Fine-Tuning 1B LLaMA 3.2: A Comprehensive Step-by-Step Guide with Code

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Building a Mental Health Chatbot by fine tuning Llama 3.2

Mental health is a critical aspect of overall well being for emotional, psychological and social dimensions.

Let’s find some mental peace 😊 by fine tuning Llama 3.2.

We need to install unsloth for 2x fast training with less size`m

%%capture
!pip install unsloth
!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"

We are going to use Unsloth because it significantly enhances the efficiency of fine-tuning large language models (LLMs) specially LLaMA and Mistral. With Unsloth, we can use advanced quantization techniques, such as 4-bit and 16-bit quantization, to reduce the memory and speed up both training and inference. This means we can deploy powerful models even on hardware with limited resources but without compromising on performance.

Additionally, Unsloth broad compatibility and customization options allow to do the quantization process to fit the specific needs of products. This flexibility combined with its ability to cut VRAM usage by up to 60%, makes Unsloth an essential tool in AI toolkit. Its not just about…