Enhanced Language Generation Capabilities of Mistral 7B Using Quantized Parameter Efficient Fine-Tuning with Lora and Rag

The language generation capability of Large Language Models (LLMs) is very helpful in various domains, from general question answering to creative writing. This research work enhances the language generation capabilities of existing base language generation models, which are based on Mistral 7B. This is a state-of-the-art open-source LLM model. Which utilized two approaches: Parameter-Efficient Fine-Tuning (PEFT) and Retrival-Augmented Genration (RAG). Additionally, the work describes the fine-tuning process of mistral 7 B v 0.1 as a base model using PEFT. Which is based on LoRA, Tatsu-lab's Alpaca instruction-question-answering dataset, used to overcome the limitations of the traditional fine-tuning process. This work also suggests a hybrid approach for RAG and fine-tuned models that combines the benefits of both fine-tuning and retrieval augmentation. Moreover, this work shows the comparative performance analysis of all three models developed by the mentioned techniques and the base Mistral 7 B model using BLEU and ROUGH scores using Alpaca dataset. This shows the effectiveness of the hybrid techniques to enhance the domain-specific language generation capabilities of the base Large Language Model that is Mistral $7 ~\mathrm{B} \mathbf{v} 0.1$ within constrained resources.