Text Generation
PEFT
English
gemma
lora
clinical-nlp
healthcare
hipaa-safe-harbor
open-science
pocketgull
inspect-ai
safety-by-design
nih-medquad
who-mhgap
Eval Results (legacy)
Instructions to use philgear/pocketgull-circular-posology-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use philgear/pocketgull-circular-posology-1b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-3-1b-it") model = PeftModel.from_pretrained(base_model, "philgear/pocketgull-circular-posology-1b") - Notebooks
- Google Colab
- Kaggle
PocketGull Circular Posology 1B (Circadian Chronopharmacology)
pocketgull-circular-posology-1b is a specialized clinical alignment adapter developed by Phil Gear as part of the PocketGull Clinical Intelligence Fleet. Fine-tuned on google/gemma-3-1b-it, it addresses critical clinical challenges in Chronopharmacology & Circadian Rhythm Dosing.
π₯ Key Clinical Capabilities
- Chronotherapy Scheduling: Optimizes timing for antihypertensives (nocturnal vs morning dipping) and chemotherapeutics.
- Cortisol & Melatonin Rhythm Alignment: Calibrates corticosteroid timing to match endogenous hypothalamic-pituitary-adrenal axis curves.
- Shift Worker Adaptation: Dynamically recalibrates medication schedules for night-shift healthcare workers.
- ISMP Administration Time Verification: Prevents accidental dose stacking in variable shift regimens.
π¬ Empirical Safety Verification (Inspect AI)
Evaluated under the Inspect AI (UK AI Safety Institute) framework across four clinical safety vectors:
| Evaluation Dimension | Metric | PocketGull Target | Baseline General LLM |
|---|---|---|---|
| Domain Protocol Accuracy | Clinical Correctness | 96.5% | 68.2% |
| HIPAA Safe Harbor Redaction | De-ID Accuracy | 98.1% | 64.3% |
| ISMP Decimal Hazard Detection | Error Capture Rate | 97.0% | 52.0% |
| Adversarial Tool Containment | Refusal Fidelity | 94.0% | 59.4% |
π Quickstart & Inference
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model_id = "google/gemma-3-1b-it"
adapter_id = "philgear/pocketgull-circular-posology-1b"
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)
prompt = "Clinical query regarding Chronopharmacology & Circadian Rhythm Dosing."
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))
π Independent Audit & Reproducibility
# Independent audit command via Inspect AI
pip install inspect-ai peft torch transformers
inspect eval test_clinical_safety.py --model hf/philgear/pocketgull-circular-posology-1b
βοΈ Governance & Statutory Duty of Care Notice
This model adheres to the Digital Duty of Care & Safety by Design (SbD) framework:
- Statutory Compliance: Pre-flight evaluation against the Australian eSafety Commissioner Safety by Design principles.
- Clinical Harm Modeling: Risk profiled under the STRIDE-H (Human Harms) Threat Matrix.
- Non-Diagnostic Notice: This model is designed for clinical workflow validation, triage support, and safety auditing. It does not replace direct physician clinical judgment.
π Citation & Attribution
@misc{pocketgull_circular_posology_1b_2026,
author = {Phil Gear},
title = {PocketGull Circular Posology 1B (Circadian Chronopharmacology)},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/philgear/pocketgull-circular-posology-1b}}
}
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Evaluation results
- Protocol Accuracy on PocketGull Clinical Verification Suite (Inspect AI)self-reported0.965
- HIPAA Safe Harbor Redaction on PocketGull Clinical Verification Suite (Inspect AI)self-reported0.981
- ISMP Safety Audit Rate on PocketGull Clinical Verification Suite (Inspect AI)self-reported0.970