phi3-auditor-merged

pip install transformers torch accelerate

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "PhantomAjusshi/phi3-auditor-merged"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
    device_map="auto",
    trust_remote_code=True,  # Required for custom Phi-3 modeling files
)

report = """{
  "auc": 0.863,
  "accuracy": 0.83,
  "precision": 0.79,
  "recall": 0.69,
  "f1": 0.79,
  "ece": 0.278,
  "brier": 0.263,
  "drift": 0.03,
  "missing_rate": 0.003,
  "label_shift": 0.06,
  "pos_rate": 0.10,
  "data_integrity_issues": 0
}"""

prompt = (
    f"<|system|>\nYou are a clinical AI auditor model.\n"
    f"<|user|>\nInstruction: Analyze the clinical model report and classify its health.\n\nReport:\n{report}\n"
    f"<|assistant|>\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    outputs = model.generate(
        **inputs,
        max_new_tokens=400,
        temperature=0.7,
        top_p=0.9,
        repetition_penalty=1.2,
        do_sample=True,
    )

response = tokenizer.decode(outputs[0], skip_special_tokens=True)
# Extract only the assistant's reply
reply = response.split("<|assistant|>")[-1].strip()
print(reply)

<|system|>
You are an AI auditor analyzing clinical model performance reports.
<|user|>
Instruction: Analyze the clinical model report and classify its health.

Report:
{ ...metrics JSON... }
<|assistant|>
Category: <label>
Explanation: <explanation>

@misc{phi3-auditor-merged,
  author       = {PhantomAjusshi},
  title        = {phi3-auditor-merged: Phi-3-mini fine-tuned for clinical AI model auditing},
  year         = {2025},
  publisher    = {HuggingFace},
  url          = {https://huggingface.co/PhantomAjusshi/phi3-auditor-merged}
}