کتاب صوتی fine-tuning مدل‌های زبانی: دستیابی به سبک‌های منحصر به فرد و قابل تنظیم در رسمیت

🎓 دوره آموزشی جامع

📚 اطلاعات دوره

عنوان دوره: دوره fine-tuning مدل‌های زبانی: دستیابی به سبک‌های منحصر به فرد و قابل تنظیم در رسمیت

موضوع کلی: هوش مصنوعی و مدل‌های زبانی بزرگ

موضوع میانی: Fine-tuning برای جنبه‌های خاص (مانند لحن، سبک)

📋 سرفصل‌های دوره

• 1. Foundations of Fine‑Tuning Language Models
• 2. Overview of Large Language Model Architectures
• 3. Parameter‑Efficient Fine‑Tuning (PEFT) Introduction
• 4. Selecting PEFT Methods: LoRA, AdaLora, Adapter Prompts
• 5. Setting Up Development Environment for Fine‑Tuning
• 6. GPU/TPU Resource Planning and Cost Estimation
• 7. Data Collection for Style‑Specific Corpora
• 8. Defining Formality Levels and Style Guidelines
• 9. Annotating Data for Consistent Formality
• 10. Text Preprocessing and Cleaning for Style Consistency
• 11. Tokenization Strategies for Formal Text
• 12. Building a Balanced Dataset Across Formality Tiers
• 13. Dataset Splitting: Train, Validation, Test Sets
• 14. Data Augmentation Techniques for Formal Style
• 15. Synthetic Data Generation Using Pre‑trained Models
• 16. Managing Dataset Versioning with DVC
• 17. Implementing Efficient Data Loading Pipelines
• 18. Understanding Model Architecture Fine‑Tuning Targets
• 19. Loading Pre‑trained LLMs for Fine‑Tuning
• 20. Freezing Base Model Weights vs. Full Fine‑Tuning
• 21. Learning Rate Scheduling for Style Adaptation
• 22. Optimizer Selection (AdamW, Lion, etc.)
• 23. Batch Size and Gradient Accumulation Strategies
• 24. Mixed Precision Training for Faster Fine‑Tuning
• 25. Gradient Clipping and Loss Scaling
• 26. Monitoring Training Progress with TensorBoard
• 27. Saving Checkpoints and Resuming Training
• 28. Early Stopping to Prevent Over‑fitting
• 29. Evaluating Perplexity as a Baseline Metric
• 30. Incorporating Style‑Specific Evaluation Metrics
• 31. Building a Formality Scoring Classifier
• 32. Using BLEU and ROUGE for Style Consistency
• 33. Human Evaluation Protocols for Formality
• 34. Statistical Significance Testing for Style Gains
• 35. Prompt Engineering for Formal Output Generation
• 36. Designing Style Prompts for Adjustable Tone
• 37. Zero‑Shot Style Induction without Fine‑Tuning
• 38. Few‑Shot Learning with In‑Context Examples
• 39. Parameter‑Efficient Prompt Tuning (Prefix, P‑Tuning)
• 40. Adapter‑Based Fine‑Tuning for Multiple Styles
• 41. Merging Multiple Fine‑Tuned Models for Hybrid Styles
• 42. Continual Learning for Evolving Formality Standards
• 43. Catastrophic Forgetting Mitigation Techniques
• 44. Replay Buffer and Elastic Weight Consolidation
• 45. Domain Adaptation Within Formal Contexts
• 46. Cross‑Genre Style Transfer (Academic ↔ Business)
• 47. Handling Code‑Switching in Formal Multilingual Data
• 48. Language‑Specific Tokenizers for Persian and English
• 49. Subword Tokenization Impacts on Style Modeling
• 50. Vocabulary Expansion for Formal Terminology
• 51. Regularizing Style Drift with Contrastive Learning
• 52. Using Data Summaries for Style Control
• 53. Implementing Temperature and Top‑p Sampling for Formality
• 54. Nucleus Sampling for Controlled Formality
• 55. Beam Search Strategies to Preserve Formal Tone
• 56. Post‑Processing Filters for Informal Leakage
• 57. Length Constraints and Sentence Segmentation
• 58. Sentence Simplification for Readability Scoring
• 59. Integrating Readability Metrics (Flesch‑Kincaid)
• 60. Automatic Style Classification with BERT‑Based Models
• 61. Fine‑Tuning Style Classifiers for Domain‑Specific Data
• 62. Using Reinforcement Learning from Human Feedback (RLHF)
• 63. Direct Preference Optimization (DPO) for Formality
• 64. Aligning Model Outputs with Style Preference Data
• 65. Safety Checks and Bias Audits for Formal Content
• 66. Model Card Creation for Style‑Specific Models
• 67. Documentation and Reproducibility with Version Control
• 68. Automated Testing for Fine‑Tuning Pipelines
• 69. Continuous Integration/Continuous Deployment (CI/CD) for Model Training
• 70. Cloud GPU Budgeting and Spot Instance Strategies
• 71. Model Quantization for Production Deployment
• 72. Edge Deployment on Mobile and IoT Devices
• 73. Model Compression via Pruning and Distillation
• 74. Knowledge Distillation to Smaller Formal Models
• 75. Evaluating Distilled Models for Style Retention
• 76. Real‑Time Inference Latency Optimization
• 77. Batch Inference for Large‑Scale Content Generation
• 78. Monitoring Model Performance in Production
• 79. Logging and Alerting for Style Drift Detection
• 80. User Feedback Loops for Iterative Style Refinement
• 81. Active Learning for Improving Formal Datasets
• 82. Semi‑Supervised Learning with Unlabeled Formal Text
• 83. Meta‑Learning Approaches for Rapid Style Adaptation
• 84. Hyperparameter Optimization with Optuna
• 85. Automated Machine Learning (AutoML) for Style Tuning
• 86. Cross‑Lingual Formality Transfer (English ↔ Persian)
• 87. Multilingual Adapter Sharing for Multiple Languages
• 88. Handling Code‑Mixed Formal Text in Multilingual Models
• 89. Legal and Licensing Considerations for Style Data
• 90. Copyright Compliance in Dataset Curation
• 91. Ethical Guidelines for Style‑Specific AI Systems
• 92. Transparency and Explainability of Style Adjustments
• 93. Model Interpretability Techniques for Style Control
• 94. User‑Centric Design of Style Customization Interfaces
• 95. Building a Dashboard for Style Parameter Adjustment
• 96. A/B Testing of Fine‑Tuned Models in Production
• 97. Cost‑Benefit Analysis of Different Fine‑Tuning Strategies
• 98. Scaling Training to Larger Datasets and Models
• 99. Distributed Training Strategies for Speedup
• 100. Future Trends in Adjustable Formality and Style AI