max_wait_time
    
    async def _process_batch(self):
        """Process current batch of requests"""
        if not self.pending_requests:
            return
            
        batch = self.pending_requests[:self.max_batch_size]
        self.pending_requests = self.pending_requests[self.max_batch_size:]
        
        # Group by similar sequence lengths for efficiency
        grouped_batches = self._group_by_length(batch)
        
        for length_batch in grouped_batches:
            await self._inference_batch(length_batch)

class ContinuousBatcher:
    def __init__(self, model, max_batch_size=64):
        self.model = model
        self.max_batch_size = max_batch_size
        self.active_sequences = {}
        
    async def add_sequence(self, sequence_id, prompt):
        """Add new sequence to continuous batch"""
        self.active_sequences[sequence_id] = {
            'tokens': tokenize(prompt),
            'position': 0,
            'completed': False
        }
        
        # Trigger processing if space available
        if len(self.active_sequences) <= self.max_batch_size:
            await self._process_step()
    
    async def _process_step(self):
        """Process one generation step for all active sequences"""
        if not self.active_sequences:
            return
            
        # Prepare batch
        batch_tokens = []
        batch_positions = []
        active_ids = []
        
        for seq_id, seq_data in self.active_sequences.items():
            if not seq_data['completed']:
                batch_tokens.append(seq_data['tokens'])
                batch_positions.append(seq_data['position'])
                active_ids.append(seq_id)
        
        if not batch_tokens:
            return
            
        # Run inference
        next_tokens = await self.model.generate_batch(batch_tokens, batch_positions)
        
        # Update sequences
        for seq_id, next_token in zip(active_ids, next_tokens):
            self.active_sequences[seq_id]['tokens'].append(next_token)
            self.active_sequences[seq_id]['position'] += 1
            
            # Check completion
            if self._is_complete(next_token):
                self.active_sequences[seq_id]['completed'] = True
        
        # Remove completed sequences
        self._cleanup_completed()
        
        # Continue processing
        if self.active_sequences:
            await self._process_step()