---
license: mit
datasets:
- sajjadhadi/disease-diagnosis-dataset
base_model:
- Qwen/Qwen2.5-3B
pipeline_tag: text-classification
tags:
- biology
language:
- zho
- eng
- fra
- spa
- por
- deu
- ita
- rus
- jpn
- kor
- vie
- tha
- ara
library_name: adapter-transformers
---

# Disease Diagnosis Adapter

A fine-tuned adapter for the Qwen/Qwen2.5-3B 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).
The data is over-saturated in some diagnosis, I limit the number of diagnosis and take a limit number of them as training tags.


## 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]}")