Add docs/DATA_PREPARATION_STRATEGY.md: Comprehensive data preparation strategy document

docs/DATA_PREPARATION_STRATEGY.md

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+# Sheikh-2.5-Coder Data Preparation Strategy
+
+**Author:** MiniMax Agent  
+**Date:** 2025-11-06  
+**Model:** Sheikh-2.5-Coder (3.09B parameters)  
+**Target:** On-device deployment with XML/MDX/JavaScript specialization  
+
+---
+
+## 1. Executive Summary (Six Thinking Hats Synthesis)
+
+### White Hat (Facts & Data)
+Sheikh-2.5-Coder is a 3.09B parameter code language model (2.77B non-embedding parameters, 36 layers, GQA with 16Q/2KV heads, 32K context length) optimized for on-device deployment. Current research establishes five key data sources: The Stack v2 (67.5TB, 900B tokens), OpenCodeInstruct (instruction-following with unit tests), CodeSearchNet (code-comment pairs), synthetic generation methods, and comprehensive preprocessing pipelines using CodeBERT tokenization and MinHash deduplication.
+
+### Red Hat (Intuition & Emotions)
+The development team feels confident about the technical architecture but concerned about data quality at scale. There's excitement about XML/MDX/JavaScript specialization potential but anxiety about 6-12GB memory constraints affecting model capacity. The parallel thinking analysis reveals optimism about on-device capabilities but realistic concerns about training efficiency.
+
+### Black Hat (Risks & Cautions)
+**Critical Risks:**
+- Data quality degradation from synthetic generation at scale
+- On-device memory constraints limiting model expressiveness
+- XML/MDX data sparsity compared to mainstream languages
+- Preprocessing pipeline bottlenecks with 900B+ tokens
+- Quality filtering false positives removing valuable code
+
+**Mitigation Strategies:**
+- Implement multi-stage quality gates with human validation sampling
+- Prioritize compression techniques (quantization-aware training)
+- Create XML/MDX augmentation pipelines from existing web datasets
+- Deploy distributed preprocessing with checkpointing
+- Use ensemble quality scoring to reduce filtering bias
+
+### Yellow Hat (Benefits & Optimism)
+**Key Opportunities:**
+- Specialized XML/MDX/JavaScript capabilities create market differentiation
+- On-device deployment enables privacy-preserving code assistance
+- 32K context length supports complex project understanding
+- GQA architecture provides efficient attention computation
+- Open-source ecosystem encourages community contributions
+
+**Strategic Advantages:**
+- First-mover advantage in on-device code generation
+- Reduced deployment costs compared to cloud-based alternatives
+- Enhanced security through local data processing
+- Faster inference times for developer workflows
+
+### Green Hat (Creative Solutions)
+**Innovation Opportunities:**
+- **Hybrid Tokenization:** Combine CodeBERT subword tokens with XML-specific token streams
+- **Adaptive Context Windows:** Dynamic context allocation based on project size
+- **Multi-Task Joint Training:** Simultaneously optimize for completion, explanation, and generation
+- **Progressive Quantization:** Train with mixed precision from the start
+- **Community-Contributed Datasets:** Incentivize XML/MDX data collection through gamification
+
+### Blue Hat (Process Control)
+**Implementation Framework:**
+1. **Phase 1 (Weeks 1-4):** Dataset acquisition and initial preprocessing
+2. **Phase 2 (Weeks 5-8):** Quality filtering and deduplication implementation
+3. **Phase 3 (Weeks 9-12):** Synthetic data generation and augmentation
+4. **Phase 4 (Weeks 13-16):** Integration testing and benchmark validation
+5. **Phase 5 (Weeks 17-20):** Model training and on-device optimization
+
+---
+
+## 2. Dataset Selection Strategy (Prioritizing XML/MDX/JavaScript Support)
+
+### Primary Dataset Priorities
+
+**Tier 1 - Core Code Sources (70% of training data)**
+1. **The Stack v2 - train-smol-ids subset**
+   - **Target Languages:** JavaScript, TypeScript, XML, HTML, CSS
+   - **Estimated Size:** ~12TB (17 languages × 700GB average)
+   - **Rationale:** Largest available high-quality codebase with permissive licensing
+   - **XML/MDX Strategy:** Prioritize XML (35%), HTML (25%), Markdown (15%) subsets
+
+2. **OpenCodeInstruct (Enhanced)**
+   - **Target Size:** ~50M instruction pairs
+   - **Language Distribution:** 
+     - JavaScript/TypeScript: 40%
+     - XML configuration files: 20%
+     - MDX/React components: 15%
+     - General programming: 25%
+   - **Quality Filter:** Unit test pass rate >70%
+
+**Tier 2 - Specialized Sources (20% of training data)**
+3. **CodeSearchNet (XML/MDX Enhanced)**
+   - **Repository Focus:** React projects with extensive MDX usage
+   - **Code-Comment Quality:** Minimum 0.8 semantic similarity score
+   - **Augmentation:** Add 200K XML documentation examples from Mozilla MDN
+
+4. **Web Development Datasets**
+   - **Next.js Documentation:** 50K XML/MDX examples
+   - **React Component Library:** 100K JSX/TSX examples
+   - **Vue.js Documentation:** 30K Vue template examples
+
+**Tier 3 - Synthetic & Augmented (10% of training data)**
+5. **Domain-Specific Generation**
+   - **React MDX Components:** 100K examples via AST mutations
+   - **XML Configuration Templates:** 75K examples from real projects
+   - **JavaScript Algorithm Explanations:** 50K generated with teacher models
+
+### Data Distribution Strategy
+```yaml
+Total Training Tokens: ~500B (suitable for 3B parameter model)
+Language Distribution:
+  JavaScript/TypeScript: 35% (175B tokens)
+  XML/HTML: 25% (125B tokens)
+  MDX/Markdown: 15% (75B tokens)
+  CSS/SCSS: 10% (50B tokens)
+  Other Languages: 15% (75B tokens)
+```
+
+---
+
+## 3. The Stack v2 Integration (train-smol-ids Configuration)
+
+### Dataset Acquisition Commands
+```bash
+# Download using BigQuery (recommended for scale)
+pip install google-cloud-bigquery
+export GOOGLE_APPLICATION_CREDENTIALS="path/to/service-account.json"
+
+# Query for target languages
+bq query --use_legacy_sql=false \
+  'SELECT content, language 
+   FROM `bigquery-public-data.github_repos.contents` 
+   WHERE language IN ("JavaScript", "TypeScript", "XML", "HTML", "CSS")
+     AND content IS NOT NULL
+     AND LENGTH(content) > 100
+     AND LENGTH(content) < 100000
+   LIMIT 500000000'
+
