Instructions to use litert-community/FunctionGemma_270M_Mobile_Actions with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use litert-community/FunctionGemma_270M_Mobile_Actions with Transformers:

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

pipe = pipeline("text-generation", model="litert-community/FunctionGemma_270M_Mobile_Actions", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("litert-community/FunctionGemma_270M_Mobile_Actions")
model = AutoModelForCausalLM.from_pretrained("litert-community/FunctionGemma_270M_Mobile_Actions", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use litert-community/FunctionGemma_270M_Mobile_Actions with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "litert-community/FunctionGemma_270M_Mobile_Actions"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "litert-community/FunctionGemma_270M_Mobile_Actions",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/litert-community/FunctionGemma_270M_Mobile_Actions

SGLang

How to use litert-community/FunctionGemma_270M_Mobile_Actions 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 "litert-community/FunctionGemma_270M_Mobile_Actions" \
    --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": "litert-community/FunctionGemma_270M_Mobile_Actions",
		"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 "litert-community/FunctionGemma_270M_Mobile_Actions" \
        --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": "litert-community/FunctionGemma_270M_Mobile_Actions",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use litert-community/FunctionGemma_270M_Mobile_Actions with Docker Model Runner:
```
docker model run hf.co/litert-community/FunctionGemma_270M_Mobile_Actions
```

jprtr commited on Dec 25, 2025

Commit

e2226c1

verified ·

1 Parent(s): 3da8a63

Update README.md

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@@ -58,10 +58,10 @@ The model is optimized to call these mobile action functions:
 Fine-tuned using Hugging Face [TRL (Transformer Reinforcement Learning)](https://huggingface.co/docs/trl) with the `SFTTrainer`.
 **Training Configuration**:
-- **Epochs**: 2
-- **Batch size**: 4 per device
-- **Gradient accumulation steps**: 8
-- **Learning rate**: 1e-5
 - **Scheduler**: Cosine
 - **Max sequence length**: 997 tokens (based on longest example: 897 tokens)
 - **Optimizer**: AdamW (fused)

 Fine-tuned using Hugging Face [TRL (Transformer Reinforcement Learning)](https://huggingface.co/docs/trl) with the `SFTTrainer`.
 **Training Configuration**:
+- **Epochs**: 4
+- **Batch size**: 8 per device
+- **Gradient accumulation steps**: 4
+- **Learning rate**: 5e-5
 - **Scheduler**: Cosine
 - **Max sequence length**: 997 tokens (based on longest example: 897 tokens)
 - **Optimizer**: AdamW (fused)