The MedicalChat-Phi-3.5-mini-instruct- fine-tuned model is designed to simulate doctor-patient conversations, offering medical consultations and suggestions based on patient queries. However, its accuracy may be limited in real-world scenarios, as the training dataset was relatively small.

🔍 Model Overview

🧑‍⚕️ Developed by: syubraj
📜 Model Type: Causal Language Model (CausalLM)
🗣️ Language: English
📜 License: MIT License
🛠️ Fine-tuned From: microsoft/phi-3.5-mini-instruct

Uses

Direct Use

!pip install unsloth

Python

from unsloth import FastLanguageModel

model, tokenizer = FastLanguageModel.from_pretrained("syubraj/Phi3.5-medicalchat-unsloth",
                                                      max_seq_length = 1024,
                                                      load_in_4bit = True,
                                                      dtype = None
)
user_query = "<Your medical query here>"
system_prompt = """You are a trusted AI-powered medical assistant. Analyze patient queries carefully and provide accurate, professional, and empathetic responses. Prioritize patient safety, adhere to medical best practices, and recommend consulting a healthcare provider when necessary."""


message = [
    {"role": "system", "content": system_prompt},
    {"role": "human", "content": user_query}
]

# Creating message based on tokenizers chat template
prompt = tokenizer.apply_chat_template(message, tokenize = False, add_generation_prompt = True)

FastLanguageModel.for_inference(model)

# Tokenizing inputs
inputs = tokenizer(prompt, return_tensors = "pt").to("cuda")

# Output Generated
outputs = model.generate(**inputs, max_new_tokens=256, use_cache=True) # Change the `max_new_tokens` according to required objective
tokenizer.batch_decode(outputs)

Model Results

Step	Training Loss
10	2.53
20	2.20
30	1.95
40	2.01
50	1.97
60	2.02