Medical_Homework2 is a fine-tuned version of SmolAI/SmolLM2-1.7B, trained specifically on structured medical question-answer data and short reasoning tasks.
The model aims to provide concise, accurate, and educational medical explanations suitable for students and basic learning purposes.

Model Overview

This model is optimized for medical comprehension tasks such as:

Short medical answers
Step-by-step reasoning
Explanations of conditions, symptoms, and basic physiology
Educational or homework-style responses

It is not designed for professional medical diagnosis or treatment decisions.

Intended Use

Recommended Use Cases

Medical homework and assignment assistance
Explanation of medical concepts in simple language
Introductory physiology and pathology topics
Basic reasoning about medical questions

Not Recommended

Real-world clinical decision-making
Emergency or diagnostic use
Any situation requiring professional medical judgement

Training Data

The model was fine-tuned using:

Synthetic medical question-answer pairs
Simplified educational medical explanations
Instruction-answer examples
Homework-style reasoning data

No real patient data or clinical records were used.

Training Details

Base model: SmolAI/SmolLM2-1.7B
Fine-tuning objective: Causal language modeling
Method: Full or LoRA fine-tuning (depending on your actual setup)
Optimizer: AdamW
Typical epochs: 1–3

If you want, a full training script section can be added.

Usage Example

python

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Abeersherif/Medical_Homework2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Explain what type 2 diabetes is in simple terms."

inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=150,
    temperature=0.7,
    top_p=0.9,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))