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))
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