Text Generation
Transformers
ONNX
Safetensors
English
Korean
function-calling
gemma
gemma3
on-device
unity
sentis
lora
Instructions to use Sky-Kim/functiongemma-270m-finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sky-Kim/functiongemma-270m-finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Sky-Kim/functiongemma-270m-finetune")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Sky-Kim/functiongemma-270m-finetune", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Sky-Kim/functiongemma-270m-finetune with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Sky-Kim/functiongemma-270m-finetune" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sky-Kim/functiongemma-270m-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Sky-Kim/functiongemma-270m-finetune
- SGLang
How to use Sky-Kim/functiongemma-270m-finetune with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Sky-Kim/functiongemma-270m-finetune" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sky-Kim/functiongemma-270m-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Sky-Kim/functiongemma-270m-finetune" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Sky-Kim/functiongemma-270m-finetune", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Sky-Kim/functiongemma-270m-finetune with Docker Model Runner:
docker model run hf.co/Sky-Kim/functiongemma-270m-finetune
File size: 8,392 Bytes
108a536 | 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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 | """Single source of truth for the tool schema, developer prompt, and prompt
rendering โ imported by both step 1 (dataset synthesis) and step 4 (export gate).
Keeping these in one module guarantees the developer+tools prefix the model is
trained on is byte-identical to the one the export gate (and the Unity runtime)
renders. Never redefine TOOLS or DEV anywhere else.
"""
import os
from transformers import AutoTokenizer
import paths
# ============================ tool schema ============================
# Rooms apply ONLY to the light tools. TV / computer / vacuum / speaker are
# single devices and take no room. play_music has a small genre enum (default
# jazz). web_search is the general-knowledge escape hatch.
TOOLS = [
{"type": "function", "function": {
"name": "turn_on_light",
"description": "Turn on the light in a room, optionally at a brightness percent.",
"parameters": {"type": "object", "properties": {
"room": {"type": "string", "description": "Which room's light."},
"brightness": {"type": "integer", "description": "Brightness percent 0-100 (default 100)."},
}, "required": ["room"]}}},
{"type": "function", "function": {
"name": "turn_off_light",
"description": "Turn off the light in a room.",
"parameters": {"type": "object", "properties": {
"room": {"type": "string", "description": "Which room's light."},
}, "required": ["room"]}}},
{"type": "function", "function": {
"name": "set_light_color",
"description": "Change a room's light color.",
"parameters": {"type": "object", "properties": {
"room": {"type": "string", "description": "Which room's light."},
"color": {"type": "string", "description": "Color name.",
"enum": ["red", "orange", "yellow", "green", "blue",
"purple", "pink", "white", "warm", "cool"]},
}, "required": ["room", "color"]}}},
{"type": "function", "function": {
"name": "turn_on_tv", "description": "Turn on the TV.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "turn_off_tv", "description": "Turn off the TV.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "turn_on_computer", "description": "Turn on the computer.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "turn_off_computer", "description": "Turn off the computer.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "start_vacuum", "description": "Start the robot vacuum.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "play_music",
"description": "Start playing music, optionally of a genre (default jazz).",
"parameters": {"type": "object", "properties": {
"genre": {"type": "string", "description": "Music genre.",
"enum": ["rock", "jazz", "lofi"]},
}, "required": []}}},
{"type": "function", "function": {
"name": "stop_music", "description": "Stop the music.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "set_volume",
"description": "Set the speaker volume to an absolute percent.",
"parameters": {"type": "object", "properties": {
"volume": {"type": "integer", "description": "Volume percent 0-100."},
}, "required": ["volume"]}}},
{"type": "function", "function": {
"name": "get_volume", "description": "Get the current speaker volume.",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "get_weather",
"description": "Get the current weather / temperature for a city.",
"parameters": {"type": "object", "properties": {
"city": {"type": "string", "description": "City name (corrected if misspelled)."},
}, "required": ["city"]}}},
{"type": "function", "function": {
"name": "get_time",
"description": "Get the current time; no city = the device's current city.",
"parameters": {"type": "object", "properties": {
"city": {"type": "string", "description": "Optional city for world time."},
}, "required": []}}},
{"type": "function", "function": {
"name": "get_location",
"description": "Get the device's current location (city / area).",
"parameters": {"type": "object", "properties": {}, "required": []}}},
{"type": "function", "function": {
"name": "web_search",
"description": "Search the web for general knowledge / facts and answer from the results.",
"parameters": {"type": "object", "properties": {
"query": {"type": "string", "description": "The search query."},
}, "required": ["query"]}}},
]
DEV = ("You convert the user's command (English or Korean) into one or more function calls "
"with correctly filled arguments, in the order the user asked. Rooms apply only to lights. "
"If the request does not match any function, reply briefly in text instead of calling.")
