OLMo 2 1B RLVR 1 April 2025 is post-trained variant of the allenai/OLMo-2-0425-1B-DPO model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset, further DPO training on this dataset, and final RLVR training on this dataset.
Tülu 3 is designed for state-of-the-art performance on a diversity of tasks in addition to chat, such as MATH, GSM8K, and IFEval.
Check out the OLMo 2 paper or Tülu 3 paper for more details!
OLMo is a series of Open Language Models designed to enable the science of language models.
These models are trained on the Dolma dataset. We are releasing all code, checkpoints, logs, and associated training details.
Model description
Model type: A model trained on a mix of publicly available, synthetic and human-created datasets.
OLMo 2 1B is supported in transformers v4.48 or higher:
pip install transformers>=4.48
If using vLLM, you will need to install from the main branch until v0.7.4 is released. Please
Using the model
Loading with HuggingFace
To load the model with HuggingFace, use the following snippet:
from transformers import AutoModelForCausalLM
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|>
It is embedded within the tokenizer as well, for tokenizer.apply_chat_template.
Intermediate Checkpoints
To facilitate research on RL finetuning, we have released our intermediate checkpoints during the model's RLVR training.
The model weights are saved every 20 training steps, and can be accessible in the revisions of the HuggingFace repository.
For example, you can load with:
The OLMo-2 models have limited safety training, but are not deployed automatically with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Performance
Model
Average
AlpacaEval 2 LC
BBH
DROP
GSM8K
IFEval
MATH
MMLU
Safety
PopQA
TruthQA
OLMo 1B 0724
24.4
2.4
29.9
27.9
10.8
25.3
2.2
36.6
52.0
12.1
44.3
SmolLM2 1.7B
34.2
5.8
39.8
30.9
45.3
51.6
20.3
34.3
52.4
16.4
45.3
Gemma 3 1B
38.3
20.4
39.4
25.1
35.0
60.6
40.3
38.9
70.2
9.6
43.8
Llama 3.1 1B
39.3
10.1
40.2
32.2
45.4
54.0
21.6
46.7
87.2
13.8
41.5
Qwen 2.5 1.5B
41.7
7.4
45.8
13.4
66.2
44.2
40.6
59.7
77.6
15.5
46.5
---
OLMo 2 1B SFT
36.9
2.4
32.8
33.8
52.1
50.5
13.2
36.4
93.2
12.7
42.1
OLMo 2 1B DPO
40.6
9.5
33.0
34.5
59.0
67.1
14.1
39.9
89.9
12.3
46.4
OLMo 2 1B
42.7
9.1
35.0
34.6
68.3
70.1
20.7
40.0
87.6
12.9
48.7
License and use
OLMo 2 is licensed under the Apache 2.0 license.
OLMo 2 is intended for research and educational use.
For more information, please see our Responsible Use Guidelines.
Citation
bibtex
1@article{olmo20242olmo2furious,
2 title={2 OLMo 2 Furious},
3 author={Team OLMo and Pete Walsh and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Shane Arora and Akshita Bhagia and Yuling Gu and Shengyi Huang and Matt Jordan and Nathan Lambert and Dustin Schwenk and Oyvind Tafjord and Taira Anderson and David Atkinson and Faeze Brahman and Christopher Clark and Pradeep Dasigi and Nouha Dziri and Michal Guerquin and Hamish Ivison and Pang Wei Koh and Jiacheng Liu and Saumya Malik and William Merrill and Lester James V. Miranda and Jacob Morrison and Tyler Murray and Crystal Nam and Valentina Pyatkin and Aman Rangapur and Michael Schmitz and Sam Skjonsberg and David Wadden and Christopher Wilhelm and Michael Wilson and Luke Zettlemoyer and Ali Farhadi and Noah A. Smith and Hannaneh Hajishirzi},
4 year={2024},
5 eprint={2501.00656},
6 archivePrefix={arXiv},
7 primaryClass={cs.CL},
8 url={https://arxiv.org/abs/2501.00656},
9}