+# Alternative: Direct HuggingFace download
+pip install datasets
+from datasets import load_dataset
+dataset = load_dataset("bigcode/the-stack-smol-ids", 
+                      data_dir="data/programming_languages_subset")
+```
+
+### Preprocessing Configuration
+```python
+# Stack v2 preprocessing pipeline
+from datasets import Dataset
+import re
+from typing import List, Dict
+
+class StackV2Preprocessor:
+    def __init__(self):
+        self.language_filters = {
+            'javascript': {
+                'extensions': ['.js', '.jsx', '.mjs'],
+                'min_length': 50,
+                'max_length': 50000,
+                'quality_score': 0.7
+            },
+            'typescript': {
+                'extensions': ['.ts', '.tsx'],
+                'min_length': 50,
+                'max_length': 50000,
+                'quality_score': 0.75
+            },
+            'xml': {
+                'extensions': ['.xml', '.xsd', '.svg', '.xhtml'],
+                'min_length': 30,
+                'max_length': 30000,
+                'quality_score': 0.8
+            },
+            'html': {
+                'extensions': ['.html', '.htm'],
+                'min_length': 100,
+                'max_length': 40000,
+                'quality_score': 0.7
+            }
+        }
+    
+    def filter_quality(self, content: str, language: str) -> bool:
+        """Apply quality filters specific to language"""
+        config = self.language_filters.get(language.lower())
+        if not config:
+            return False
+            
+        # Length checks
+        if not (config['min_length'] <= len(content) <= config['max_length']):
+            return False
+            
+        # Language-specific patterns
+        if language.lower() == 'xml':
+            xml_patterns = [
+                r'<\?xml[^>]*\?>',  # XML declaration
+                r'<[a-zA-Z][^>]*>',  # Valid tags
+                r'</[a-zA-Z][^>]*>',  # Closing tags
+            ]
+            quality_score = sum(1 for pattern in xml_patterns 
+                              if re.search(pattern, content))
+            return quality_score >= 3
+            
+        elif language.lower() in ['javascript', 'typescript']:
+            js_patterns = [
+                r'\b(function|const|let|var|class|import|export)\b',
+                r'[{}();]',  # Basic syntax
+                r'[a-zA-Z_$][a-zA-Z0-9_$]*',  # Identifiers
+            ]
+            quality_score = sum(1 for pattern in js_patterns 
+                              if re.search(pattern, content))
+            return quality_score >= 4
+            
+        return True
+    
+    def deduplicate_content(self, dataset: Dataset) -> Dataset:
+        """Remove near-duplicates using MinHash LSH"""
+        from datasketch import MinHash, LSH
+        
+        lsh = LSH(threshold=0.8, num_perm=128)
+        unique_contents = []
+        
+        for idx, example in enumerate(dataset):
+            content = example['content']
+            minhash = MinHash(num_perm=128)
+            minhash.update(content.encode('utf-8'))
+            
+            # Check for duplicates
+            query_result = lsh.query(minhash)
+            if not query_result:
+                lsh.insert(idx, minhash)
+                unique_contents.append(example)
+                
+        return Dataset.from_list(unique_contents)
+```
+
+### Target Statistics After Filtering
+```yaml
+Stack v2 Processed Dataset:
+  Raw Size: ~12TB
+  After Language Filtering: ~4.2TB (35% reduction)
+  After Quality Filtering: ~2.8TB (33% further reduction)
+  After Deduplication: ~2.1TB (25% further reduction)
+  
+Language Breakdown:
+  JavaScript: 840GB
+  TypeScript: 420GB
+  XML: 350GB
+  HTML: 280GB
+  CSS: 210GB
+```
+
+---
+
+## 4. Instruction-Following Data (OpenCodeInstruct + Quality Filtering)
+
+### Enhanced OpenCodeInstruct Strategy
+```bash
+# Download and process OpenCodeInstruct
+git clone https://github.com/OpenLLMAI/OpenCodeInstruct.git
+cd OpenCodeInstruct
+pip install -r requirements.txt
+
+# Process with XML/MDX focus
+python scripts/filter_for_web_dev.py \
+  --input_dir data/raw \
+  --output_dir data/processed \
+  --languages javascript,typescript,xml,html,jsx,tsx,mdx \
+  --min_quality_score 0.75 \
+  --max_length 8192 \
+  --unit_test_validation True
+```
+
+### Custom Data Generation Pipeline
+```python
+# Enhanced instruction generation for web development
+class WebDevInstructionGenerator:
+    def __init__(self):
+        self.templates = {
+            'xml_generation': [
+                "Create a complete XML schema for {topic}",
+                "Generate XML configuration for {framework} deployment",
+                "Write XML transformation (XSLT) for {data_type}",
+                "Create XML sitemap for {website_type}"
+            ],
+            'mdx_creation': [
+                "Create interactive MDX component for {library}",
+                "Generate MDX documentation with code examples for {framework}",
+                "Write MDX blog post with {feature_type} examples",
+                "Create MDX component with {styling_library} integration"
+            ],
+            'js_enhancement': [
+                "Optimize this JavaScript {algorithm_type} for {performance_target}",
+                "Refactor this React component to use {pattern_type} pattern",
+                "Add TypeScript types for this {library_name} interface",
+                "Implement error handling for this {api_type} API call"
+            ]
+        }
+    
+    def generate_instructions(self, count: int = 100000) -> List[Dict]:
+        instructions = []
+        
+        for _ in range(count):
+            # Select template type based on target distribution
+            template_type = np.random.choice(
+                ['xml_generation', 'mdx_creation', 'js_enhancement'],
+                p=[0.25, 0.25, 0.5]
+            )
+            
+            template = random.choice(self.templates[template_type])
+            context = self.generate_context(template_type)
+            
+            instruction = template.format(**context)
+            expected_output = self.generate_expected_output(instruction, context)
+            
+            instructions.append({
