Pomona Tomato Risk Reasoner v0.1.7 LoRA

This is Pomona's first compact tomato greenhouse risk-label reasoner. It is a LoRA adapter trained for a narrow task:

Pomona tomato greenhouse sensor JSON -> JSON list of risk labels

It is not a general chat model and it is not safe as a standalone controller. Use it with Pomona's deterministic tomato safety/rule checker.

Pomona Ecosystem

Platform and deterministic rules: okyanu/pomona
Water/irrigation specialist: Okyanus/pomona-water-irrigation-risk-reasoner-v0.1.8-lora
Actuator-gate research preview: Okyanus/pomona-actuator-command-gate-reasoner-v0.1-lora
Agronomist assistant: Okyanus/ai-pomona-agronomist-gemma4
Public greenhouse sensor dataset: Okyanus/greenhouse-sensor-data

Motivation: Small Verifiable Reasoners

This model is part of Pomona's small-model factory experiment: instead of relying on one large general-purpose LLM for every agriculture task, Pomona trains compact specialists for narrow, verifiable jobs and wraps them with deterministic safety logic.

This direction is inspired by recent small-reasoner work such as:

Pomona does not use VibeThinker code, weights, or training data. The connection is conceptual: VibeThinker-style work suggests that small models can become useful when the task is narrow, the output is verifiable, and evaluation is strict. Pomona applies that idea to agriculture by pairing a small LoRA reasoner with deterministic tomato safety rules.

In this release, the small model handles the learned risk-label classification behavior, while Pomona's rule checker enforces hard thresholds for missing data, impossible sensor values, water risk, fungal pressure, and actuator conflicts.

Base Model

Base: Qwen/Qwen2.5-0.5B-Instruct
Adapter type: PEFT LoRA
Output format: JSON list of risk labels

Allowed Labels

[
  "high_ph",
  "low_ph",
  "high_ec",
  "low_ec",
  "heat_stress",
  "cold_stress",
  "fungal_pressure",
  "nutrient_uptake_issue",
  "sensor_anomaly",
  "missing_critical_data",
  "water_level_risk",
  "actuator_conflict"
]

Recommended Use

sensor input
  -> v0.1.7 adapter predicts risk labels
  -> Pomona deterministic rule checker validates/corrects labels
  -> guarded hybrid output is used by API/dashboard

Do not use this adapter for direct pesticide dosage, autonomous fertigation changes, direct actuator control, definitive disease diagnosis, or unsafe chemical recommendations.

Example Input

{
  "system_type": "controlled_greenhouse",
  "crop": "tomato",
  "growth_stage": "flowering",
  "air_temperature_c": 24.0,
  "humidity_pct": 89.0,
  "co2_ppm": 600,
  "light_ppfd": 350,
  "ph": 6.2,
  "ec_ms_cm": 2.4,
  "water_temperature_c": 21.0,
  "substrate_temperature_c": 23.0,
  "substrate_moisture_pct": 45.0,
  "actuator_states": {
    "screen_energy_pct": 90
  },
  "symptoms": []
}

Expected guarded Pomona output:

["fungal_pressure", "actuator_conflict"]

Local Pomona Usage

Clone the platform and run the guarded route end to end — no extra dependencies, no private files required:

git clone https://github.com/Okyanus/pomona.git
cd pomona
cp .env.example .env
./scripts/up.sh

python3 examples/tomato_risk_quickstart.py

Runtime status: local Ollama inference for this adapter is wired into the platform (schema-constrained JSON decoding via format in the Ollama /api/chat request, so the model can only emit valid label lists), but it is off by default (REASONER_BACKEND=rules). Enable it with REASONER_BACKEND=ollama once pomona-tomato-risk:v0.1.7-local is built and running locally — see docs/LOCAL_MODEL_RUNTIMES.md in the platform repo. Even with it enabled, hybrid_guarded mode validates the model's output but keeps the deterministic rules as the final decision; only model_only (evaluation) mode surfaces raw model output, currently at 0.60 label F1 on the 15-case golden smoke suite — well below the rules' 1.0, which is why the guardrail stays authoritative.

Evaluation Snapshot

Best standalone adapter from the local iteration:

v0.1.7 staged risk F1: 0.924 on its original staged test
v0.1.7 golden risk F1: 0.667

Hybrid guarded evaluation with Pomona deterministic tomato rules:

golden eval:
  model-only risk F1: 0.667
  hybrid risk F1:     1.000
  corrections:        5 / 15

v0.1.7 staged test:
  model-only risk F1: 0.902
  hybrid risk F1:     1.000
  corrections:        59 / 473

The hybrid score is measured on a rule-derived eval set, so it should be interpreted as a guardrail integration check, not as an independent real-world agronomy benchmark. Future releases should add human-reviewed field cases.

Limitations

Narrow tomato greenhouse risk-label classifier only.
Not a chat model.
Threshold reasoning is imperfect without Pomona guardrails.
Does not replace agronomist review.
Does not authorize autonomous actuator or chemical actions.

Intended Role In Pomona

This adapter is one small specialist in the Pomona small-model factory:

small task model + deterministic safety rules = practical local AI component

The platform repository keeps code, schemas, docs, and rule logic. Hugging Face stores model adapter weights.

Citation / References

If you discuss the design motivation, cite the VibeThinker papers as related small-reasoner inspiration, not as the source of this model:

@article{xu2025vibethinker15b,
  title = {Tiny Model, Big Logic: Diversity-Driven Optimization Elicits Large-Model Reasoning Ability in VibeThinker-1.5B},
  author = {Xu, Sen and Zhou, Yi and Wang, Wei and Min, Jixin and Yin, Zhibin and Dai, Yingwei and Liu, Shixi and Pang, Lianyu and Chen, Yirong and Zhang, Junlin},
  journal = {arXiv preprint arXiv:2511.06221},
  year = {2025}
}

@article{xu2026vibethinker3b,
  title = {VibeThinker-3B: Exploring the Frontier of Verifiable Reasoning in Small Language Models},
  author = {Xu, Sen and Liu, Shixi and Wang, Wei and Min, Jixin and Dai, Yingwei and Yin, Zhibin and Chen, Yirong and Zhou, Xin and Zhang, Junlin},
  journal = {arXiv preprint arXiv:2606.16140},
  year = {2026}
}