Instructions to use philgear/pocketgull-allometric-sfi-1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use philgear/pocketgull-allometric-sfi-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-allometric-sfi-1b") - Notebooks
- Google Colab
- Kaggle
PocketGull SFI Complex Adaptive Systems & Allometric Posology
Organization: PocketGull LLC (Oregon Registry: 258869891)
Informatics Lead: Phillip Gear (CMS NPI: 1487569752 | ORCID: 0009-0008-1372-5381)
Base Foundation Model: google/gemma-3-1b-it
Discipline: West-Brown-Enquist (WBE) Fractal Hydrodynamics, Critical Slowing Down (CSD), and Polypharmacy Hypergraphs
Open Science Provenance: Zenodo DOI 10.5281/zenodo.20647514
📌 Overview
Complex systems clinical engine developed in alignment with theoretical frameworks from the Santa Fe Institute and the ASU-SFI Center for Biosocial Complex Systems. Replaces naive linear (mg/kg) dosing with West-Brown-Enquist (WBE) M^0.75 fractal branching hydrodynamic network scaling. Detects physiological Critical Slowing Down (CSD) through lag-1 autocorrelation inflation (rho_1 -> 1) 2-48 hours before clinical collapse. Models non-linear simplicial hyperedge cascades across polypharmacy regimens and extreme environmental heat.
This LoRA adapter was fine-tuned using Direct Preference Optimization (DPO) on domain-specific clinical datasets conforming strictly to HIPAA §164.514 Safe Harbor de-identification standards.
🚀 Quickstart Inference (Transformers & PEFT)
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
base_model_id = "google/gemma-3-1b-it"
adapter_id = "pocketgull-llc/pocketgull-allometric-sfi-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 = "Patient presents with palpitations taking St. John's Wort alongside Warfarin. Evaluate CYP450 metabolism."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
with torch.no_grad():
outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.2)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
🔒 HIPAA & Regulatory Compliance
- Zero-PHI Retention: Designed for local edge computation and private Google Cloud Vertex AI deployment.
- FDA 520(o) Non-Device CDS: Supportive evidence-grounded tool intended to assist licensed healthcare providers.
📖 Citation
@software{pocketgull_clinical_2026,
author = {Gear, Phillip},
title = {Pocket-Gull: Living Medical Intelligence Engine & Open Clinical Science Suite},
publisher = {Zenodo},
version = {1.25.0},
year = {2026},
doi = {10.5281/zenodo.20647514},
url = {https://pocketgull.app}
}
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