Original model description:

language: - en license: apache-2.0 library_name: transformers tags: - medical pipeline_tag: text-generation

Model Card for Model ID

Model Details

Model Description

This model is fined tune based on Google's Gemma model for creating virtual doctor or medical Asistant. It can be used in medical and healthcare AI assitant apps and chatbots.

Developed by: [Ali Bidaran]

Uses

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, GemmaTokenizer

model_id = "alibidaran/Gemma2_Virtual_doctor"
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)


tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config, device_map={"":0})

prompt = " Hi doctor, I feel a pain on my ankle, I walk hardly and with pain what do you recommend me?"
text=f"<s> ###Human: {prompt} ###Asistant: "
inputs=tokenizer(text,return_tensors='pt').to('cuda')
with torch.no_grad():
    outputs=model.generate(**inputs,max_new_tokens=200,do_sample=True,top_p=0.92,top_k=10,temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Parameters

    per_device_train_batch_size=1,
    gradient_accumulation_steps=8,
    warmup_steps=2,
    #max_steps=200,
   
    num_train_epochs=1,
    learning_rate=2e-4,
    fp16=True,
    logging_steps=100,
    output_dir="outputs",
    optim="paged_adamw_8bit",
    save_steps=500,
    ddp_find_unused_parameters=False // for training on multiple GPU