NuvoChat is a fine-tuned version of the Mistral-7b-Instruct-v0.2 on a medical domain. The fine-tuning was done with LoRA and a quantized version of the model. The Lora was then merged back into the original model to get a standalone one.
NuvoChat is made to assist patients and clinicians by providing relevant, adapted and clear information. The model knows how to adapt effectively its tone and vocabulary absed on the user's background.
This is done by providing the model with a specific template where the status of the user (patient or professionnals) is explicitly provided.
The model can be used for text summarization.
The model is trained on an unknown dataset by Mistral and fine-tuned on a multilingual dataset from MSD. The model might have different performances depending on the language used.
Use the code below to get started with the model.
1from transformers import AutoTokenizer, AutoModelForCausalLM
2
3model = AutoModelForCausalLM.from_pretrained("nuvocare/NuvoChat", device = "auto")
4tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2")
5
6prompt = "[INST] Je suis un patient qui souhaite connaitre des informations sur la chirurgie de la cataracte [/INST]"
7
8input = tokenizer(prompt).to("cuda")
9
10answer = tokenizer.decode(model.generate(**input, max_new_tokens = 200, pad_token = tokenizer.eos_token)[0])
You can check dataset card.
Trained over 7000 steps with a total batch size of 32 (corresponding to a bit more than 1 epoch) and a sequence length of 2048.
