🧩 Part 7/10

✔ Fine-Tuning Large Language Models: Challenges and Opportunities
✔ Challenges of Fine-Tuning Large Language Models
✔ Opportunities for Improving Performance and Efficiency
✔ Pruning: Reducing the Number of Parameters
✔ Pruning Methods and Techniques
✔ Pruning Results and Trade-offs
✔ Quantization: Reducing the Precision of Parameters
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🧩 Part 6/10

✔ Memory Errors
✔ Reducing Batch Size
✔ Using Gradient Checkpointing
✔ Using Mixed Precision Training
✔ Gradient Explosion
✔ Clipping Gradients
✔ Using Gradient Accumulation
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🧩 Part 5/10

✔ Fine-Tuning Large Language Models
✔ What is Fine-Tuning?
✔ Why Fine-Tune Large Language Models?
✔ How to Fine-Tune Large Language Models?
✔ Loss Functions for Fine-Tuning
✔ Cross-Entropy Loss
✔ Contrastive Loss
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🧩 Part 4/10

✔ Choosing a Framework: PyTorch vs TensorFlow
✔ Installing Libraries and Dependencies
✔ Loading Pre-trained Models
✔ Configuring Hyperparameters
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🧩 Part 3/10

✔ What are Large Language Models?
✔ How to Choose a Model for Fine-Tuning?
✔ Model Size and Architecture
✔ Model Availability and Accessibility
✔ Model Performance and Generalization
✔ How to Choose a Task for Fine-Tuning?
✔ Task Type and Complexity
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🧩 Part 2/10

✔ Data Preparation
✔ Data Cleaning and Filtering
✔ Data Augmentation and Sampling
✔ Preprocessing
✔ Tokenization
✔ Masking
✔ Batching
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🧩 Part 1/10

✔ What are Large Language Models?
✔ Why are Large Language Models Important?
✔ How to Fine-Tune Large Language Models?
✔ Data Preparation
✔ Model Selection
✔ Hyperparameter Tuning
✔ Evaluation and Deployment
✔ Examples of Fine-Tuning Large Language Models
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