Instructions to use tsilva/clinical-field-mapper-causal_lm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tsilva/clinical-field-mapper-causal_lm with Transformers:

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

pipe = pipeline("text-generation", model="tsilva/clinical-field-mapper-causal_lm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsilva/clinical-field-mapper-causal_lm")
model = AutoModelForCausalLM.from_pretrained("tsilva/clinical-field-mapper-causal_lm")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tsilva/clinical-field-mapper-causal_lm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tsilva/clinical-field-mapper-causal_lm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilva/clinical-field-mapper-causal_lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tsilva/clinical-field-mapper-causal_lm

SGLang

How to use tsilva/clinical-field-mapper-causal_lm 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 "tsilva/clinical-field-mapper-causal_lm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilva/clinical-field-mapper-causal_lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "tsilva/clinical-field-mapper-causal_lm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tsilva/clinical-field-mapper-causal_lm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tsilva/clinical-field-mapper-causal_lm with Docker Model Runner:
```
docker model run hf.co/tsilva/clinical-field-mapper-causal_lm
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Card for tsilva/clinical-field-mapper-causal_lm

This model is a fine-tuned version of distilbert/distilgpt2 on the tsilva/clinical-field-mappings dataset. Its purpose is to normalize healthcare database column names to a standardized set of target column names.

Task

This is a causal language model designed to map free-text field names to standardized schema terms.

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tsilva/clinical-field-mapper-causal_lm") model = AutoModelForCausalLM.from_pretrained("tsilva/clinical-field-mapper-causal_lm")

def predict(input_text): inputs = tokenizer(input_text + "|", return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=50) print(tokenizer.decode(outputs[0], skip_special_tokens=True))

predict('cardi@')

Evaluation Results

train accuracy: 98.24%
validation accuracy: 89.84%
test accuracy: 89.35%

Training Details

Seed: 42
Epochs scheduled: 50
Epochs completed: 14
Early stopping triggered: Yes
Final training loss: 1.3344
Final evaluation loss: 1.1981
Optimizer: adamw_bnb_8bit
Learning rate: 0.0005
Batch size: 512
Precision: fp16
DeepSpeed enabled: True
Gradient accumulation steps: 1

License

Specify your license here (e.g., Apache 2.0, MIT, etc.)

Limitations and Bias

Model was trained on a specific clinical mapping dataset.
Performance may vary on out-of-distribution column names.
Ensure you validate model outputs in production environments.

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F16

Model tree for tsilva/clinical-field-mapper-causal_lm

Quantizations

2 models

Evaluation results

train Accuracy on tsilva/clinical-field-mappings
self-reported

0.982
validation Accuracy on tsilva/clinical-field-mappings
self-reported

0.898
test Accuracy on tsilva/clinical-field-mappings
self-reported

0.893