Instructions to use madhuHuggingface/functiongemma-clean with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhuHuggingface/functiongemma-clean with Transformers:

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

pipe = pipeline("text-generation", model="madhuHuggingface/functiongemma-clean")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("madhuHuggingface/functiongemma-clean")
model = AutoModelForCausalLM.from_pretrained("madhuHuggingface/functiongemma-clean")
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

vLLM

How to use madhuHuggingface/functiongemma-clean with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/madhuHuggingface/functiongemma-clean

SGLang

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

Docker Model Runner
How to use madhuHuggingface/functiongemma-clean with Docker Model Runner:
```
docker model run hf.co/madhuHuggingface/functiongemma-clean
```

functiongemma-clean

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{
  "backend": "tokenizers",
  "boi_token": "<start_of_image>",
  "bos_token": "<bos>",
  "clean_up_tokenization_spaces": false,
  "eoi_token": "<end_of_image>",
  "eos_token": "<eos>",
  "image_token": "<image_soft_token>",
  "is_local": false,
  "mask_token": "<mask>",
  "model_max_length": 1000000000000000019884624838656,
  "model_specific_special_tokens": {
    "boi_token": "<start_of_image>",
    "eoi_token": "<end_of_image>",
    "image_token": "<image_soft_token>",
    "sfr_token": "<start_function_response>"
  },
  "pad_token": "<pad>",
  "padding_side": "left",
  "sfr_token": "<start_function_response>",
  "sp_model_kwargs": null,
  "spaces_between_special_tokens": false,
  "tokenizer_class": "GemmaTokenizer",
  "unk_token": "<unk>",
  "use_default_system_prompt": false
}