Unleashing the Power of Next-Gen NLP Models

Mastering Llama 2: A Comprehensive Guide to Fine-Tuning in Google Colab

Dive deep into Llama 2 — the cutting-edge NLP model. This guide covers everything from setup and loading to fine-tuning and deployment in Google Colab.

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TL;DR

This guide explores the intricacies of fine-tuning the Llama 2–7B, a large language model by Meta, in Google Colab. Addressing initial setup requirements, we delve into overcoming memory constraints using quantization. Leveraging the patient-doctor dataset, we adapt the model’s broad capabilities to medical interactions. Key techniques like Low-Rank Adaptation (LoRA) and QLoRA are unpacked, providing an efficient approach to fine-tuning massive models. Read on for a step-by-step breakdown, from preliminary setups to training results.

Here is what we cover in this article:

• Introduction to Llama 2: The Powerhouse of Language Models
• Preliminaries
• Loading the model
• Tokenizer
• Inference
• Fine-Tunning
• Saving the Model
• Load and export the fine-tuned model
• Conclusions