Keywords for Fine-tuning LLMs: Prompt Engineering: Designing effective prompts to elicit desired responses without altering the model’s weights. Few-shot Learning: Fine-tuning models on very few examples to adapt to new tasks. Zero-shot Learning: Applying models to tasks without any task-specific training data. Transfer Learning: Adapting a pre-trained model for a different but related task. Meta-Learning: Training models to learn new tasks quickly with minimal data. Techniques: Layer Freezing: Only fine-tune specific layers of the model while keeping others frozen to save computation and prevent overfitting. Task-specific Heads: Adding task-specific layers on top of a pre-trained model for specific tasks (e.g., classification heads). Regularization Techniques: Applying methods like dropout, weight decay, or early stopping to prevent overfitting during fine-tuning. Curriculum Learning: Gradually increasing the difficulty of tasks during training to improve model learning. Data Augmentation: Generating additional training data by modifying existing examples to improve model robustness and generalization. Weighted Loss Functions: Adjusting the loss function to prioritize certain aspects of the training data or tasks. Dynamic Learning Rates: Adjusting the learning rate during training (e.g., learning rate schedules, warm-up periods) to improve convergence. Multi-task Learning: Training the model on multiple tasks simultaneously to improve generalization and performance across tasks. Contrastive Learning: Learning by comparing pairs or groups of examples to learn representations that distinguish between them. Self-supervised Learning: Generating labels from the data itself and using these labels as supervision for model training. Cross-lingual Transfer: Leveraging knowledge from one language to improve model performance on other languages. Ensemble Methods: Combining multiple models or fine-tuning strategies to improve performance and robustness.

Keywords for Fine-tuning LLMs:

Techniques:

Full fine-tuning: Updates all model parameters (most memory intensive) Adapter/bottleneck methods: Update smaller “adapter” modules instead of full weights (e.g., LoRA, Adapter-Tuning) Knowledge distillation: Train a smaller model on the outputs of a larger model (efficient but might sacrifice accuracy) Pruning: Remove unimportant connections and weights to reduce model size Quantization: Reduce precision of weights and activations (e.g., 4-bit, 8-bit) Sparse training/inference: Exploit sparsity in activations/gradients for performance Mixed-precision training: Use different precisions for different parts of the model Tools and Libraries:

BigScience BLOOM: Open-source 176B LLM with adapter-based tuning Google Pathways System: LLM fine-tuning with quantization and mixed-precision NVIDIA Megatron-Turing NLG: LLM fine-tuning for natural language generation Hugging Face Transformers: Popular library for LLMs with various fine-tuning methods OpenAI API: Access to fine-tuned LLMs like ChatGPT Cohere: Cloud-based platform for LLM fine-tuning Other Key Terms:

Memory efficiency: Reducing memory footprint for deployment on edge devices Inference speed: Optimizing models for faster predictions Transfer learning: Leveraging pre-trained weights for new tasks Few-shot learning: Adapting models with minimal data On-device inference: Running models directly on devices without cloud access Prompt engineering: Crafting effective prompts for fine-tuning Additional Notes:

The best approach depends on specific goals, resource constraints, and desired trade-offs between accuracy, speed, and memory consumption. New techniques and tools are constantly emerging, so staying updated is important.

Keywords for Fine-tuning LLMs:

Techniques:

Full fine-tuning: Updates all model parameters (most memory intensive) Adapter/bottleneck methods: Update smaller “adapter” modules instead of full weights (e.g., LoRA, Adapter-Tuning) Knowledge distillation: Train a smaller model on the outputs of a larger model (efficient but might sacrifice accuracy) Pruning: Remove unimportant connections and weights to reduce model size Quantization: Reduce precision of weights and activations (e.g., 4-bit, 8-bit) Sparse training/inference: Exploit sparsity in activations/gradients for performance Mixed-precision training: Use different precisions for different parts of the model

========= Fine-tuning Techniques for LLMs:

Full Fine-tuning:

Gradient Descent Methods (SGD, Adam, AdamW) Curriculum Learning Multi-stage Training Weight Initialization Strategies Adapter/Bottleneck Methods:

LoRA (Low-Rank Adaptation) Adapter-Tuning HAT (Hierarchical Adapter Transformers) PTB (Parameter-efficient Transfer Bottleneck) Meta-Tuning Knowledge Distillation:

Teacher-Student Architecture Hint Learning Knowledge Embedding Model Compression Pruning:

Magnitude Pruning Lottery Ticket Hypothesis Structured Pruning Knowledge-Driven Pruning Quantization:

Post-training Quantization Quantization-Aware Training Ternary Quantization Dynamic Fixed-Point Quantization Sparse Training/Inference:

Stochastic Weight Averaging (SWA) SignSGD Knowledge Sparsity Pruning with Importance Sampling Mixed-Precision Training:

Automatic Mixed Precision (AMP) Selective Precision Training Gradient Accumulation with Gradient Cheering Tensor Cores Optimization Additional Techniques:

Multi-task Learning Transfer Learning with Task Adaptation Fine-tuning with Contrastive Learning Multi-modal Fine-tuning (text & image, code & text) Meta-learning for Few-shot Adaptation Metrics for Evaluation:

Accuracy BLEU Score ROUGE Score METEOR Score Human Evaluation Frameworks and Tools:

Hugging Face Transformers NVIDIA Megatron-Turing NLG Google Pathways System BigScience BLOOM OpenAI API Cohere