+                'instruction': instruction,
+                'input': context.get('code_snippet', ''),
+                'output': expected_output,
+                'task_type': template_type,
+                'domain': 'web_development',
+                'difficulty': self.assess_difficulty(instruction)
+            })
+            
+        return instructions
+```
+
+### Quality Filtering Implementation
+```python
+# Multi-stage quality filtering for instruction data
+class InstructionQualityFilter:
+    def __init__(self):
+        self.quality_thresholds = {
+            'semantic_similarity': 0.7,
+            'code_syntax_validity': 0.85,
+            'instruction_clarity': 0.8,
+            'output_completeness': 0.9
+        }
+    
+    def filter_instructions(self, dataset: Dataset) -> Dataset:
+        """Apply comprehensive quality filtering"""
+        filtered_data = []
+        
+        for example in dataset:
+            quality_scores = self.calculate_quality_scores(example)
+            
+            if all(score >= self.quality_thresholds[key] 
+                   for key, score in quality_scores.items()):
+                filtered_data.append(example)
+                
+        return Dataset.from_list(filtered_data)
+    
+    def calculate_quality_scores(self, example: Dict) -> Dict[str, float]:
+        """Calculate multi-dimensional quality scores"""
+        scores = {}
+        
+        # Semantic similarity (instruction-input alignment)
+        scores['semantic_similarity'] = self.bert_similarity(
+            example['instruction'], example.get('input', '')
+        )
+        
+        # Code syntax validity
+        scores['code_syntax_validity'] = self.validate_code_syntax(
+            example.get('output', '')
+        )
+        
+        # Instruction clarity (readability score)
+        scores['instruction_clarity'] = self.calculate_readability(
+            example['instruction']
+        )
+        
+        # Output completeness (length and structure)
+        scores['output_completeness'] = self.assess_output_completeness(
+            example['output']
+        )
+        
+        return scores
+```
+
+---
+
+## 5. Code-Comment Pairs (CodeSearchNet + CAT Cleaning)
+
+### Enhanced CodeSearchNet Processing
+```python
+# Enhanced CodeSearchNet pipeline with XML/MDX focus
+from datasets import load_dataset
+import subprocess
+import json
+
+class CodeSearchNetProcessor:
+    def __init__(self):
+        self.language_priorities = {
+            'javascript': 0.4,
+            'typescript': 0.3,
+            'xml': 0.15,
+            'html': 0.1,
+            'css': 0.05
+        }
+    
+    def download_and_filter(self) -> Dataset:
+        """Download and filter CodeSearchNet for target languages"""
+        # Download CodeSearchNet
+        datasets = {}
+        for lang in ['javascript', 'typescript']:
+            datasets[lang] = load_dataset("code_search_net", lang)
+        
+        # Process and filter
+        filtered_examples = []
+        
+        for lang, dataset in datasets.items():
+            for split in ['train', 'valid', 'test']:
+                examples = dataset[split]
+                
+                # Language-specific filtering
+                if lang in ['javascript', 'typescript']:
+                    filtered = self.filter_js_ts_examples(examples)
+                else:
+                    continue
+                    
+                filtered_examples.extend(filtered)
+        
+        return Dataset.from_list(filtered_examples)
+    
+    def filter_js_ts_examples(self, examples: Dataset) -> List[Dict]:
+        """Filter JavaScript/TypeScript examples for quality"""
+        filtered = []
+        
+        for example in examples:
+            # Quality checks
+            if (len(example['func_documentation_string']) < 50 or
+                len(example['func_documentation_string']) > 2000 or
+                len(example['code']) < 100 or
+                len(example['code']) > 10000):
+                continue
+                
+            # Semantic quality check
+            similarity = self.calculate_doc_code_similarity(
+                example['func_documentation_string'], example['code']
+            )
+            
+            if similarity > 0.6:
+                # Add XML/MDX context if applicable
+                example['extended_context'] = self.add_web_context(example)
+                filtered.append(example)
+                
+        return filtered
+    
+    def add_web_context(self, example: Dict) -> Dict:
+        """Add XML/MDX context for web development examples"""
+        # Detect if function is part of web framework
+        framework_indicators = {
+            'react': ['React', 'JSX', 'Component', 'useState', 'useEffect'],
+            'vue': ['Vue', 'template', 'script', 'style'],
+            'angular': ['Angular', '@Component', 'NgModule'],
+            'xml': ['XML', 'schema', 'XSD', 'XSLT']
+        }
+        
+        framework = self.detect_framework(example['code'])
+        example['framework_type'] = framework
+        
+        return example
+```
+
+### CAT (Clean, Annotate, Transform) Pipeline Implementation
+```python
+# CAT (Clean, Annotate, Transform) pipeline
+class CATProcessor:
+    def __init__(self):
+        self.cleaning_rules = {
+            'code_removal': [
+                r'//\s*TODO[^\n]*',
+                r'/\*.*TODO.*\*/',
+                r'console\.log[^\n]*',
+                r'alert\([^\)]*\)',
+                r'debugger;'
+            ],
+            'comment_fixes': [
+                (r'/\*\s*\*\s*([^}]+)\s*\*/', r'/** \1 */'),  # Fix malformed docstrings
+                (r'//\s*([^/]+)//', r'// \1'),  # Remove trailing slashes
+            ]
+        }
+    
+    def clean_code(self, code: str) -> str:
+        """Apply cleaning rules to code"""
+        cleaned = code
+        
+        for pattern in self.cleaning_rules['code_removal']:
+            cleaned = re.sub(pattern, '', cleaned)
+            
+        for pattern, replacement in self.cleaning_rules['comment_fixes']:
+            cleaned = re.sub(pattern, replacement, cleaned)
+            
+        return cleaned.strip()
+    
+    def annotate_code(self, code: str, language: str) -> str:
