LOINC Medical Terminology Mapper

Fine-tuned Llama-3.2-3B model for mapping German medical terms to LOINC codes using Chain-of-Thought reasoning.

Model Details

  • Base Model: unsloth/Llama-3.2-3B-Instruct
  • Fine-tuning Method: LoRA (Low-Rank Adaptation)
  • Training Framework: Unsloth + Hugging Face Transformers
  • Language: German (primary), English (secondary)
  • Task: Medical terminology to LOINC code mapping

Performance

  • Accuracy: 0.00%
  • Total Samples: 0
  • Correct Predictions: 0

Training Configuration

  • LoRA Rank: 64
  • LoRA Alpha: 128
  • Learning Rate: 0.0002
  • Batch Size: 64
  • Epochs: 1
  • Precision: BF16

Usage

from unsloth import FastLanguageModel

# Load model
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name="Franc105/loinc-mapper",
    max_seq_length=2048,
    dtype=None,
    load_in_4bit=True,
)

# Enable inference mode
FastLanguageModel.for_inference(model)

# Format input
messages = [
    {"role": "system", "content": "Du bist ein Experte für medizinische Terminologie und LOINC-Mapping."},
    {"role": "user", "content": "Begriff: Glukose\nEinheit: mg/dL\nBeschreibung: Blutzucker"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

# Generate
outputs = model.generate(
    input_ids=inputs,
    max_new_tokens=512,
    temperature=0.1,
    top_p=0.9,
    do_sample=True
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This model is designed for:

  • Mapping German medical terminology to standardized LOINC codes
  • Supporting clinical documentation systems
  • Assisting healthcare professionals with terminology standardization

Limitations

  • Primarily trained on German medical terms
  • Requires structured input format (Begriff, Einheit, Beschreibung)
  • May not cover all edge cases in medical terminology

Training Data

  • Custom dataset of German LOINC mappings
  • Augmented with synonyms from RELATEDNAMES2
  • Chain-of-Thought reasoning examples

Citation

If you use this model, please cite:

@misc{loinc-mapper-2024,
  title={LOINC Medical Terminology Mapper},
  author={IMESO IT GmbH},
  year={2024},
  publisher={Hugging Face},
  howpublished={\url{Franc105/loinc-mapper}}
}

License

Apache 2.0

Contact

For questions or issues, please open an issue on the model repository.

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