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

Libraries

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

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

pipe = pipeline("text-generation", model="ibm-granite/granite-4.0-h-tiny")
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-h-tiny")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.0-h-tiny")
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-h-tiny 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-h-tiny"
# 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-h-tiny",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

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

granite-4.0-h-tiny / config.json

ranarag

model upload

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	{
	"architectures": [
	"GraniteMoeHybridForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attention_multiplier": 0.0078125,
	"bos_token_id": 100257,
	"embedding_multiplier": 12,
	"eos_token_id": 100257,
	"hidden_act": "silu",
	"hidden_size": 1536,
	"initializer_range": 0.1,
	"intermediate_size": 512,
	"layer_types": [
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	"mamba",
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	"mamba",
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	],
	"logits_scaling": 6,
	"mamba_chunk_size": 256,
	"mamba_conv_bias": true,
	"mamba_d_conv": 4,
	"mamba_d_head": 64,
	"mamba_d_state": 128,
	"mamba_expand": 2,
	"mamba_n_groups": 1,
	"mamba_n_heads": 48,
	"mamba_proj_bias": false,
	"max_position_embeddings": 131072,
	"model_type": "granitemoehybrid",
	"normalization_function": "rmsnorm",
	"num_attention_heads": 12,
	"num_experts_per_tok": 6,
	"num_hidden_layers": 40,
	"num_key_value_heads": 4,
	"num_local_experts": 64,
	"output_router_logits": false,
	"pad_token_id": 100256,
	"position_embedding_type": "nope",
	"residual_multiplier": 0.22,
	"rms_norm_eps": 1e-05,
	"rope_scaling": null,
	"rope_theta": 10000,
	"router_aux_loss_coef": 0.0,
	"shared_intermediate_size": 1024,
	"tie_word_embeddings": true,
	"torch_dtype": "bfloat16",
	"transformers_version": "4.56.0",
	"use_cache": true,
	"vocab_size": 100352
	}