beria-kalpelbe/finetuning-llm-for-african-language

# Fine-tuning BLOOM 560M on the Aya dataset for the Xhosa language Fine-tuning pretrained language models has emerged as a powerful technique for improving performance on various natural language processing (NLP) tasks. Leveraging large pretrained models, such as BLOOM 560M, and adapting them to specific languages and datasets has shown great potential for enhancing NLP capabilities. In this study, we focus on fine-tuning the BLOOM 560M model on the Aya dataset for the Xhosa language. Xhosa is a Bantu language with a rich linguistic structure, presenting unique challenges and opportunities for NLP tasks. The Aya dataset, being a comprehensive resource for the Xhosa language, provides an ideal foundation for training and evaluating language models for this specific context. Our goal is to explore the effectiveness of fine-tuning BLOOM 560M on the Aya dataset for improving performance on Xhosa language tasks. We begin by conducting a thorough hyperparameter search to determine the optimal settings for fine-tuning. We then evaluate the fine-tuned model using standard NLP metrics, with a particular focus on its ability to generate coherent and contextually relevant text in Xhosa. ## 1. Fine-tuning using LoRA method LoRA is an improved finetuning method where instead of finetuning all the weights that constitute the weight matrix of the pre-trained large language model, two smaller matrices that approximate this larger matrix are fine-tuned. These matrices constitute the LoRA adapter. This fine-tuned adapter is then loaded to the pretrained model and used for inference. For example, suppose we have an LLM with 7B parameters represented in a weight matrix W. During backpropagation, we learn a $\Delta W$ matrix, which contains information on how much we want to update the original weights to minimize the loss function during training. The weight update is then $$W_{updated} = W + \Delta W$$. The LoRA method replaces to decompose the weight changes, $\Delta W$, int …