Instructions to use allenai/OLMo-2-0425-1B-RLVR1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use allenai/OLMo-2-0425-1B-RLVR1 with Transformers:

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

pipe = pipeline("text-generation", model="allenai/OLMo-2-0425-1B-RLVR1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B-RLVR1")
model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B-RLVR1", 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 allenai/OLMo-2-0425-1B-RLVR1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "allenai/OLMo-2-0425-1B-RLVR1"
# 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-2-0425-1B-RLVR1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/allenai/OLMo-2-0425-1B-RLVR1

SGLang

How to use allenai/OLMo-2-0425-1B-RLVR1 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-2-0425-1B-RLVR1" \
    --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-2-0425-1B-RLVR1",
		"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-2-0425-1B-RLVR1" \
        --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-2-0425-1B-RLVR1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use allenai/OLMo-2-0425-1B-RLVR1 with Docker Model Runner:
```
docker model run hf.co/allenai/OLMo-2-0425-1B-RLVR1
```

TheRootOf3 commited on Feb 19

Commit

e2e002f

verified ·

1 Parent(s): 12cb334

Update model card to reflect the correct chat template

Browse files

## Background
The current description of the chat template is incorrect. The model uses the same template as the chat-tuned 7B and 13B variants. The `tokenizer_config.json` specifies:
```
"bos_token": "<|endoftext|>",
"chat_template": "{{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'user' %}{{ '<|user|>\n' + message['content'] + '\n' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>\n' + message['content'] + eos_token + '\n' }}{% else %}{{ '<|assistant|>\n' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>\n' }}{% endif %}{% endfor %}",
```

Hence, the tokenizer will include the `<|endoftext|>` token at the start. This is inconsistent with the model card description, which says the opposite.

## Key Change
- Copied the chat template description from the `allenai/OLMo-2-1124-13B-Instruct` model card.

Files changed (1) hide show

README.md +2 -7

README.md CHANGED Viewed

@@ -59,18 +59,13 @@ olmo_model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B-RLVR1"
 ### Chat template
-*NOTE: This is different than previous OLMo 2 and Tülu 3 models due to a minor change in configuration. It does NOT have the bos token before the rest. Our other models have <|endoftext|> at the beginning of the chat template.*
 The chat template for our models is formatted as:
 ```
-<|user|>
-How are you doing?
-<|assistant|>
-I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
 ```
 Or with new lines expanded:
 ```
-<|user|>
 How are you doing?
 <|assistant|>
 I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>

 ### Chat template
 The chat template for our models is formatted as:
 ```
+<|endoftext|><|user|>\nHow are you doing?\n<|assistant|>\nI'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>
 ```
 Or with new lines expanded:
 ```
+<|endoftext|><|user|>
 How are you doing?
 <|assistant|>
 I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?<|endoftext|>