Instructions to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Use Docker

docker model run hf.co/Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

LM Studio
Jan

vLLM

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Ollama
How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with Ollama:
```
ollama run hf.co/Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
```
Unsloth Desktop

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with Docker Model Runner:
```
docker model run hf.co/Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16
```

Lemonade

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Run and chat with the model

lemonade run user.pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Pomona Nutrient / pH-EC Reasoner v0.1.1 GGUF

Experimental runtime artifact, published for local testing. This F16 GGUF is a conversion of the Pomona Nutrient/pH-EC v0.1.1 correction LoRA merged into Qwen/Qwen2.5-0.5B-Instruct.

The source adapter's independent 140-case semantic evaluation passed valid JSON, allowed labels/actions, nutrient label F1, blocked-action F1, and human-review match at 1.0. The conversion was evaluated with the exact training prompt on the same 140-case holdout, but is not release-ready: valid JSON and schema compliance were 1.0000, allowed-label rate was 0.8571, nutrient-label F1 was 0.6690, blocked-action F1 was 0.8571, and human-review match was 0.8571. Normal and missing-critical-data cases were the main failures. These runtime results do not represent the quality of the source PEFT adapter.

The Pomona guarded hybrid path applies deterministic rules after model generation. With that guard enabled, the same 140-case holdout reached valid JSON, allowed labels/actions, nutrient label F1, blocked-action F1, and human-review match of 1.0000 in every category. This measures the complete Pomona deployment path, not standalone GGUF reasoning.

Use only as an advisory component behind Pomona's deterministic nutrient and safety routes. Never use it to directly change fertigation, dose chemicals, control actuators, or diagnose disease.

Local Ollama name prepared: pomona-nutrient-ph-ec:v0.1.1.

Related platform: okyanu/pomona

Usage

Pull directly from Hugging Face with Ollama:

ollama pull hf.co/Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF

Verified on a clean pull: 994 MB download, ~1.1 GB RAM while loaded (100% GPU offload reported on Apple Silicon), under 2 seconds per inference once loaded. The local Ollama wrapper used for internal testing was:

FROM pomona-nutrient-ph-ec-v0.1.1-f16.gguf
PARAMETER temperature 0
PARAMETER num_predict 220

Use the exact task prompt and keep deterministic validation in front of any fertigation workflow. A prompt or runtime wrapper is not a substitute for the deterministic Pomona safety route. Model-only output is not guaranteed schema-perfect even with format: json — see the evaluation numbers above.

For the guarded path, use scripts/models/guard_nutrient_ph_ec_output.py or the Pomona model-router deterministic route. guarded_evaluation.json records the guarded result; evaluation.json records the unguarded model-only result.

Release Status

This package is structurally uploadable as an experimental conversion, but it is not an approved production or release-candidate runtime. The canonical PEFT LoRA remains the reference artifact until this conversion passes an independent full holdout.

Downloads last month: 106

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

16-bit

Model tree for Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-lora

Quantized

(2)

this model

Okyanus
/

pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF

Pomona Nutrient / pH-EC Reasoner v0.1.1 GGUF

Usage

Release Status

Model tree for Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF

Dataset used to train Okyanus/pomona-nutrient-ph-ec-reasoner-v0.1.1-GGUF