Instructions to use simonycl/OLMoE-1B-7B-0125 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use simonycl/OLMoE-1B-7B-0125 with Transformers:

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

pipe = pipeline("text-generation", model="simonycl/OLMoE-1B-7B-0125")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("simonycl/OLMoE-1B-7B-0125")
model = AutoModelForCausalLM.from_pretrained("simonycl/OLMoE-1B-7B-0125", 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 simonycl/OLMoE-1B-7B-0125 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "simonycl/OLMoE-1B-7B-0125"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "simonycl/OLMoE-1B-7B-0125",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/simonycl/OLMoE-1B-7B-0125

SGLang

How to use simonycl/OLMoE-1B-7B-0125 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 "simonycl/OLMoE-1B-7B-0125" \
    --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": "simonycl/OLMoE-1B-7B-0125",
		"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 "simonycl/OLMoE-1B-7B-0125" \
        --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": "simonycl/OLMoE-1B-7B-0125",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use simonycl/OLMoE-1B-7B-0125 with Docker Model Runner:
```
docker model run hf.co/simonycl/OLMoE-1B-7B-0125
```

simonycl commited on Oct 8, 2025

Commit

87b7181

verified ·

1 Parent(s): 3b7cfd8

Upload tokenizer

Browse files

Files changed (1) hide show

chat_template.jinja +4 -8

chat_template.jinja CHANGED Viewed

@@ -1,9 +1,5 @@
-{{ bos_token }}<|system|>
-You are a helpful assistant.
-{% for message in messages %}{% if message['role'] == 'user' %}<|user|>
 Question: {{ message['content'] }}
-Please reason step by step, and put your final answer within \boxed{}.
-{% elif message['role'] == 'assistant' %}<|assistant|>
-{{ message['content'] }}{{ eos_token }}
-{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>
-{% endif %}

+{% for message in messages %}{% if message['role'] == 'user' %}A conversation between User and Assistant. The user asks a question, and the Assistant solves it. The assistant first thinks about the reasoning process in the mind and then provides the user with the answer. User: You must put your answer inside {% raw %}\boxed{{}}{% endraw %} and your final answer will be extracted automatically by the {% raw %}\boxed{{}}{% endraw %} tag.
 Question: {{ message['content'] }}
+Assistant:{% elif message['role'] == 'assistant' %}{{ message['content'] }}
+{% endif %}{% endfor %}