# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("thillaic/MediQuill")
model = AutoModelForCausalLM.from_pretrained("thillaic/MediQuill", device_map="auto")Quick Links
Model Card for thillaic/MediQuill
This is a medicine-focussed Llama-2 fine tuned using thillaic/MedQuad-MedicalQnADataset-Llama2-1k dataset
Model Details
Model Description
The llama-2 model is finetuned with the given dataset to comprehend and respond to diverse medical questions, covering diagnoses, treatments, symptoms, medications, and more
- Developed by: ThillaiC
- Shared by : ThillaiC
- Model type: Llama-2 Fine-Tune
- Language(s) (NLP): English
- License: MIT2.0
- Finetuned from model : NousResearch/Llama-2-7b-chat-hf
Model Sources
- Repository: thillaic/MediQuill
- Code : github
Uses
This model aims to provide accurate, up-to-date information and assist healthcare professionals and individuals in making informed decisions about health concerns.
Hardware
T4 GPU
Model Card Authors [optional]
Model Card Contact
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="thillaic/MediQuill")