Introduction to Finetuned-QLoRA-Falcon7B-Medical Project
Introduction
Mental health is a crucial part of our overall well-being, yet it is often misunderstood or stigmatized. Society's general lack of understanding can lead to misconceptions and fear surrounding mental health issues. This project addresses these challenges by using technology to promote empathy, challenge stereotypes, and enhance accessibility to mental health care.
Core Rationale
In our digitally-connected world, chatbots offer an innovative solution for providing mental health support. These digital assistants are available 24/7, offering immediate assistance to anyone in need. Though they can't replace professional care, chatbots provide non-judgmental, empathetic responses that can serve as a critical support system, especially during distressing moments.
Dataset
The project utilizes a dataset curated from online FAQs, popular healthcare blogs like WebMD, Mayo Clinic, and Healthline, and other authoritative sources. The data has been transformed into a conversational format, simulating interactions between patients and doctors. Importantly, all personal identifying information has been anonymized to protect individual privacy. You can explore the dataset here: mental_health_chatbot_dataset.
Model Finetuning
The heart of the project lies in fine-tuning the Falcon-7B language model, a large, pre-trained model tailored to understand and generate text related to mental health. Using a technique called QLoRA, the model was specifically adjusted using the custom mental health dataset mentioned. Remarkably, this finetuning process was rapid, taking less than an hour with the use of Nvidia A100 from Google Colab Pro. Even on slower GPUs like the Nvidia T4, the process is feasible with some adjustments. For more technical insights, consider reading the blog post: Fine-tuning of Falcon-7B.
Model Inference
After finetuning, the model was made available for use. Users can experiment with it by running a notebook that utilizes Gradio to create a chatbot-like interface. This allows for interactive testing with customizable parameters, helping users evaluate the quality of the model's generated responses. The model's responses are generally produced within three minutes, demonstrating its efficiency. Here is the link to the finetuned model: finetuned-mental-health-conversational.
Conclusion
This project exemplifies how technology can be leveraged to enhance our understanding and treatment of mental health issues, providing accessible support through digital means. For those interested in the technical aspects of the process, a detailed blog is available: Fine-tuning of Falcon-7B. For any further inquiries, consider opening an issue on the project's repository or leaving a comment on the blog. If you appreciate this work, feel free to support it by starring the repository.