Phi-4-mini Clinical (PyTorch / Transformers)
A specialized 3.8B biomedical & clinical reasoning foundation model built on Microsoft's Phi-4-mini-instruct, formatted for standard Hugging Face transformers and PyTorch.
3-Stage Transfer Learning Curriculum
- Stage 1 (STEM Foundation): 116,000 instruction pairs across NCERT Classes 6–12 (Physics, Chemistry, Biology) eliminating foundational science hallucinations.
- Stage 2 (PubMed 2026 Evidence): 12 recent 2026 clinical update archives from NCBI FTP covering survival outcomes (OS, PFS, HR), targeted therapeutics, and clinical trial endpoints.
- Stage 3 (Comprehensive Internal Medicine): Balanced multi-specialty clinical curriculum (cardiology, nephrology, endocrinology, pulmonology) with an active oncology replay buffer.
All LoRA adapter weights have been permanently fused into the base weights.
Benchmark Results (PubMedQA)
Evaluated on 50 biomedical research decision tasks from PubMedQA:
| Model |
Accuracy |
Score |
Avg Latency |
Relative Improvement |
| Base Phi-4-mini (4-bit) |
26.0% |
13 / 50 |
1.02s / question |
Baseline |
| Phi-4-mini Clinical (Merged) |
40.0% |
20 / 50 |
0.93s / question |
+53.8% relative gain |
Quickstart with Transformers
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "charakaweb/phi4-clinical"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
prompt = "<|user|>\nWhat are the first-line therapeutic recommendations for heart failure with preserved ejection fraction (HFpEF)?<|end|>\n<|assistant|>\n"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Clinical Disclaimer
This model is intended solely for biomedical research, educational exploration, and experimental evaluation. It is not an FDA-cleared medical device and must not be used as a substitute for professional clinical judgment, diagnosis, or treatment.
Verified Medical Benchmark Results
| Benchmark |
Scope |
Tested Samples |
Accuracy |
Evaluation Hardware |
| PubMedQA |
Clinical Trial Evidence Decisions |
100 |
49.0% |
Apple Silicon Metal GPU |
| MedQA (USMLE) |
Medical Board Diagnostic Cases |
100 |
53.0% |
Apple Silicon Metal GPU |
