Fine-tuning Meta's Llama 2 7B on Google Colab
Overcoming Memory and Computing Constraints
In this notebook and tutorial, we delve into the fine-tuning of Meta's Llama 2 7B model using Google Colab. We will explore innovative techniques to address the memory and computing limitations associated with working with such a large language model.
Fine-tuning Llama 2 on a T4 GPU
In this section, we focus on fine-tuning a Llama 2 model with 7 billion parameters using a T4 GPU equipped with 16 GB of VRAM. We will guide you through the necessary steps and configurations to optimize the fine-tuning process.
Understanding Llama 2
Meta's Llama 2 is a family of powerful large language models (LLMs). In this article, we will provide an overview of the capabilities and limitations of Llama 2 and discuss its potential applications in various domains.
Challenges of Fine-tuning LLMs on Google Colab
Fine-tuning substantial LLMs, such as Llama 2, on Google Colab presents unique challenges due to memory and computational constraints. We will explore these challenges and discuss strategies to mitigate them.
Fine-tuning Techniques for Large LLMs
There are two primary fine-tuning techniques for large LLMs: full model fine-tuning and parameter-efficient fine-tuning. We will compare these techniques and provide guidelines on choosing the most appropriate method for your specific task.
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