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tbuckley
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Model Details
Model Provider
tbuckley
Model Tree
Qwen/Qwen3-32B
Input Modalities
Output Modalities
Supported Functionality
python
import torchfrom peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-32B", torch_dtype=torch.bfloat16, device_map="auto")model = PeftModel.from_pretrained(base, "tbuckley/PrecepTron-32B-Management")tok = AutoTokenizer.from_pretrained("Qwen/Qwen3-32B")messages = [{"role": "system", "content": SYSTEM_PROMPT}, # see "Prompt" below{"role": "user", "content": USER_PROMPT}, # case + response to score]inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)out = model.generate(inputs, max_new_tokens=512, do_sample=False)print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
The model was trained with the exact system and user prompts below — the same
prompts it expects at inference time. The scoring rubric is not fixed — it varies per question and is passed to the model at inference time via the {rubric} field of the user prompt. The system prompt below is the generic evaluator instruction.
System prompt
text
You are an expert clinical evaluator. You will be given a clinical response and a scoring rubric.Score the response according to the rubric. You must output ONLY valid JSON with your scoring result.Your output must be a JSON object with these fields:- "score": the numeric score you assign- "justification": a brief explanation of your scoring decisionExample output:{"score": 3,"justification": "The response demonstrates..."}
User prompt template (case-specific fields filled in at inference)
text
## Case Vignette{case_vignette}## Question{question_text}## Response to Score{response}## Scoring Rubric{rubric}
Supervised fine-tuning on physician rubric scores from the study dataset. The assistant target is the reconciled physician score (or, when no reconciliation exists, a single physician's score selected deterministically), validated against the task's allowed score scale. Train/validation case IDs were held out of all reported evaluations.
This is a research artifact for the automated evaluation of medical AI. It is not a diagnostic or clinical decision-support tool and must not be used for patient care. Scores reflect agreement with the specific rubrics and physician annotations in this study and may not transfer to other rubrics, populations, or response formats.
bibtex
@article{buckley2026preceptron,title = {Scaling Clinical Judgment to Evaluate Medical AI},author = {Buckley, Thomas A. and Kanjee, Zahir and Brodeur, Peter G. andCrowe, Byron and Pettinato, Anthony M. and Shah, Aashna P. andHaimovich, Adrian D. and McCoy, Liam G. and Restrepo, Daniel andGoh, Ethan and Chen, Jonathan H. and Zwaan, Laura andGoodman, Katherine E. and Morgan, Daniel J. andAbdulnour, Raja-Elie E. and Rodman, Adam and Manrai, Arjun K.},year = {2026},note = {Preprint, forthcoming}}