Instructions to use turnercore/functiongemma-270m-automaticity-v9-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use turnercore/functiongemma-270m-automaticity-v9-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/functiongemma-270m-it")
model = PeftModel.from_pretrained(base_model, "turnercore/functiongemma-270m-automaticity-v9-lora")

Transformers

How to use turnercore/functiongemma-270m-automaticity-v9-lora with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="turnercore/functiongemma-270m-automaticity-v9-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("turnercore/functiongemma-270m-automaticity-v9-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use turnercore/functiongemma-270m-automaticity-v9-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "turnercore/functiongemma-270m-automaticity-v9-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turnercore/functiongemma-270m-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/turnercore/functiongemma-270m-automaticity-v9-lora

SGLang

How to use turnercore/functiongemma-270m-automaticity-v9-lora 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 "turnercore/functiongemma-270m-automaticity-v9-lora" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turnercore/functiongemma-270m-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "turnercore/functiongemma-270m-automaticity-v9-lora" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "turnercore/functiongemma-270m-automaticity-v9-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use turnercore/functiongemma-270m-automaticity-v9-lora 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 turnercore/functiongemma-270m-automaticity-v9-lora 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 turnercore/functiongemma-270m-automaticity-v9-lora to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for turnercore/functiongemma-270m-automaticity-v9-lora to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="turnercore/functiongemma-270m-automaticity-v9-lora",
    max_seq_length=2048,
)

Docker Model Runner
How to use turnercore/functiongemma-270m-automaticity-v9-lora with Docker Model Runner:
```
docker model run hf.co/turnercore/functiongemma-270m-automaticity-v9-lora
```

functiongemma-270m-automaticity-v9-lora / README.md

turnercore

Publish FunctionGemma Automaticity V9 response-only LoRA

d87ad0e verified 16 days ago

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	---
	license: gemma
	base_model: google/functiongemma-270m-it
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- function-calling
	- tool-use
	- automaticity
	- automaticity-v9
	- lora
	- sft
	- transformers
	- trl
	- unsloth
	---

	# FunctionGemma 270M + Automaticity V9 LoRA

	Rank-16 response-only LoRA trained for one epoch on the private Automaticity V9
	friendly direct-tool corpus. This is the retained validation candidate, not a
	production-promoted router.

	The model routes one current thought to at most one available tool, or emits the
	native FunctionGemma no-tool response. Training used native FunctionGemma tool
	declarations and call tokens, a 768-token rendered-row budget, and loss only on
	the model turn.

	## Training

	- Base: `google/functiongemma-270m-it`
	- Base/tokenizer revision: `39eccb091651513a5dfb56892d3714c1b5b8276c`
	- Rows: 4,900
	- Context: 768 tokens; no truncation; 100% training-gold retention
	- LoRA: rank 16, alpha 16
	- Epochs: 1
	- Learning rate: 2e-4
	- Effective batch: 16 (4 x 4 gradient accumulation)
	- Seed: 3407
	- Loss: native assistant response only
	- Adapter SHA-256: `8e68ed2224f4d95d16a632dea2ceff35682f66c47fbbae128c14d56779155462`

	## Frozen validation result

	The evaluation used 1,050 private validation rows with normal five-tool retrieval,
	no gold injection, 100% action-gold retrieval recall, and no decoding constraint.

	\| Metric \| Result \|
	\| --- \| ---: \|
	\| End-to-end exact \| 89.71% \|
	\| Routing \| 94.95% \|
	\| Action exact \| 65.38% \|
	\| No-tool precision \| 99.86% \|
	\| No-tool recall \| 100% \|
	\| Listed-tool rate \| 99.14% \|
	\| Valid-call rate \| 99.90% \|
	\| Latency average \| 1.630 s \|
	\| Latency p50 \| 0.527 s \|
	\| Latency p95 \| 6.883 s \|

	## Limitations

	This adapter is not yet promoted for autonomous execution. Nine validation action
	rows produced plausible but unlisted aliases, one output was invalid, and action
	exact accuracy remains 65.38%. Use strict listed-name/schema validation or
	constrained decoding and reject invalid calls at runtime. Constraints cannot fix
	semantically wrong listed tools or schema-valid wrong arguments.

	The private dataset and row-level evaluation repository is
	`turnercore/automaticity-v9`.