PocketGull Circular Posology 1B (Circadian Chronopharmacology)

License: Apache 2.0 Base Model Safety by Design Evaluated with Inspect AI

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.


πŸ₯ Key Clinical Capabilities

  1. Chronotherapy Scheduling: Optimizes timing for antihypertensives (nocturnal vs morning dipping) and chemotherapeutics.
  2. Cortisol & Melatonin Rhythm Alignment: Calibrates corticosteroid timing to match endogenous hypothalamic-pituitary-adrenal axis curves.
  3. Shift Worker Adaptation: Dynamically recalibrates medication schedules for night-shift healthcare workers.
  4. ISMP Administration Time Verification: Prevents accidental dose stacking in variable shift regimens.

πŸ”¬ 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%

πŸš€ 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))

πŸ” 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

βš–οΈ 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.

πŸ“„ 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-reported
    0.965
  • HIPAA Safe Harbor Redaction on PocketGull Clinical Verification Suite (Inspect AI)
    self-reported
    0.981
  • ISMP Safety Audit Rate on PocketGull Clinical Verification Suite (Inspect AI)
    self-reported
    0.970