---
license: mit
datasets:
- sajjadhadi/disease-diagnosis-dataset
base_model:
- ministral/Ministral-3b-instruct
pipeline_tag: text-classification
tags:
- biology
language:
- en
library_name: adapter-transformers
---

# Disease Diagnosis Adapter

A fine-tuned adapter for the Ministral-3b-instruct model specialized in disease diagnosis and classification.
Trained through MLX and MPI, to test performance and accuracy.

## Overview

This adapter enhances the base Ministral-3b-instruct model to improve performance on medical diagnosis tasks. It was trained on the [disease-diagnosis-dataset](https://huggingface.co/datasets/sajjadhadi/disease-diagnosis-dataset).

## Usage

```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer

# Load model and tokenizer
model_name = "naifenn/diagnosis-adapter"
model = AutoModelForSequenceClassification.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Example input
text = "Patient presents with fever, cough, and fatigue for 3 days."
inputs = tokenizer(text, return_tensors="pt")

# Get prediction
outputs = model(**inputs)
prediction = outputs.logits.argmax(-1).item()
print(f"Predicted diagnosis: {model.config.id2label[prediction]}")