+        """Add language-specific annotations"""
+        if language == 'xml':
+            return self.annotate_xml(code)
+        elif language in ['javascript', 'typescript']:
+            return self.annotate_js(code)
+        else:
+            return code
+    
+    def transform_for_learning(self, code: str, comments: str, language: str) -> Dict:
+        """Transform code-comment pairs for model training"""
+        # Create multiple learning objectives
+        transformations = []
+        
+        # 1. Code completion from comments
+        transformations.append({
+            'task_type': 'comment_to_code',
+            'input': comments,
+            'target': code,
+            'language': language
+        })
+        
+        # 2. Comment generation from code
+        transformations.append({
+            'task_type': 'code_to_comment',
+            'input': code,
+            'target': comments,
+            'language': language
+        })
+        
+        # 3. Code explanation (detailed)
+        if len(comments) > 100:  # Only for detailed comments
+            transformations.append({
+                'task_type': 'code_explanation',
+                'input': code,
+                'target': self.expand_explanation(comments),
+                'language': language
+            })
+        
+        return transformations
+```
+
+---
+
+## 6. Synthetic Data Generation (LLM-based + AST Mutations)
+
+### LLM-Based Generation Pipeline
+```python
+# Enhanced synthetic data generation for web technologies
+import ast
+import random
+from typing import List, Dict, Optional
+
+class WebDevSyntheticGenerator:
+    def __init__(self):
+        self.generator_models = {
+            'gpt3.5': 'openai/gpt-3.5-turbo',
+            'codellama': 'codellama/CodeLlama-7b-Instruct-hf',
+            'deepseek': 'deepseek-ai/deepseek-coder-6.7b-instruct'
+        }
+        
+        self.generation_strategies = {
+            'self_instruct': self.self_instruct_generation,
+            'evol_instruct': self.evol_instruct_generation,
+            'chain_of_thought': self.chain_of_thought_generation,
+            'domain_specific': self.domain_specific_generation
+        }
+    
+    def self_instruct_generation(self, seed_code: str, count: int = 1000) -> List[Dict]:
+        """Generate instructions using Self-Instruct methodology"""
+        instructions = []
+        
+        for _ in range(count):
+            # Generate diverse instruction templates
+            template = self.select_instruction_template(seed_code)
+            context = self.generate_context(template)
+            
+            instruction = template.format(**context)
+            response = self.generate_with_teacher_model(instruction)
+            
+            instructions.append({
+                'instruction': instruction,
+                'input': seed_code,
+                'output': response,
+                'generation_method': 'self_instruct',
+                'quality_score': self.assess_generation_quality(instruction, response)
+            })
+            
+        return instructions
+    
+    def evol_instruct_generation(self, base_examples: List[Dict], count: int = 1000) -> List[Dict]:
+        """Generate more complex examples using Evol-Instruct"""
+        evolved_examples = []
+        
+        for _ in range(count):
+            # Select base example
+            base = random.choice(base_examples)
+            
+            # Apply evolution operations
+            evolved_instruction = self.evolve_instruction(base['instruction'])
+            evolved_output = self.evolve_output(base['output'])
+            
+            evolved_examples.append({
+                'instruction': evolved_instruction,
+                'input': base['input'],
+                'output': evolved_output,
+                'generation_method': 'evol_instruct',
+                'evolution_operations': self.record_evolution_operations(),
+                'difficulty_increase': self.calculate_difficulty_increase(base, evolved)
+            })
+            
+        return evolved_examples
+    
+    def domain_specific_generation(self) -> Dict[str, List[Dict]]:
+        """Generate domain-specific examples for XML/MDX/JavaScript"""
+        synthetic_data = {}
+        
+        # XML generation
+        synthetic_data['xml'] = self.generate_xml_examples(10000)
+        
+        # MDX generation
+        synthetic_data['mdx'] = self.generate_mdx_examples(8000)
+        
+        # JavaScript/React generation
+        synthetic_data['javascript'] = self.generate_js_examples(15000)
+        
+        return synthetic_data
+```
+
+### AST Mutation Strategies
+```python
+# Advanced AST mutation for code augmentation
+class ASTMutator:
+    def __init__(self):
+        self.mutation_operators = {
+            'javascript': [
+                self.replace_variable_names,
+                self.add_error_handling,
+                self.insert_logging_statements,
+                self.modify_function_signatures,
+                self.add_type_annotations
+            ],
+            'xml': [
+                self.modify_attribute_values,
+                self.add_nested_elements,
+                self.reorganize_element_structure,
+                self.add_namespace_declarations,
+                self.insert_processing_instructions
+            ]
+        }
+    
+    def mutate_code(self, code: str, language: str, mutation_rate: float = 0.3) -> str:
+        """Apply AST-based mutations to code"""
+        if language == 'javascript':
+            return self.mutate_js_code(code, mutation_rate)
+        elif language == 'xml':
+            return self.mutate_xml_code(code, mutation_rate)
+        else:
+            return code
+    
+    def mutate_js_code(self, code: str, mutation_rate: float) -> str:
+        """Mutate JavaScript/TypeScript code using AST"""
+        try:
+            # Parse to AST
+            tree = ast.parse(code)
+            
+            # Apply random mutations
+            mutations_applied = []
+            for node in ast.walk(tree):
+                if random.random() < mutation_rate:
+                    mutation = random.choice(self.mutation_operators['javascript'])
+                    new_node = mutation(node)
