Instructions to use Franc105/loinc-mapper with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Franc105/loinc-mapper with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Franc105/loinc-mapper")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Franc105/loinc-mapper", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Franc105/loinc-mapper with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Franc105/loinc-mapper"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Franc105/loinc-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Franc105/loinc-mapper

SGLang

How to use Franc105/loinc-mapper with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Franc105/loinc-mapper" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Franc105/loinc-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Franc105/loinc-mapper" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Franc105/loinc-mapper",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Franc105/loinc-mapper with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Franc105/loinc-mapper to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Franc105/loinc-mapper to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Franc105/loinc-mapper to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Franc105/loinc-mapper",
    max_seq_length=2048,
)

Docker Model Runner
How to use Franc105/loinc-mapper with Docker Model Runner:
```
docker model run hf.co/Franc105/loinc-mapper
```

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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Model tree for Franc105/loinc-mapper

Base model

meta-llama/Llama-3.2-3B-Instruct

Finetuned

unsloth/Llama-3.2-3B-Instruct

Finetuned

(609)

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