Instructions to use allenai/Olmo-3-32B-Think with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/Olmo-3-32B-Think with Transformers:

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

pipe = pipeline("text-generation", model="allenai/Olmo-3-32B-Think")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-32B-Think")
model = AutoModelForMultimodalLM.from_pretrained("allenai/Olmo-3-32B-Think")
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 allenai/Olmo-3-32B-Think with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/Olmo-3-32B-Think"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "allenai/Olmo-3-32B-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/Olmo-3-32B-Think

SGLang

How to use allenai/Olmo-3-32B-Think 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 "allenai/Olmo-3-32B-Think" \
    --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": "allenai/Olmo-3-32B-Think",
		"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 "allenai/Olmo-3-32B-Think" \
        --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": "allenai/Olmo-3-32B-Think",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/Olmo-3-32B-Think with Docker Model Runner:
```
docker model run hf.co/allenai/Olmo-3-32B-Think
```

owao commited on Nov 21, 2025

Commit

121468c

verified ·

1 Parent(s): fa4d9b4

chat template: strip out `assistant\n`?

Browse files

Hey guys, I don't know if what I did is right, but I needed to get rid of the `assistant\n` prefix for each responses.
Despite being a minimal change, just adding `.lstrip("assistant\n")`, I have no idea if this is a good solution. Adapting those jinja templates drive me mad every single time, so the less I deal with them the better I feel lol.

In all case, thanks for offering the fruit of your work to the community! The fully open model gap has finally been filled 🙏 And I'm thrilled to try it out now :)

Files changed (1) hide show

chat_template.jinja +1 -1

chat_template.jinja CHANGED Viewed

@@ -13,4 +13,4 @@ You are a helpful AI assistant.<|im_end|>
 ' }}{% else %}{{ eos_token }}{% endif %}{% elif message['role'] == 'environment' %}{{ '<|im_start|>environment
 ' + message['content'] + '<|im_end|>
 ' }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|im_start|>assistant
-<think>' }}{% endif %}{% endfor %}

 ' }}{% else %}{{ eos_token }}{% endif %}{% elif message['role'] == 'environment' %}{{ '<|im_start|>environment
 ' + message['content'] + '<|im_end|>
 ' }}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|im_start|>assistant
+<think>' }.lstrip("assistant\n")}{% endif %}{% endfor %}