This README provides a summary of the GitHub repository dedicated to fine-tuning the Llama-2-7b-chat-hf model on Red Hat OpenShift Data Science (RHODS). The repository guides through the process of optimizing this Large Language Model (LLM) for use in GPU environments with limited memory.
LLMs, such as "llama-2-7b-chat-hf," are capable of understanding and generating human-like text. They are pre-trained on extensive language datasets, making them highly effective in various NLP tasks.
This model, part of the Llama series, is designed specifically for chat-based applications, with 7 billion parameters making it adept at conversational scenarios.
The process involves adapting a pre-trained model to specific tasks or domains, enhancing its performance for particular applications.
- Model Parameters: Define the model's learning attributes.
- Weight Importance: Determines the significance of features in predictions.
- Gradient Descent and Learning Rate: Crucial for navigating the optimization landscape.
- Activations: Key to decision-making in neural networks but also a factor in memory usage.
- Precision Dilemmas: Balancing between FP16, BF16, and 8-bit formats for memory efficiency.
- Half Precision: Reduces memory footprint and computational requirements.
- Quantization: Simplifies number representations for efficient memory use.
- Low Rank Adaptation (LoRA): Compresses large matrices in neural networks.
- Gradient Accumulation: Efficient memory utilization for large mini-batch training.
- Paged Optimizers and QLoRA: Managing memory spikes, especially for large models.
- Gradient Checkpointing: Saves memory by selectively storing intermediate activations.
- Knowledge Distillation with K-Bit Quantization: Compresses larger models into smaller, efficient ones.