+                    if new_node:
+                        mutations_applied.append(mutation.__name__)
+            
+            # Generate mutated code
+            mutated_code = ast.unparse(tree)
+            
+            # Add metadata
+            return {
+                'code': mutated_code,
+                'mutations_applied': mutations_applied,
+                'original_code': code,
+                'mutation_count': len(mutations_applied)
+            }
+            
+        except SyntaxError:
+            return {'code': code, 'mutations_applied': [], 'error': 'syntax_error'}
+```
+
+---
+
+## 7. Preprocessing Pipeline (CodeBERT Tokenization + MinHash Deduplication)
+
+### CodeBERT Tokenization Strategy
+```python
+# CodeBERT-based preprocessing pipeline
+from transformers import AutoTokenizer
+from typing import List, Dict, Tuple
+import hashlib
+from datasketch import MinHash, LSH
+
+class CodeBERTPreprocessor:
+    def __init__(self, model_name: str = "microsoft/codebert-base"):
+        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
+        self.tokenizer.model_max_length = 8192  # Increased for long code sequences
+        
+        # Language-specific tokenization configurations
+        self.language_configs = {
+            'javascript': {
+                'special_tokens': ['<js>', '</js>', '<function>', '</function>'],
+                'context_tokens': ['<react>', '<node>', '<browser>']
+            },
+            'xml': {
+                'special_tokens': ['<xml>', '</xml>', '<element>', '</element>'],
+                'context_tokens': ['<web>', '<config>', '<schema>']
+            },
+            'mdx': {
+                'special_tokens': ['<mdx>', '</mdx>', '<component>', '</component>'],
+                'context_tokens': ['<react>', '<markdown>', '<interactive>']
+            }
+        }
+    
+    def tokenize_code(self, code: str, language: str, max_length: int = 1024) -> Dict:
+        """Tokenize code with language-specific enhancements"""
+        config = self.language_configs.get(language, {})
+        
+        # Add language-specific tokens
+        enhanced_code = self.add_language_tokens(code, language)
+        
+        # Tokenize with CodeBERT
+        tokens = self.tokenizer.encode_plus(
+            enhanced_code,
+            max_length=max_length,
+            padding='max_length',
+            truncation=True,
+            return_tensors='pt',
+            return_special_tokens_mask=True
+        )
+        
+        # Calculate statistics
+        stats = self.calculate_tokenization_stats(enhanced_code, tokens)
+        
+        return {
+            'tokens': tokens,
+            'input_ids': tokens['input_ids'].squeeze().tolist(),
+            'attention_mask': tokens['attention_mask'].squeeze().tolist(),
+            'special_tokens_mask': tokens['special_tokens_mask'].squeeze().tolist(),
+            'statistics': stats,
+            'language': language,
+            'original_code': code
+        }
+```
+
+### MinHash Deduplication System
+```python
+# Advanced deduplication using MinHash + LSH
+class AdvancedDeduplicator:
+    def __init__(self, threshold: float = 0.8, num_perm: int = 128):
+        self.threshold = threshold
+        self.num_perm = num_perm
+        self.lsh = LSH(threshold=threshold, num_perm=num_perm)
+        self.minhash_registry = {}
+        
+    def build_dedup_index(self, dataset: Dataset) -> Dict[str, List[int]]:
+        """Build deduplication index using MinHash LSH"""
+        print("Building MinHash deduplication index...")
+        
+        duplicates = {}
+        total_examples = len(dataset)
+        
+        for idx, example in enumerate(dataset):
+            # Create content representation
+            content = self.preprocess_for_hashing(example)
+            
+            # Create MinHash
+            minhash = MinHash(num_perm=self.num_perm)
+            minhash.update(content.encode('utf-8'))
+            
+            # Query existing index
+            query_result = self.lsh.query(minhash)
+            
+            if not query_result:
+                # New unique content
+                self.lsh.insert(str(idx), minhash)
+                self.minhash_registry[str(idx)] = minhash
+            else:
+                # Found duplicates
+                for duplicate_idx in query_result:
+                    if duplicate_idx not in duplicates:
+                        duplicates[duplicate_idx] = []
+                    duplicates[duplicate_idx].append(idx)
+            
+            # Progress tracking
+            if idx % 10000 == 0:
+                print(f"Processed {idx}/{total_examples} examples")
+        
+        print(f"Deduplication complete. Found {len(duplicates)} duplicate groups")
+        return duplicates
+```
+
+---
+
+## 8. Quality Assurance & Metrics (MMLU Benchmarking Strategy)
+
+### MMLU Benchmark Implementation
+```python
+# MMLU benchmark adaptation for code generation
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from typing import List, Dict, Tuple
+import numpy as np
+from sklearn.metrics import accuracy_score, f1_score
+
+class MMLUCodeBenchmark:
+    def __init__(self, model_path: str, tokenizer_path: str):
+        self.model = AutoModelForCausalLM.from_pretrained(model_path)
+        self.tokenizer = AutoTokenizer.from_pretrained(tokenizer_path)
+        self.model.eval()
+        
+        # MMLU domains adapted for coding
+        self.code_domains = [
+            'programming_fundamentals',
+            'web_development',
+            'data_structures',
+            'algorithms',
+            'software_engineering',
+            'cybersecurity',
+            'databases',
+            'computer_networks'
+        ]
+    
+    def create_code_mmlu_dataset(self) -> Dict[str, List[Dict]]:
+        """Create MMLU-style dataset for coding evaluation"""
+        dataset = {}
+        
+        for domain in self.code_domains:
+            domain_questions = self.generate_domain_questions(domain)
+            dataset[domain] = domain_questions
+        
+        return dataset
+    
+    def generate_web_dev_questions(self) -> List[Dict]:
+        """Generate web development questions"""
+        questions = [
+            {
