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

hmahadik commited on 18 days ago

Commit

c3ed491

verified ·

1 Parent(s): 4e15ea2

chore: token map for v6 (11 tools, camera/vision dropped)

Browse files

Files changed (1) hide show

token_map.json +5 -11

token_map.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
-  "version": "0.1.0",
-  "description": "Compressed token map for FunctionGemma CPU inference on SL2619. Shrinks tool-call output ~4-6x vs canonical JSON to hit sub-1s voice UX on 2-core A55.",
   "tokens": {
     "turn_on_lights": "<tool_0>",
     "turn_off_lights": "<tool_1>",
@@ -12,9 +12,7 @@
     "cancel_alarm": "<tool_7>",
     "list_alarms": "<tool_8>",
     "get_system_status": "<tool_9>",
-    "capture_photo": "<tool_10>",
-    "describe_scene": "<tool_11>",
-    "respond": "<tool_12>"
   },
   "reverse": {
     "<tool_0>": "turn_on_lights",
@@ -27,9 +25,7 @@
     "<tool_7>": "cancel_alarm",
     "<tool_8>": "list_alarms",
     "<tool_9>": "get_system_status",
-    "<tool_10>": "capture_photo",
-    "<tool_11>": "describe_scene",
-    "<tool_12>": "respond",
     "<tool_none>": null
   },
   "special_tokens": [
@@ -44,11 +40,9 @@
     "<tool_8>",
     "<tool_9>",
     "<tool_10>",
-    "<tool_11>",
-    "<tool_12>",
     "<tool_none>",
     "<end>"
   ],
   "output_format": "<tool_N>(\"arg1\",\"arg2\",...)<end>",
-  "notes": "Argument order must match the canonical schema's 'required' list first, then optional params in schema declaration order. Converter normalizes canonical JSON -> positional args at training time."
 }

 {
+  "version": "0.2.0",
+  "description": "Compressed token map for FunctionGemma CPU inference on SL2619. 11 tools — camera/vision removed in v6.",
   "tokens": {
     "turn_on_lights": "<tool_0>",
     "turn_off_lights": "<tool_1>",
     "cancel_alarm": "<tool_7>",
     "list_alarms": "<tool_8>",
     "get_system_status": "<tool_9>",
+    "respond": "<tool_10>"
   },
   "reverse": {
     "<tool_0>": "turn_on_lights",
     "<tool_7>": "cancel_alarm",
     "<tool_8>": "list_alarms",
     "<tool_9>": "get_system_status",
+    "<tool_10>": "respond",
     "<tool_none>": null
   },
   "special_tokens": [
     "<tool_8>",
     "<tool_9>",
     "<tool_10>",
     "<tool_none>",
     "<end>"
   ],
   "output_format": "<tool_N>(\"arg1\",\"arg2\",...)<end>",
+  "notes": "Argument order positional per canonical schema's required-first then optional declaration order. Camera/vision tools (capture_photo, describe_scene) removed in v6 — out of scope per project decision."
 }