# Held-out evaluation queries โ kept OUT of the training set (step 1 filters on
# this) and used by the step 4 ORT parity gate, so the gate always tests unseen
# phrasings. Keep the two in sync.
BENCH = {
"๊ฑฐ์ค ๋ถ 30ํผ์ผํธ๋ก ์ผ์ค", "turn on the living room light at 30 percent", "๋ง๋ฃจ ๋ถ ์ผ์ค",
"๊ฑฐ์ค ๋ถ ๊บผ์ค", "turn off the kitchen light", "๊ฑฐ์ค ์กฐ๋ช
ํ๋์์ผ๋ก ๋ฐ๊ฟ์ค",
"ํฐ๋น ์ผ์ค", "turn on the tv", "์ปดํจํฐ ์ผ์ค", "turn off the computer", "์ฒญ์๊ธฐ ๋๋ ค์ค",
"์์
ํ์ด์ค", "play music", "๋ฝ ์์
ํ์ด์ค", "play some jazz", "๋กํ์ด ํ์ด์ค",
"์์
๊บผ์ค", "๋ณผ๋ฅจ 30์ผ๋ก ํด์ค", "set the volume to 60", "๋ณผ๋ฅจ ๋ช์ด์ผ", "what's the volume",
"์์ธ ๋ ์จ ์ด๋?", "what's the weather in london right now?", "๋ด์์ ์ง๊ธ ๋ช ์์ผ?",
"์ง๊ธ ๋ช์์ผ?", "์ฌ๊ธฐ๊ฐ ์ด๋์ผ?", "ํ๋์ค ์๋๊ฐ ์ด๋์ผ", "what is the capital of france?",
"๋ถ์, ๊ฑฐ์ค, ์๋ฐฉ ๋ถ ์ผ์ค", "๋ง๋ฃจ์ ๋นจ๊ฐ ๋ถ ์ผ๊ณ ์ฌ์ฆ ํ์ด์ค",
}
# ============================ prompt rendering ============================
# The tokenizer + chat template come from the base model; merging LoRA leaves
# both untouched, so training and export render identically.
_tok = AutoTokenizer.from_pretrained(paths.BASE_MODEL)
_chat_template = open(os.path.join(paths.BASE_MODEL, "chat_template.jinja")).read()
def prompt_only(user):
"""The rendered prompt up to (and including) the model's generation turn."""
return _tok.apply_chat_template(
[{"role": "developer", "content": DEV}, {"role": "user", "content": user}],
tools=TOOLS, chat_template=_chat_template,
add_generation_prompt=True, tokenize=False)
def render_calls(user, calls):
"""Render one training row for a tool-calling turn.
`calls` is a list of (name, args_dict). Returns (prompt, completion) where
completion is just the assistant's function-call span.
"""
prompt = prompt_only(user)
tool_calls = [{"type": "function", "function": {"name": n, "arguments": a}} for n, a in calls]
full = _tok.apply_chat_template(
[{"role": "developer", "content": DEV}, {"role": "user", "content": user},
{"role": "assistant", "tool_calls": tool_calls}],
tools=TOOLS, chat_template=_chat_template,
add_generation_prompt=False, tokenize=False)
return prompt, full[len(prompt):]
def render_text(user, reply):
"""Render one training row for a plain-text refusal turn."""
prompt = prompt_only(user)
full = _tok.apply_chat_template(
[{"role": "developer", "content": DEV}, {"role": "user", "content": user},
{"role": "assistant", "content": reply}],
tools=TOOLS, chat_template=_chat_template,
add_generation_prompt=False, tokenize=False)
return prompt, full[len(prompt):]
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