Fine-Tuning a Quantized LLM with LoRA: The Phi-3 Mini Walkthrough

Author(s): Akash Verma

Originally published on Towards AI.

Fine-Tuning a Quantized LLM with LoRA: The Phi-3 Mini Walkthrough

In this post, we’ll take our first steps toward efficient large language model (LLM) experimentation — setting up the environment, understanding quantization, and loading a small yet powerful model like Phi-3-Mini-4K-Instruct, we’ll fine-tune Microsoft’s Phi-3-Mini-4K-Instruct model to perform shiroyasha13/llama_text_to_sql_dataset — all while keeping GPU usage under 8GB.

This article covers the process of fine-tuning the Phi-3 Mini 4K Instruct model using quantization and Parameter-Efficient Fine-Tuning (PEFT) with Low-Rank Adapters (LoRA). It highlights the importance of quantized models for reducing GPU memory usage and demonstrates how to set up a development environment, import necessary libraries, load the model, configure training, and prepare datasets. The post also touches on efficient experimentation and the democratization of intelligence within the AI tooling ecosystem, suggesting that advanced model training is now achievable on consumer-grade hardware.

Read the full blog for free on Medium.

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Published via Towards AI