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Okyanus
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Model Details
Model Provider
Okyanus
Model Tree
google/gemma-4-E2B-it
Input Modalities
Output Modalities
Supported Functionality
The advisor is designed for:
The advisor is not designed for:
Pomona deployments should treat model output as advisory evidence only. Any operational action should pass through policy, rule, authorization, and audit layers outside the model.
Required integration pattern:
The model should use language such as "consider", "review", "inspect", or "escalate" instead of issuing direct equipment commands.
python
from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model_name = "google/gemma-4-E2B-it"adapter_name = "Okyanus/ai-pomona-agronomist-gemma4" tokenizer = AutoTokenizer.from_pretrained(adapter_name)base_model = AutoModelForCausalLM.from_pretrained( base_model_name, device_map="auto", torch_dtype=torch.float16,)model = PeftModel.from_pretrained(base_model, adapter_name)model.eval() system_prompt = """You are Pomona Agronomist Advisor.The model advises; Pomona policies authorize.Do not directly control actuators. Do not provide pesticide dosage.""" user_prompt = """Crop: tomatoSystem: greenhouseSensors: temperature=31.5 C, relative_humidity=45%, co2=380 ppm, substrate_ec=3.2 mS/cm, ph=6.8, ppfd=210 umol/m2/sQuestion: Why is plant stress elevated, and what should the operator review?""" prompt = ( f"<start_of_turn>user\n{system_prompt}\n\n{user_prompt}<end_of_turn>\n" f"<start_of_turn>model\n") inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=350, do_sample=False) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))See examples/ and prompts/ for more complete prompt templates and sample sensor payloads.
For production integrations, prefer structured advisory responses:
summary: concise crop-state interpretation.likely_factors: environmental or nutrient drivers to investigate.operator_checks: manual inspections or data-quality checks.advisory_options: non-binding options for review.policy_handoff: what Pomona policy services should validate before action.safety_notes: any prohibited, uncertain, or escalation-worthy items.This repository documents an adapter-style advisor model trained for greenhouse and hydroponic agronomy language tasks. It should be evaluated in the target crop, climate, hardware, and policy environment before operational use.
Example domains include:
CHANGELOG.md: documentation and release notes.examples/: prompt and JSON examples for safe advisory use.prompts/advisor_system_prompt.md: recommended system prompt.metadata/pomona-model.yaml: model-card metadata for Pomona integration.evals/eval_report.md: qualitative evaluation report and safety checks.This model is part of the Pomona project.
Okyanus/greenhouse-sensor-dataRelated repositories:
Okyanus/ai-pomona-agronomist-gemma4Okyanus/greenhouse-sensor-dataai-pomona/pomona-greenhouse-demov0.1.0: initial releasev0.1.1: model card and examplesv0.2.0: better training datav0.3.0: tomato greenhouse evaluationv0.4.0: safety-tuned versionfrom transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModelimport torch base_model_name = "google/gemma-4-E2B-it"adapter_name = "Okyanus/ai-pomona-agronomist-gemma4" tokenizer = AutoTokenizer.from_pretrained(adapter_name)base_model = AutoModelForCausalLM.from_pretrained( base_model_name, device_map="auto", torch_dtype=torch.float16,)model = PeftModel.from_pretrained(base_model, adapter_name)model.eval() system_prompt = """You are Pomona Agronomist Advisor.The model advises; Pomona policies authorize.Do not directly control actuators. Do not provide pesticide dosage.""" user_prompt = """Crop: tomatoSystem: greenhouseSensors: temperature=31.5 C, relative_humidity=45%, co2=380 ppm, substrate_ec=3.2 mS/cm, ph=6.8, ppfd=210 umol/m2/sQuestion: Why is plant stress elevated, and what should the operator review?""" prompt = ( f"<start_of_turn>user\n{system_prompt}\n\n{user_prompt}<end_of_turn>\n" f"<start_of_turn>model\n") inputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=350, do_sample=False) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))