+                'question': 'Which of the following is the correct way to create a React component?',
+                'options': [
+                    'function MyComponent() { return <div>Hello</div>; }',
+                    'class MyComponent extends React.Component { render() { return <div>Hello</div>; } }',
+                    'const MyComponent = () => <div>Hello</div>;',
+                    'All of the above'
+                ],
+                'correct_answer': 3,
+                'domain': 'web_development',
+                'difficulty': 'medium',
+                'context': 'react_components'
+            },
+            {
+                'question': 'What is the purpose of the useState hook in React?',
+                'options': [
+                    'To handle side effects',
+                    'To manage component state',
+                    'To make API calls',
+                    'To style components'
+                ],
+                'correct_answer': 1,
+                'domain': 'web_development',
+                'difficulty': 'easy',
+                'context': 'react_hooks'
+            },
+            {
+                'question': 'Which XML namespace declaration is required for XSLT transformations?',
+                'options': [
+                    'xmlns:xsl="http://www.w3.org/1999/XSL/Transform"',
+                    'xmlns="http://www.w3.org/TR/xslt"',
+                    'xmlns:transform="http://www.w3.org/xslt"',
+                    'xmlns:xalan="http://xml.apache.org/xslt"'
+                ],
+                'correct_answer': 0,
+                'domain': 'web_development',
+                'difficulty': 'hard',
+                'context': 'xml_xslt'
+            }
+        ]
+        
+        # Generate additional questions programmatically
+        for _ in range(100):  # Generate 100 questions per domain
+            question = self.generate_random_web_question()
+            if question:
+                questions.append(question)
+        
+        return questions
+```
+
+### Code-Specific Evaluation Metrics
+```python
+# Advanced evaluation metrics for code generation
+class CodeEvaluationMetrics:
+    def __init__(self):
+        self.bleu_weights = (0.25, 0.25, 0.25, 0.25)
+        self.bertscore_model = 'microsoft/codebert-base'
+    
+    def evaluate_code_completion(self, references: List[str], predictions: List[str]) -> Dict[str, float]:
+        """Evaluate code completion quality"""
+        metrics = {}
+        
+        # BLEU score
+        metrics['bleu'] = self.calculate_bleu(references, predictions)
+        
+        # CodeBLEU (simplified version)
+        metrics['codebleu'] = self.calculate_codebleu(references, predictions)
+        
+        # BERTScore
+        metrics['bertscore'] = self.calculate_bertscore(references, predictions)
+        
+        # Syntax validity
+        metrics['syntax_validity'] = self.calculate_syntax_validity(predictions)
+        
+        # Semantic similarity
+        metrics['semantic_similarity'] = self.calculate_semantic_similarity(
+            references, predictions
+        )
+        
+        return metrics
+    
+    def calculate_syntax_validity(self, code_predictions: List[str]) -> float:
+        """Calculate percentage of predictions with valid syntax"""
+        valid_count = 0
+        
+        for code in code_predictions:
+            if self.validate_syntax(code):
+                valid_count += 1
+        
+        return valid_count / len(code_predictions) if code_predictions else 0
+    
+    def validate_syntax(self, code: str) -> bool:
+        """Validate code syntax for different languages"""
+        try:
+            # Try to parse as JavaScript
+            if any(keyword in code for keyword in ['function', 'const', 'let', 'var']):
+                import subprocess
+                result = subprocess.run(['node', '-c'], 
+                                      input=code, 
+                                      text=True, 
+                                      capture_output=True)
+                return result.returncode == 0
+            
+            # Try to parse as XML
+            if code.strip().startswith('<'):
+                import xml.etree.ElementTree as ET
+                ET.fromstring(code)
+                return True
+            
+            return False
+        except:
+            return False
+```
+
+---
+
+## 9. On-Device Optimization Considerations (3.09B Parameter Constraints)
+
+### Memory Optimization Strategy
+```python
+# On-device optimization for 3.09B parameter model
+import torch
+import torch.nn as nn
+from transformers import BitsAndBytesConfig
+from typing import Dict, Tuple
+
+class OnDeviceOptimizer:
+    def __init__(self, target_memory_gb: float = 8.0):
+        self.target_memory_gb = target_memory_gb
+        self.quantization_config = BitsAndBytesConfig(
+            load_in_8bit=True,
+            llm_int8_threshold=6.0,
+            llm_int8_skip_modules=["embed_tokens", "lm_head"]
+        )
+    
+    def calculate_memory_requirements(self, model_config: Dict) -> Dict[str, float]:
+        """Calculate memory requirements for different configurations"""
+        base_memory_gb = 3.09 * 4 / 1024  # 3.09B parameters * 4 bytes/float32
+        
+        memory_breakdown = {
+            'base_model_fp32': base_memory_gb,
+            'base_model_fp16': base_memory_gb / 2,
+            'base_model_int8': base_memory_gb / 4,
+            'base_model_int4': base_memory_gb / 8,
+            'with_optimizer_states': base_memory_gb * 1.5,
+            'with_gradient_checkpointing': base_memory_gb * 0.7,
+            'estimated_runtime': 0
+        }
+        
+        # Calculate runtime memory (model + activations)
+        runtime_memory = self.estimate_runtime_memory(model_config)
+        memory_breakdown['estimated_runtime'] = runtime_memory
+        
+        return memory_breakdown
+    
+    def estimate_runtime_memory(self, config: Dict) -> float:
+        """Estimate runtime memory including activations"""
+        # Estimate activation memory
+        batch_size = config.get('batch_size', 1)
+        seq_length = config.get('seq_length', 2048)
+        hidden_size = config.get('hidden_size', 2048)
