Instructions to use LilaRest/gemma-4-31B-it-NVFP4-turbo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LilaRest/gemma-4-31B-it-NVFP4-turbo with Transformers:

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

pipe = pipeline("text-generation", model="LilaRest/gemma-4-31B-it-NVFP4-turbo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("LilaRest/gemma-4-31B-it-NVFP4-turbo")
model = AutoModelForCausalLM.from_pretrained("LilaRest/gemma-4-31B-it-NVFP4-turbo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use LilaRest/gemma-4-31B-it-NVFP4-turbo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LilaRest/gemma-4-31B-it-NVFP4-turbo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LilaRest/gemma-4-31B-it-NVFP4-turbo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LilaRest/gemma-4-31B-it-NVFP4-turbo

SGLang

How to use LilaRest/gemma-4-31B-it-NVFP4-turbo 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 "LilaRest/gemma-4-31B-it-NVFP4-turbo" \
    --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": "LilaRest/gemma-4-31B-it-NVFP4-turbo",
		"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 "LilaRest/gemma-4-31B-it-NVFP4-turbo" \
        --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": "LilaRest/gemma-4-31B-it-NVFP4-turbo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LilaRest/gemma-4-31B-it-NVFP4-turbo with Docker Model Runner:
```
docker model run hf.co/LilaRest/gemma-4-31B-it-NVFP4-turbo
```

Mlx Native multimodal and text only variants please

by Narutoouz - opened Apr 12

Discussion

Narutoouz

Apr 12

Thankyou for doing this variant. Can you support it for Apple silicon.

Narutoouz changed discussion title from Mlx Native multimodal and text only variant please to Mlx Native multimodal and text only variants please Apr 12

LilaRest

Owner Apr 15

Hey @Narutoouz ! Thanks, I've no plan to make an MLX variant right now, but there are many available already.

LilaRest changed discussion status to closed Apr 15

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