mhylle/gemma3-270m-9liner

Gemma 3 270M - MIST 9-Liner Fine-tuned

This model is a full parameter fine-tuned version of google/gemma-3-270m-it trained on MIST 9-liner (medical evacuation request) data.

Model Details

• Base Model: google/gemma-3-270m-it
• Training Type: Full parameter fine-tuning (not LoRA)
• Parameters: 268M (100% trainable)
• Training Data: 9,500 MIST 9-liner examples
• Epochs: 3
• Final Loss: 0.191
• Token Accuracy: 92.5%

Training Configuration

• Learning Rate: 2e-5
• Batch Size: 8
• Gradient Accumulation: 2
• Max Sequence Length: 1024
• Optimizer: AdamW
• Precision: bfloat16

Intended Use

This model is designed for parsing and generating MIST 9-liner medical evacuation requests. The 9-liner format is a standardized military medical evacuation request format.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("mhylle/gemma3-270m-9liner")
tokenizer = AutoTokenizer.from_pretrained("mhylle/gemma3-270m-9liner")

# Example usage
prompt = "Convert this 9-liner to a medical record: ..."
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Limitations

• Trained specifically on 9-liner format data
• May not generalize well to other medical documentation formats
• Should be validated before use in real medical applications

License

This model inherits the Gemma license from the base model.