Finetuning & Improving Performance of LLMs

Q1. Explain LORA and QLORA for LLMs finetuning.

Ans:

LoRA (Low-Rank Adaptation) is a widely used, parameter-efficient fine-tuning technique for training custom LLMs. It is used to adapt a pre-trained LLM to a new task by adding a small number of task-specific parameters. LoRA is based on the low-rank matrix factorization technique, which reduces the number of parameters required for fine-tuning. This technique has been shown to be effective in reducing the number of parameters required for fine-tuning, while maintaining or improving the performance of the model .

QLoRA (Quantized Low-Rank Adaptation) is an extension of LoRA that further reduces the memory requirements of fine-tuning LLMs. QLoRA first quantizes the LLM to 4-bit to reduce its size and then performs LoRA training in 32-bit precision for effectiveness. Weights temporarily revert to 32-bit. Weights are quantized and de-quantized in a stepwise manner to manage GPU memory. QLoRA enables efficient fine-tuning of giant LLMs on typical GPUs.

Interesting resources: You can read the following articles to ger more details on same.

• https://dzone.com/articles/fine-tuning-llm-parameter-efficient-fine-tuning-pe
• https://www.datacamp.com/tutorial/mastering-low-rank-adaptation-lora-enhancing-large-language-models-for-efficient-adaptation

Q2. Why do we use LoRA, and QLoRa for LLMs?

Ans: LoRA and QLoRA are two widely used techniques for training custom Large Language Models (LLMs) that are parameter-efficient and memory-efficient . LoRA stands for Low Rank Adapters, which is a fine-tuning technique that helps in training LLMs with fewer parameters. QLoRA, on the other hand, is a memory-efficient version of LoRA that quantizes the LLM weights to 4-bits, reducing the model's memory footprint by 8x. QLoRA then finetunes the quantized LLM using LoRA, which enables the refined model to preserve the majority of the accuracy of the original LLM while being significantly smaller and quicker.

Q3: How to improve performance of LLMs?

Ans: There are three major techniques for optimizing the performance of large language models (LLMs):

• Prompt optimization: This involves tailoring the prompts to guide the model's responses. For example, you can use keywords, templates, or natural language instructions to elicit the desired output from the model.
• Retrieval-augmented generation (RAG): This enhances the model's context understanding through external data. For example, you can use a knowledge base, a search engine, or a document collection to provide relevant information to the model.
• Fine-tuning: This is the process of adjusting the model's parameters on a specific dataset or task. For example, you can fine-tune a pre-trained model on a domain-specific corpus or a downstream task to improve its accuracy and relevance.

These techniques can be combined and applied iteratively to maximize the performance of LLMs on a given task. 😊