Instructions to use devlucascfarias/ordo-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use devlucascfarias/ordo-3b with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-3B-Instruct")
model = PeftModel.from_pretrained(base_model, "devlucascfarias/ordo-3b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use devlucascfarias/ordo-3b 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 devlucascfarias/ordo-3b
# Run inference directly in the terminal:
llama cli -hf devlucascfarias/ordo-3b

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf devlucascfarias/ordo-3b
# Run inference directly in the terminal:
llama cli -hf devlucascfarias/ordo-3b

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf devlucascfarias/ordo-3b
# Run inference directly in the terminal:
./build/bin/llama cli -hf devlucascfarias/ordo-3b

Use Docker

docker model run hf.co/devlucascfarias/ordo-3b

Llama new
LM Studio
Jan

vLLM

How to use devlucascfarias/ordo-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "devlucascfarias/ordo-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "devlucascfarias/ordo-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/devlucascfarias/ordo-3b

Ollama
How to use devlucascfarias/ordo-3b with Ollama:
```
ollama run hf.co/devlucascfarias/ordo-3b
```
Unsloth Desktop

How to use devlucascfarias/ordo-3b with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devlucascfarias/ordo-3b

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": "devlucascfarias/ordo-3b"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use devlucascfarias/ordo-3b with Docker Model Runner:
```
docker model run hf.co/devlucascfarias/ordo-3b
```

Lemonade

How to use devlucascfarias/ordo-3b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull devlucascfarias/ordo-3b

Run and chat with the model

lemonade run user.ordo-3b-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use devlucascfarias/ordo-3b with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devlucascfarias/ordo-3b

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 devlucascfarias/ordo-3b

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use devlucascfarias/ordo-3b with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf devlucascfarias/ordo-3b

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 "devlucascfarias/ordo-3b" \
  --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"

ordo-3b / Modelfile

devlucascfarias

feat: upload Ordo-3B Model Card, LoRA adapter weights, configs, and benchmarks

3427563 verified 4 days ago

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1.06 kB

# Ollama Modelfile for Ordo 3B ("Ordo et connexio idearum" — Spinoza, Ethics II P7)
# Base: Qwen2.5-Coder-3B-Instruct + Ordo 3B v2.0 Trained LoRA Adapter (Merged)
# Run from repository root:
#   ollama create ordo:3b -f Modelfile

FROM ./ordo-3b.gguf

PARAMETER temperature 0.0
PARAMETER top_p 0.9
PARAMETER num_ctx 16384
PARAMETER num_predict 512
PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"

SYSTEM """You are Ordo 3B, a surgical Neural Context Garbage Collector for coding agents. Your sole role is to compress noisy terminal outputs, source files, and agent trajectories into lossless causal YAML based on the agent's <intent>.
RULES:
1. Output ONLY valid YAML matching the schema for <gc_mode>. Never include markdown fences, greetings, or explanations.
2. Never invent solutions or guess fixes. Report only factual traces present in <raw_input>.
3. Preserve 100% of exact file paths, line numbers, symbol names, and error messages tied to <intent>.
4. If status is PASS and no errors exist, omit causal_trace and exact_snippets completely."""

	# Ollama Modelfile for Ordo 3B ("Ordo et connexio idearum" — Spinoza, Ethics II P7)
	# Base: Qwen2.5-Coder-3B-Instruct + Ordo 3B v2.0 Trained LoRA Adapter (Merged)
	# Run from repository root:
	# ollama create ordo:3b -f Modelfile

	FROM ./ordo-3b.gguf

	PARAMETER temperature 0.0
	PARAMETER top_p 0.9
	PARAMETER num_ctx 16384
	PARAMETER num_predict 512
	PARAMETER stop "<\|im_start\|>"
	PARAMETER stop "<\|im_end\|>"

	SYSTEM """You are Ordo 3B, a surgical Neural Context Garbage Collector for coding agents. Your sole role is to compress noisy terminal outputs, source files, and agent trajectories into lossless causal YAML based on the agent's <intent>.
	RULES:
	1. Output ONLY valid YAML matching the schema for <gc_mode>. Never include markdown fences, greetings, or explanations.
	2. Never invent solutions or guess fixes. Report only factual traces present in <raw_input>.
	3. Preserve 100% of exact file paths, line numbers, symbol names, and error messages tied to <intent>.
	4. If status is PASS and no errors exist, omit causal_trace and exact_snippets completely."""