Top 5 Fine Tuning LLM Techniques & Inference To Improve AI
The Top 5 Fine Tuning LLM Techniques and Inference Tricks to Boost Your AI Proficiency. With LLM inference and fine-tuning, your generative AI (GenAI) systems will perform even better
The performance of the model is largely dependent on efficient data preparation. Having a clean and well-labeled dataset may greatly improve training results
Optimizing hyperparameters is essential to attaining peak performance. Because of the large search space, choosing the appropriate learning rate, batch size, and number of epochs may be challenging
Training time and memory may be greatly decreased by using sophisticated methods like parameter-efficient fine-tuning (PEFT), distributed training, and mixed precision
Minimizing inference latency is essential for successfully deploying LLMs, but it may be difficult because of their complexity and scale
Maintaining low latency, fault tolerance, and load balancing are some of the issues associated with large-scale LLM deployment
They go over methods and tools such as Hugging Face for the Optimum for Intel Gaudi library, distributed training, LoRA fine-tuning of Llama 7B, and mor