+        
+        # Attention activation memory
+        attention_memory = (batch_size * seq_length * seq_length * 4) / (1024**3)  # GB
+        
+        # Feed-forward activation memory
+        ff_memory = (batch_size * seq_length * hidden_size * 8) / (1024**3)  # GB
+        
+        # Total runtime memory
+        runtime_memory = attention_memory + ff_memory
+        
+        return runtime_memory
+```
+
+### Inference Optimization
+```python
+# Inference optimization for on-device deployment
+class InferenceOptimizer:
+    def __init__(self):
+        self.optimization_strategies = {
+            'flash_attention': self.enable_flash_attention,
+            'gradient_checkpointing': self.enable_gradient_checkpointing,
+            'mixed_precision': self.enable_mixed_precision,
+            'dynamic_batching': self.enable_dynamic_batching
+        }
+    
+    def optimize_inference(self, model: nn.Module, 
+                          optimization_level: str = 'medium') -> nn.Module:
+        """Apply inference optimizations based on optimization level"""
+        
+        if optimization_level == 'light':
+            model = self.enable_mixed_precision(model)
+        elif optimization_level == 'medium':
+            model = self.enable_flash_attention(model)
+            model = self.enable_gradient_checkpointing(model)
+        elif optimization_level == 'aggressive':
+            model = self.enable_all_optimizations(model)
+        
+        return model
+    
+    def enable_flash_attention(self, model: nn.Module) -> nn.Module:
+        """Enable Flash Attention for memory efficiency"""
+        try:
+            from flash_attn import flash_attn_func
+            
+            # Replace attention implementation with Flash Attention
+            for name, module in model.named_modules():
+                if 'attention' in name.lower():
+                    # Create Flash Attention wrapper
+                    flash_attn_wrapper = FlashAttentionWrapper(module)
+                    # Replace module (implementation depends on specific model)
+                    # self.replace_module(model, name, flash_attn_wrapper)
+            
+        except ImportError:
+            print("Flash Attention not available, skipping optimization")
+        
+        return model
+```
+
+---
+
+## 10. Implementation Roadmap (Specific Tools and Configurations)
+
+### Phase 1: Dataset Acquisition & Initial Preprocessing (Weeks 1-4)
+
+#### Week 1: Infrastructure Setup
+```bash
+# Environment setup
+pip install torch==2.0.1 torchvision==0.15.2 torchaudio==2.0.2
+pip install transformers==4.30.0 datasets==2.14.0 accelerate==0.20.0
+pip install bitsandbytes==0.41.0 safetensors==0.3.0
+pip install google-cloud-bigquery datasets[bigquery]
+pip install datasketch==1.6.4 nltk==3.8.1 rouge==1.1.1
+
+# Install language-specific tools
+npm install -g @babel/parser @babel/traverse @babel/types
+pip install tree-sitter==0.20.0
+
+# Setup directory structure
+mkdir -p {data/{raw,processed,tokenized},models,logs,scripts,evaluation}
+cd data
+```
+
+#### Week 2: The Stack v2 Integration
+```python
+# scripts/stack_v2_download.py
+import os
+from datasets import load_dataset
+from datasets.dataset_dict import DatasetDict
+
+def download_stack_v2_subset():
+    """Download and process Stack v2 subset"""
+    
+    # Configuration
+    target_languages = ['javascript', 'typescript', 'xml', 'html', 'css']
+    max_examples_per_lang = 1000000  # 1M examples per language
+    
+    # Download dataset
+    print("Downloading Stack v2 dataset...")
+    dataset = load_dataset("bigcode/the-stack-smol-ids", 
+                          data_dir="programming_languages_subset")
+    
+    # Process each language
+    processed_data = {}
+    for lang in target_languages:
+        print(f"Processing {lang} data...")
+        
+        if lang in dataset:
+            lang_data = dataset[lang]
+            
+            # Filter and clean
+            filtered_data = filter_language_data(lang_data, lang)
+            
+            # Deduplicate
+            deduped_data = deduplicate_data(filtered_data)
+            
+            # Quality filter
+            quality_filtered = apply_quality_filters(deduped_data, lang)
+            
+            processed_data[lang] = quality_filtered
+            
+            print(f"  {lang}: {len(quality_filtered)} examples after processing")
+    
+    # Save processed data
+    for lang, data in processed_data.items():
+        data.save_to_disk(f"data/processed/stack_v2_{lang}")
+    
+    return processed_data
+
+if __name__ == "__main__":
+    download_stack_v2_subset()
+```
+
+#### Week 3: Instruction Dataset Processing
+```python
+# scripts/process_instructions.py
+import json
+from datasets import Dataset
+
+def process_instruction_datasets():
+    """Process and enhance instruction datasets"""
+    
+    # Download OpenCodeInstruct
+    print("Downloading OpenCodeInstruct...")
+    instruct_dataset = load_dataset("bigcode/instructcodet5p-px")
+
+    # Process with quality filtering
+    enhanced_instructions = []
+    
+    for example in instruct_dataset['train']:
+        # Language detection
+        detected_lang = detect_programming_language(example['code'])
+        
+        if detected_lang in ['javascript', 'typescript', 'xml', 'html']:
+            # Quality scoring
+            quality_score = calculate_instruction_quality(example)
+            
+            if quality_score > 0.75:
+                # Add web development context
+                enhanced_example = add_web_dev_context(example, detected_lang)
+                enhanced_instructions.append(enhanced_example)
+    
+    # Save enhanced instructions
+    enhanced_dataset = Dataset.from_list(enhanced_instructions)
+    enhanced_dataset.save_to_disk("data/processed/enhanced_instructions")
+    
+    print(f"Enhanced instructions: {len(enhanced_instructions)} examples")
+
+if __name__ == "__main__":
+    process_instruction_datasets()
+```
+
+### Phase 2: Quality Filtering & Deduplication (Weeks 5-8)
+
+#### Week 5: Advanced Deduplication System
+```python
+# scripts/advanced_deduplication.py
+from datasketch import MinHash, LSH
+from datasets import Dataset
+import numpy as np
+
+class AdvancedDeduplicator:
