Text Generation
ONNX
GGUF
English
function-calling
edge
on-device
physical-ai
iot
octopus-v2
synaptics-sl2619
gemma3
conversational
Instructions to use BrinqAI/functiongemma-270m-physical-ai with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
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
File size: 1,673 Bytes
78658b9 2a22670 78658b9 2a22670 78658b9 2a22670 78658b9 2a22670 eef4acc 2a22670 78658b9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {
"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."
}
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