Instructions to use ibm-granite/granite-3.1-2b-instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ibm-granite/granite-3.1-2b-instruct with Transformers:

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

pipe = pipeline("text-generation", model="ibm-granite/granite-3.1-2b-instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-3.1-2b-instruct")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-3.1-2b-instruct", 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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ibm-granite/granite-3.1-2b-instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ibm-granite/granite-3.1-2b-instruct"
# 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-3.1-2b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ibm-granite/granite-3.1-2b-instruct

SGLang

How to use ibm-granite/granite-3.1-2b-instruct 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-3.1-2b-instruct" \
    --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-3.1-2b-instruct",
		"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-3.1-2b-instruct" \
        --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-3.1-2b-instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ibm-granite/granite-3.1-2b-instruct with Docker Model Runner:
```
docker model run hf.co/ibm-granite/granite-3.1-2b-instruct
```

Exception: data did not match any variant of untagged enum ModelWrapper at line 244953 column 3

by eknath2k - opened Mar 12, 2025

Discussion

eknath2k

Mar 12, 2025

granite model: granite-3.1-8b-instruct
tokenizer version: tokenizers 0.19.1 py311h6640629_0 conda-forge
Error traceback:
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 268, in
main()
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 243, in main
init_llm(p.configfile)
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 193, in init_llm
setup(config)
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 97, in setup
tokenizer = AutoTokenizer.from_pretrained(model_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 897, in from_pretrained
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2271, in from_pretrained
return cls._from_pretrained(
^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2505, in _from_pretrained
tokenizer = cls(*init_inputs, init_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/models/gpt2/tokenization_gpt2_fast.py", line 99, in init
super().init(
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_fast.py", line 115, in init**
fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Exception: data did not match any variant of untagged enum ModelWrapper at line 244953 column 3
(askpenv) [eknath@cccxl008 /dccstor/askpower/eknath/askpbot2]$python runAnyllm.py -c ./newconfig.yaml
Initializing LLM .. from config file: ./newconfig.yaml...Traceback (most recent call last):
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 269, in
main()
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 244, in main
init_llm(p.configfile)
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 193, in init_llm
setup(config)
File "/dccstor/askpower/eknath/askpbot2/runAnyllm.py", line 97, in setup
tokenizer = AutoTokenizer.from_pretrained(model_path)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/models/auto/tokenization_auto.py", line 897, in from_pretrained
return tokenizer_class.from_pretrained(pretrained_model_name_or_path, *inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2271, in from_pretrained
return cls._from_pretrained(
^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_base.py", line 2505, in _from_pretrained
tokenizer = cls(*init_inputs, init_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/models/gpt2/tokenization_gpt2_fast.py", line 99, in init
super().init(
File "/dccstor/askpower/conda_askp/envs/askpenv/lib/python3.11/site-packages/transformers/tokenization_utils_fast.py", line 115, in init**
fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
Exception: data did not match any variant of untagged enum ModelWrapper at line 244953 column 3

Any help would be appreciated

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