gemma-2b-mini-doctor

Gemma-2B-Mini-Doctor

This is a fine-tuned version of the Gemma-2B model specifically adapted for medical-related tasks.

Model Details

• Model Name: Gemma-2B-Mini-Doctor
• Base Model: Gemma-2B
• Fine-tuned by: Yevhen Solovei | Maverkick
• Fine-tuning Dataset: mamachang/medical-reasoning
• Number of Parameters: 2 billion

Training Details

• Training Epochs: 3
• Learning Rate: 2e-5
• Batch Size: 16
• Optimizer: AdamW

Intended Use

• Use Cases: Medical question answering, medical text generation
• Limitations: Not suitable for real-time medical advice, should not be used as a substitute for professional medical advice.

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("gemma-2b-mini-doctor")
tokenizer = AutoTokenizer.from_pretrained("gemma-2b-mini-doctor")

inputs = tokenizer("What are the symptoms of flu?", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))