# 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']*>', # 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': ['', '', '', ''], 'context_tokens': ['', '', ''] }, 'xml': { 'special_tokens': ['', '', '', ''], 'context_tokens': ['', '', ''] }, 'mdx': { 'special_tokens': ['', '', '', ''], 'context_tokens': ['', '', ''] } } 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
Hello
; }', 'class MyComponent extends React.Component { render() { return
Hello
; } }', 'const MyComponent = () =>
Hello
;', '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.