Instructions to use Agnania/EviNurse-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Agnania/EviNurse-32B with Transformers:

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

pipe = pipeline("text-generation", model="Agnania/EviNurse-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Agnania/EviNurse-32B")
model = AutoModelForCausalLM.from_pretrained("Agnania/EviNurse-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Agnania/EviNurse-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Agnania/EviNurse-32B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Agnania/EviNurse-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Agnania/EviNurse-32B

SGLang

How to use Agnania/EviNurse-32B 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 "Agnania/EviNurse-32B" \
    --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": "Agnania/EviNurse-32B",
		"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 "Agnania/EviNurse-32B" \
        --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": "Agnania/EviNurse-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Agnania/EviNurse-32B with Docker Model Runner:
```
docker model run hf.co/Agnania/EviNurse-32B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

EviNurse-32B

EviNurse-32B is a domain-specific large language model developed for evidence-based nursing. It is based on Qwen3-32B and adapted to nursing-domain use through supervised fine-tuning. In the EviNurse study, the model was evaluated with automated multiple-choice testing, expert assessment of short-answer responses, and real-world usability evaluation among nurses.

The goal of this release is to support reproducible research on nursing AI by providing a model artifact that can be evaluated together with the public NursData-MCQ benchmark.

Resources

Resource	Link
Benchmark dataset	NursData-MCQ
Code repository	EviNurse
Model repository	EviNurse-32B

Model Details

Base model: Qwen3-32B
Architecture: Qwen3ForCausalLM
Parameters: approximately 32.8B
Precision: bfloat16
Context length in config: 40,960 tokens
Training method: supervised fine-tuning for nursing-domain question answering and evidence-based nursing use cases
Intended interface: chat/text generation, preferably through an OpenAI-compatible serving engine such as vLLM

Intended Use

This model is intended for research on:

Nursing-domain language models
Evidence-based nursing question answering
Retrieval-augmented generation for nursing evidence
Automated nursing MCQ benchmark evaluation
Comparative evaluation of general-purpose and domain-specific LLMs

This model is not a medical device and should not be used as a substitute for professional clinical judgment.

Evaluation

The automated evaluation benchmark used with this model is available at:

https://huggingface.co/datasets/Agnania/NursData-MCQ

The benchmark contains 3,438 Chinese nursing multiple-choice questions. Accuracy is computed by exact match between the extracted final option letter and the standard answer.

Example evaluation code is provided in:

https://github.com/Creeper12345/EviNurse

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Agnania/EviNurse-32B"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "You are an evidence-based nursing assistant."},
    {"role": "user", "content": "请简要说明预防压疮的核心护理措施。"},
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Serving with vLLM

python -m vllm.entrypoints.openai.api_server \
  --model Agnania/EviNurse-32B \
  --served-model-name EviNurse \
  --tensor-parallel-size 4 \
  --trust-remote-code

Then evaluate through the OpenAI-compatible endpoint:

python scripts/evaluate_mcq.py \
  --input data/NursData-MCQ/evinurse_automated_eval_3438.json \
  --output outputs/evinurse_mcq_predictions.json \
  --base-url http://127.0.0.1:8000/v1 \
  --model EviNurse \
  --api-key EMPTY

Release Notes

Before uploading this directory, ensure that all weight shards referenced in model.safetensors.index.json are present:

model-00001-of-00004.safetensors
model-00002-of-00004.safetensors
model-00003-of-00004.safetensors
model-00004-of-00004.safetensors

The non-weight configuration files in this release have been checked for local training and server path leaks.

License

EviNurse-32B is released under the Apache License 2.0.

Base model:

Qwen3-32B

Please also comply with the license of the original base model and any applicable data/source licenses.

This model is intended for research and educational use in nursing and healthcare AI. It is not a medical device and should not be used as the sole basis for clinical decision-making.

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Safetensors

Model size

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Tensor type

BF16

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