Instructions to use Kberta2014/Medical_Chat_Bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Adapters
How to use Kberta2014/Medical_Chat_Bot with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("<base-model-id>") model.load_adapter("Kberta2014/Medical_Chat_Bot", set_active=True) - Notebooks
- Google Colab
- Kaggle
Commit History
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RREADME.md
Verified signatureMedicalChatBot is a state-of-the-art, domain-specialized chatbot fine-tuned on high-quality medical dialogue and question-answer datasets using LoRA (Low-Rank Adaptation) on top of the Mistral-7B-Instruct large language model. It is designed to provide accurate, natural language responses to medical and health-related queries for educational, research, and health literacy use cases. 🚀 Key Features Foundation Model: Built on mistralai/Mistral-7B-Instruct, a powerful open-weight instruction-tuned LLM with strong zero-shot reasoning capabilities. LoRA Fine-Tuning: Efficient fine-tuning method using PEFT that adapts only a small number of trainable parameters (~7 million), enabling training even on low-resource environments like Google Colab. Domain Data: Trained on kberta2014/medical-chat-dataset, a curated dataset composed of patient-symptom-dialogs, condition-based queries, and verified health questions. Optimized for Inference: Lightweight, fast, and easily deployable in inference pi…b8d45d8