Instructions to use ibm-granite/granite-4.2-30b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-4.2-30b with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-4.2-30b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-4.2-30b")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.2-30b", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-4.2-30b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-4.2-30b"
# 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-4.2-30b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-4.2-30b

SGLang

How to use ibm-granite/granite-4.2-30b 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-4.2-30b" \
    --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-4.2-30b",
		"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-4.2-30b" \
        --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-4.2-30b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ibm-granite/granite-4.2-30b with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-4.2-30b
```

yousafshah commited on 13 days ago

Commit

c26d718

verified ·

1 Parent(s): cef5eb8

granite-4.2

Browse files

Files changed (4) hide show

config.json +3 -3
generation_config.json +2 -2
granite_thinking_parser.py +24 -0
model.sig +1 -1

config.json CHANGED Viewed

@@ -5,7 +5,7 @@
   "attention_bias": false,
   "attention_dropout": 0.0,
   "attention_multiplier": 0.0078125,
-  "bos_token_id": 100257,
   "dtype": "bfloat16",
   "embedding_multiplier": 1.0,
   "eos_token_id": 100257,
@@ -20,7 +20,7 @@
   "num_attention_heads": 32,
   "num_hidden_layers": 64,
   "num_key_value_heads": 8,
-  "pad_token_id": 100256,
   "residual_multiplier": 1.0,
   "rms_norm_eps": 1e-05,
   "rope_parameters": {
@@ -34,4 +34,4 @@
   "use_cache": true,
   "vocab_size": 100352,
   "torch_dtype": "bfloat16"
-}

   "attention_bias": false,
   "attention_dropout": 0.0,
   "attention_multiplier": 0.0078125,
+  "bos_token_id": 100283,
   "dtype": "bfloat16",
   "embedding_multiplier": 1.0,
   "eos_token_id": 100257,
   "num_attention_heads": 32,
   "num_hidden_layers": 64,
   "num_key_value_heads": 8,
+  "pad_token_id": 100257,
   "residual_multiplier": 1.0,
   "rms_norm_eps": 1e-05,
   "rope_parameters": {
   "use_cache": true,
   "vocab_size": 100352,
   "torch_dtype": "bfloat16"
+}

generation_config.json CHANGED Viewed

@@ -1,9 +1,9 @@
 {
   "_from_model_config": true,
   "do_sample": true,
-  "bos_token_id": 100257,
   "eos_token_id": 100257,
-  "pad_token_id": 100256,
   "temperature": 1.0,
   "top_p": 0.95,
   "transformers_version": "4.57.1"

 {
   "_from_model_config": true,
   "do_sample": true,
+  "bos_token_id": 100283,
   "eos_token_id": 100257,
+  "pad_token_id": 100257,
   "temperature": 1.0,
   "top_p": 0.95,
   "transformers_version": "4.57.1"

granite_thinking_parser.py ADDED Viewed

	@@ -0,0 +1,24 @@

+from vllm.reasoning.abs_reasoning_parsers import ReasoningParserManager
+from vllm.reasoning.deepseek_r1_reasoning_parser import DeepSeekR1ReasoningParser
+@ReasoningParserManager.register_module("granite_thinking_parser")
+class GraniteThinkingReasoningParser(DeepSeekR1ReasoningParser):
+    def extract_reasoning(self, model_output, request):
+        reasoning_content, final_content = super().extract_reasoning(
+            model_output, request
+        )
+        if final_content is not None:
+            final_content = final_content.lstrip("\n")
+        if (
+            hasattr(request, "chat_template_kwargs")
+            and request.chat_template_kwargs
+            and request.chat_template_kwargs.get("enable_thinking") is False
+            and final_content is None
+        ):
+            reasoning_content, final_content = None, reasoning_content
+        return reasoning_content, final_content

model.sig CHANGED Viewed

@@ -1 +1 @@

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