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Fine-Tuning Falcon-7b: Training a ChatBot Support FAQ Model on a Single GPU

Fine-Tuning Falcon-7b: Training a ChatBot Support FAQ Model on a Single GPU

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Unleash the Power of Falcon-7b with QLoRA

By Venelin Valkov, Misusings of AI, Stats & Programming

Fine-tuning LLM with QLoRA on Single GPU: Training Falcon-7b on ChatBot Support FAQ Dataset

In this tutorial, you will learn how to fine-tune Falcon-7b, one of the best open large language models, on a custom dataset containing FAQ examples from a chatbot support FAQ dataset.

We will explore the process of training the model, evaluating its performance before and after fine-tuning, and comparing the results.

By the end of this tutorial, you will have a solid understanding of how to fine-tune a large language model on a single GPU using the QLoRA technique.

Steps:

  • Step 1: Introduction to Falcon-7b Learn about Falcon-7b, an open-source large language model provided by the Technology Innovation Institute. Discover its features, licensing, and performance on the language model leaderboard.
  • Step 2: Setting Up the Environment Download the required libraries, including bits and bytes, PyTorch, Transformers, and Accelerate. Install the necessary dependencies and prepare the Google Cloud notebook for fine-tuning.
  • Step 3: Understanding the Dataset Explore the chatbot support FAQ dataset available on Kaggle. Analyze the structure of the dataset and extract the necessary question-answer pairs for fine-tuning.
  • Step 4: Loading and Preparing the Model Load the Falcon-7b model and initialize the tokenizer. Configure the model for fine-tuning using the QLoRA technique. Print the trainable parameters to understand the model's architecture.
  • Step 5: Training the Model Start the training process with the prepared dataset. Set the training parameters, including batch size, number of epochs, and optimizer. Monitor the training progress using TensorBoard.
  • Step 6: Evaluating the Fine-Tuned Model Generate responses from the fine-tuned model using sample prompts. Compare the responses with the original dataset to assess the model's performance.
  • Step 7: Saving and Deploying the Model Save the fine-tuned model and upload it to the Hugging Face Hub for easy access and sharing. Learn how to load the model for future use and generate responses.
  • Step 8: Conclusion Summarize the tutorial and provide additional resources for further exploration. Encourage readers to subscribe to ML Expert Pro for a complete text and source code tutorial.