metadata
language:
- de
- en
license: apache-2.0
tags:
- medical
- loinc
- terminology-mapping
- llama-3
- unsloth
base_model: unsloth/Llama-3.2-3B-Instruct
datasets:
- custom-loinc-dataset
metrics:
- accuracy
library_name: transformers
pipeline_tag: text-generation
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.