Okyanus/pomona-actuator-command-gate-reasoner-v0.1-lora

Pomona Actuator Command Gate Reasoner v0.1 LoRA

This PEFT LoRA adapter is a compact classifier for proposed farm commands. It maps farm context, sensor quality, risk labels, actor identity, and a proposed command to a structured gate recommendation.

Research preview only. Never connect this model directly to actuators. Independent evaluation is below Pomona's release gates. This model can miss blocked actions or human-approval requirements. Pomona's deterministic safety checker and a separately authorized human workflow must remain final authority.

farm context + sensor quality + proposed command
  -> decision + gate labels + blocked actions + safe alternatives

This is not a general chat model, agronomist replacement, PLC, safety-certified controller, or authorization system.

Pomona Ecosystem

• Platform and deterministic gate: okyanu/pomona
• Base model: Qwen/Qwen2.5-0.5B-Instruct
• Water/irrigation specialist: Okyanus/pomona-water-irrigation-risk-reasoner-v0.1.8-lora
• Tomato specialist: Okyanus/pomona-tomato-risk-reasoner-v0.1.7-lora
• Agronomist assistant: Okyanus/ai-pomona-agronomist-gemma4
• Public greenhouse sensor dataset: Okyanus/greenhouse-sensor-data

GitHub contains platform code, the deterministic safety implementation, schemas, model metadata, and evaluation scaffolding. Adapter weights belong on Hugging Face.

Task Contract

Required input groups:

• farm_context: crop, system type, and zone
• sensor: current telemetry
• sensor_quality: quality labels and missing/suspect fields
• risk_labels: upstream advisory risks
• actor: assistant, automation engine, digital twin, or human
• proposed_command: action type, target, value, unit, and description

Output schema:

{
  "decision": "blocked",
  "gate_labels": [],
  "blocked_actions": [],
  "human_approval_required": true,
  "safe_alternatives": [],
  "rationale": ""
}

Allowed decisions:

["allowed", "blocked", "needs_human_approval"]

Allowed gate labels and blocked actions are provided in labels.json.

Intended Use

Use this adapter only as an advisory classifier or explanation component before the deterministic command gate:

proposed command
  -> this research-preview adapter
  -> deterministic Pomona actuator-command gate
  -> dashboard and human approval
  -> separately authorized automation layer

Reasonable research uses include:

• structured classification of proposed climate, irrigation, fertigation, and chemical commands,
• comparing compact-model recommendations against deterministic rules,
• generating human-readable safe alternatives,
• studying failures and calibration of small specialist reasoners.

Prohibited Use

Do not use this adapter to:

• directly open vents, run pumps, change valves, heaters, screens, or fans,
• autonomously change irrigation schedules or fertigation recipes,
• prescribe or apply pesticides, fungicides, acids, bases, or nutrients,
• bypass deterministic checks or human approval,
• claim safety certification or field validation,
• make definitive disease diagnoses.

Usage

import json
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER = "Okyanus/pomona-actuator-command-gate-reasoner-v0.1-lora"

SYSTEM_PROMPT = """You are Pomona Actuator Command Gate Reasoner, an advisory classifier.
Return exactly one JSON object with these keys: decision, gate_labels,
blocked_actions, human_approval_required, safe_alternatives, rationale.
Use only the decisions, labels, and blocked actions documented in labels.json.
Direct actuator, irrigation, fertigation, chemical, and definitive diagnosis
requests are not authorized. Never execute a command and never output text
outside the JSON object."""

payload = {
    "farm_context": {
        "crop": "tomato",
        "system_type": "controlled_greenhouse",
        "zone_id": "greenhouse-a",
    },
    "sensor": {
        "air_temperature_c": 31.0,
        "humidity_pct": 68.0,
        "ph": 6.2,
        "ec_ms_cm": 2.4,
        "substrate_moisture_pct": 42.0,
    },
    "sensor_quality": {
        "data_quality_labels": [],
        "missing_fields": [],
        "suspect_fields": [],
    },
    "risk_labels": ["heat_stress"],
    "actor": "automation_engine",
    "proposed_command": {
        "action_type": "open_vent",
        "target": "roof_vent",
        "value": "100",
        "unit": "pct",
        "description": "Open the roof vent automatically.",
    },
}

tokenizer = AutoTokenizer.from_pretrained(ADAPTER)
dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    dtype=dtype,
    device_map="auto",
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {"role": "user", "content": "Gate this proposed farm command.\n" + json.dumps(payload, separators=(",", ":"), sort_keys=True)},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.no_grad():
    generated = model.generate(
        **inputs,
        max_new_tokens=300,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

text = tokenizer.decode(generated[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
result = json.loads(text[text.find("{"):text.rfind("}") + 1])
print(json.dumps(result, indent=2))

The returned object is advisory. Pass the original command through Pomona's deterministic POST /v1/actuator-command-gate/check endpoint before displaying or routing it as an operational proposal.

Independent Evaluation

The release decision uses a 126-case, nine-category clean holdout with zero exact input overlap against training.

Metric	Result	Pomona release gate
Valid JSON	1.000	1.000
Required fields	1.000	1.000
Allowed decisions	1.000	1.000
Allowed labels	1.000	1.000
Allowed blocked actions	1.000	1.000
Gate-label F1	0.813	>= 0.900
Blocked-action F1	0.833	1.000
Decision match	0.857	>= 0.950
Human-approval match	0.857	>= 0.950
Exact full-object match	0.437	informational

This adapter does not pass Pomona's standalone release gate. It is published to make the research checkpoint, limitations, and deterministic-wrapper design inspectable.

Known weaker categories on the independent holdout include chemical requests, manual-check boundaries, irrigation control, and actuator-conflict context. Detailed machine-readable metrics are in eval_summary.json.

Data

Training and evaluation data are synthetic and rule-derived from Pomona's deterministic command policy. No raw third-party dataset is included in this model repository. The independent holdout tests unseen wording and context, but it is not real-farm validation.

Version History

• v0.1: this retained research-preview checkpoint.
• v0.1.1-hardcases: rejected local regression; not published.
• v0.1.2-correction: improved label F1 but regressed decision and human-review behavior and emitted an unknown label; not published.

Newer experiment numbers do not imply better or safer models.

Limitations

• Synthetic/rule-derived training and evaluation.
• No safety certification or field validation.
• Can miss blocked actions and approval requirements.
• Can confuse observation/manual-check boundaries.
• Small language models can emit malformed or unexpected content despite the structured-output prompt.
• Correct JSON is not authorization to act.
• Deterministic validation and human review are mandatory.

Small-Reasoner Motivation

Pomona explores compact specialist adapters for narrow, inspectable tasks rather than asking one large model to perform every farm operation. This is conceptually related to VibeThinker-style small-reasoner research: constrained tasks, strict outputs, and explicit evaluation can make small models useful.

This adapter does not use VibeThinker code, weights, or datasets. The relationship is design inspiration only.