Instructions to use ibm-granite/granite-4.0-tiny-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-4.0-tiny-preview with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-4.0-tiny-preview")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-4.0-tiny-preview")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-tiny-preview")
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 ibm-granite/granite-4.0-tiny-preview with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-4.0-tiny-preview"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ibm-granite/granite-4.0-tiny-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-4.0-tiny-preview

SGLang

How to use ibm-granite/granite-4.0-tiny-preview 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 "ibm-granite/granite-4.0-tiny-preview" \
    --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": "ibm-granite/granite-4.0-tiny-preview",
		"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 "ibm-granite/granite-4.0-tiny-preview" \
        --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": "ibm-granite/granite-4.0-tiny-preview",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ibm-granite/granite-4.0-tiny-preview with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-4.0-tiny-preview
```

Is there a non-preview version?

#11

by ptrdvn - opened Nov 11, 2025

Discussion

ptrdvn

Nov 11, 2025

Hi IBM - first of all, thanks for the great model!
These Granite models are superb in my testing, especially Micro.
I was wondering if you had released a non-preview version of Tiny? I see you have a released a non-preview version of the hybrid version, but are you going to release a non-preview version of the dense Tiny model?

Thanks!

gabegoodhart

IBM Granite org Nov 11, 2025

Hi @ptrdvn , I'm glad you are enjoying the models so far! At the moment, we only have hybrid versions of the tiny and small model sizes and don't have immediate plans to release non-hybrid versions. We are, however, definitely aware that there are use cases where a larger non-hybrid model would be advantageous, so stay tuned!

ptrdvn

Nov 11, 2025

Great - excited for what your team produces 😎
Thanks!

ptrdvn changed discussion status to closed Nov 11, 2025

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