Instructions to use BrinqAI/functiongemma-270m-physical-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BrinqAI/functiongemma-270m-physical-ai with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="BrinqAI/functiongemma-270m-physical-ai",
	filename="functiongemma-physical-ai-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use BrinqAI/functiongemma-270m-physical-ai with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

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 BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

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 BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Use Docker

docker model run hf.co/BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

LM Studio
Jan

vLLM

How to use BrinqAI/functiongemma-270m-physical-ai with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BrinqAI/functiongemma-270m-physical-ai"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BrinqAI/functiongemma-270m-physical-ai",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Ollama
How to use BrinqAI/functiongemma-270m-physical-ai with Ollama:
```
ollama run hf.co/BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
```

Unsloth Studio new

How to use BrinqAI/functiongemma-270m-physical-ai with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BrinqAI/functiongemma-270m-physical-ai to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for BrinqAI/functiongemma-270m-physical-ai to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for BrinqAI/functiongemma-270m-physical-ai to start chatting

Pi new

How to use BrinqAI/functiongemma-270m-physical-ai with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "BrinqAI/functiongemma-270m-physical-ai:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use BrinqAI/functiongemma-270m-physical-ai with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

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 BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use BrinqAI/functiongemma-270m-physical-ai with Docker Model Runner:
```
docker model run hf.co/BrinqAI/functiongemma-270m-physical-ai:Q4_K_M
```

Lemonade

How to use BrinqAI/functiongemma-270m-physical-ai with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull BrinqAI/functiongemma-270m-physical-ai:Q4_K_M

Run and chat with the model

lemonade run user.functiongemma-270m-physical-ai-Q4_K_M

List all available models

lemonade list

functiongemma-270m-physical-ai / token_map.json

hmahadik

v10: unified set_lights, named-args output, 6 tools

2a22670 verified 7 days ago

raw

history blame contribute delete

1.67 kB

	{
	"version": "0.5.0-draft",
	"description": "DRAFT v10 compact token map. 6 tools + <end> terminator. Single unified set_lights tool with semantic named args (Mercedes-Benz Octopus v2 convention, arXiv 2501.02342). The model is hardware-agnostic; the dispatcher maps semantic args to whatever LED hardware is detected at launch.",
	"tokens": {
	"set_lights": "<tool_0>",
	"play_buzzer": "<tool_1>",
	"set_alarm": "<tool_2>",
	"cancel_alarm": "<tool_3>",
	"get_system_status": "<tool_4>",
	"respond": "<tool_5>"
	},
	"reverse": {
	"<tool_0>": "set_lights",
	"<tool_1>": "play_buzzer",
	"<tool_2>": "set_alarm",
	"<tool_3>": "cancel_alarm",
	"<tool_4>": "get_system_status",
	"<tool_5>": "respond"
	},
	"special_tokens": ["<tool_0>", "<tool_1>", "<tool_2>", "<tool_3>", "<tool_4>", "<tool_5>", "<end>"],
	"output_format": "<tool_N>(name1=\"value1\", name2=value2, ...)<end>",
	"prompt_format": "<start_of_turn>user\\n{user_text}<end_of_turn>\\n<start_of_turn>model\\n",
	"notes": "v10 uses NAMED arguments — per Mercedes-Benz Octopus v2 (arXiv 2501.02342), the published Octopus-v2 reference that demonstrates production multi-arg tool calls with optional args absent. The model emits only the args the user implied; absent args are simply not emitted. set_lights kept to 3 optional args (color/effect/state) for robustness on the 270M — brightness/count/speed were dropped after analysis; the dispatcher uses sensible defaults (full brightness, normal speed, ~3 repetitions for blink). Backwards-compat positional parsing is retained in compact_codec.py so the parser still handles v9 GGUF output during the transition."
	}