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GitHub - iamarunbrahma/finetuned-qlora-falcon7b-medical: Finetuning of Falcon-7B LLM using QLoRA on Mental Health Conversational Dataset

Aug 25, 2023 - github.com
The article discusses the fine-tuning of the Falcon-7B Large Language Model using QLoRA on a mental health conversational dataset. The dataset was curated from online FAQs, healthcare blogs, and wiki articles related to mental health, and was pre-processed in a conversational format. The author used a sharded Falcon-7B pre-trained model and fine-tuned it using the QLoRA technique on this custom mental health dataset. The fine-tuning process took less than an hour on Nvidia A100 from Google Colab Pro, but it could also be trained on a free-tier GPU using Nvidia T4 provided by Colab.

The author emphasizes that while mental health chatbots can be helpful, they are not a replacement for professional mental health care. They can complement existing mental health services by providing additional support and resources. The fine-tuned model has been updated and a chatbot-like interface using Gradio as a frontend for demo has been provided. The author has also written a detailed technical blog explaining key concepts of QLoRA and PEFT fine-tuning method.

Key takeaways:

  • The Falcon-7B LLM has been fine-tuned using QLoRA on a mental health conversational dataset, offering a chatbot platform for individuals seeking support.
  • The dataset used was curated from online FAQs, healthcare blogs, and wiki articles related to mental health, pre-processed in a conversational format.
  • The fine-tuning process was done using Nvidia A100 from Google Colab Pro, achieving a training loss of 0.031 after 320 steps.
  • A detailed technical blog explaining the key concepts of QLoRA and PEFT fine-tuning method is available for further understanding.
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