+    def __init__(self, threshold=0.8, num_perm=128):
+        self.threshold = threshold
+        self.num_perm = num_perm
+        self.lsh = LSH(threshold=threshold, num_perm=num_perm)
+    
+    def deduplicate_dataset(self, dataset_path: str, language: str):
+        """Advanced deduplication with semantic similarity"""
+        
+        dataset = Dataset.load_from_disk(dataset_path)
+        duplicates = self.find_duplicates(dataset)
+        
+        # Remove duplicates, keeping highest quality
+        unique_data = self.remove_duplicates(dataset, duplicates)
+        
+        # Save deduplicated dataset
+        unique_dataset = Dataset.from_list(unique_data)
+        unique_dataset.save_to_disk(f"{dataset_path}_deduped")
+        
+        return unique_dataset
+```
+
+### Phase 3: Synthetic Data Generation (Weeks 9-12)
+
+#### Week 9: LLM-Based Generation Setup
+```bash
+# Setup synthetic data generation environment
+pip install openai anthropic
+
+# Configure API keys
+export OPENAI_API_KEY="your-openai-api-key"
+export ANTHROPIC_API_KEY="your-anthropic-api-key"
+
+# Create synthetic data generation script
+touch scripts/synthetic_generation.py
+chmod +x scripts/synthetic_generation.py
+```
+
+### Phase 4: Integration & Benchmarking (Weeks 13-16)
+
+#### Week 13: Model Integration Testing
+```python
+# scripts/integration_test.py
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+def test_model_integration():
+    """Test data integration with model architecture"""
+    
+    # Load model configuration
+    model_config = {
+        'model_name': 'microsoft/phi-2',
+        'vocab_size': 51200,
+        'max_position_embeddings': 2048,
+        'num_attention_heads': 32,
+        'num_hidden_layers': 36,
+        'intermediate_size': 8192
+    }
+    
+    # Initialize tokenizer
+    tokenizer = AutoTokenizer.from_pretrained(model_config['model_name'])
+    tokenizer.padding_side = 'right'
+    tokenizer.pad_token = tokenizer.eos_token
+    
+    # Load sample data
+    sample_data = load_sample_processed_data()
+    
+    # Test tokenization
+    tokenized_data = []
+    for example in sample_data[:1000]:  # Test with 1000 examples
+        tokenized = tokenizer(
+            example['content'],
+            max_length=1024,
+            truncation=True,
+            padding='max_length',
+            return_tensors='pt'
+        )
+        tokenized_data.append(tokenized)
+    
+    print(f"Tokenization test completed with {len(tokenized_data)} examples")
+    print(f"Tokenizer vocab size: {tokenizer.vocab_size}")
+    print(f"Special tokens: {tokenizer.all_special_tokens}")
+    
+    return tokenized_data
+```
+
+### Phase 5: Final Training & Optimization (Weeks 17-20)
+
+#### Week 17: Training Configuration
+```bash
+# Setup training environment
+pip install deepspeed fairscale wandb
+
+# Create training script
+touch scripts/train_model.py
+chmod +x scripts/train_model.py
+```
+
+#### Week 18: Training Execution
+```python
+# scripts/training_config.py
+training_config = {
+    'model_name_or_path': 'microsoft/phi-2',
+    'output_dir': './outputs/sheikh-2.5-coder',
+    'per_device_train_batch_size': 8,
+    'per_device_eval_batch_size': 8,
+    'gradient_accumulation_steps': 4,
+    'learning_rate': 1e-4,
+    'num_train_epochs': 3,
+    'logging_steps': 100,
+    'save_steps': 1000,
+    'eval_steps': 1000,
+    'warmup_steps': 1000,
+    'max_grad_norm': 1.0,
+    'weight_decay': 0.01,
+    'save_total_limit': 3,
+    'load_best_model_at_end': True,
+    'report_to': 'wandb',
+    'run_name': 'sheikh-2.5-coder-training'
+}
+```
+
+### Success Metrics & Validation
+
+#### Technical Metrics
+```yaml
+Model Performance Targets:
+  MMLU Code Score: >60% accuracy
+  HumanEval: >40% pass@1
+  CodeBLEU: >0.65
+  Syntax Validity: >95%
+  Semantic Coherence: >0.80
+
+On-Device Performance:
+  Memory Footprint: <8GB (INT8 quantized)
+  Inference Speed: <100ms for 512 token completion
+  Context Length: 32K tokens
+  Battery Impact: <5% per inference session
+```
+
+#### Quality Validation Pipeline
+```python
+# Quality validation at each phase
+class QualityValidator:
+    def __init__(self):
+        self.thresholds = {
+            'data_quality': 0.85,
+            'duplication_rate': <0.05,
+            'language_accuracy': 0.95,
+            'syntax_validity': 0.90,
+            'semantic_coherence': 0.75
+        }
+    
+    def validate_phase_completion(self, phase: str, outputs: Dict):
+        """Validate that each phase meets quality thresholds"""
+        
+        validation_results = {}
+        
+        if phase == "dataset_acquisition":
+            validation_results = self.validate_dataset_acquisition(outputs)
+        elif phase == "quality_filtering":
+            validation_results = self.validate_quality_filtering(outputs)
+        elif phase == "synthetic_generation":
+            validation_results = self.validate_synthetic_generation(outputs)
+        
+        # Check all thresholds met
+        all_passed = all(
+            validation_results[metric] >= self.thresholds[metric] 
+            for metric in validation_results
+        )
+        
+        return {
+            'phase': phase,
+            'validation_results': validation_results,
+            'all_thresholds_met': all_passed,
+            'blocking_issues': self.identify_blocking_issues(validation_results)
+        }
+```
+
+### Deployment Readiness Checklist
+- [ ] Dataset quality validation completed (>95% samples pass)
+- [ ] Deduplication implemented (duplication rate <5%)
+- [ ] Synthetic data diversity validated (DCS score >0.7)
+- [ ] On-device memory requirements confirmed (<8GB)
+- [ ] Inference optimization applied (Flash Attention, quantization)
+- [ ] MMLU benchmarking completed (>60% accuracy)
+- [ ] Code generation quality validated (CodeBLEU >0.65)
+- [ ] Performance testing on target hardware completed
+- [ ] Documentation and examples prepared
+- [ ] GitHub repository structured and documented
+
+This comprehensive implementation plan provides a complete roadmap for developing Sheikh-2.5-Coder's data preparation strategy, ensuring high-quality training data that supports the model's specialization in XML/MDX/JavaScript while maintaining the on-device deployment requirements.