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Agnania
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Model Details
Model Provider
Agnania
Model Tree
Qwen/Qwen3-32B
Input Modalities
Output Modalities
Supported Functionality
| Resource | Link |
|---|---|
| Benchmark dataset | NursData-MCQ |
| Code repository | EviNurse |
| Model repository | EviNurse-32B |
This model is intended for research on:
This model is not a medical device and should not be used as a substitute for professional clinical judgment.
The automated evaluation benchmark used with this model is available at:
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:
python
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))bash
python -m vllm.entrypoints.openai.api_server \ --model Agnania/EviNurse-32B \ --served-model-name EviNurse \ --tensor-parallel-size 4 \ --trust-remote-codeThen evaluate through the OpenAI-compatible endpoint:
bash
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 EMPTYBefore uploading this directory, ensure that all weight shards referenced in model.safetensors.index.json are present:
text
model-00001-of-00004.safetensorsmodel-00002-of-00004.safetensorsmodel-00003-of-00004.safetensorsmodel-00004-of-00004.safetensorsThe non-weight configuration files in this release have been checked for local training and server path leaks.
EviNurse-32B is released under the Apache License 2.0.
Base model:
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.
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))python -m vllm.entrypoints.openai.api_server \ --model Agnania/EviNurse-32B \ --served-model-name EviNurse \ --tensor-parallel-size 4 \ --trust-remote-codepython 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 EMPTYmodel-00001-of-00004.safetensorsmodel-00002-of-00004.safetensorsmodel-00003-of-00004.safetensorsmodel-00004-of-00004.safetensors