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allenai/
OLMo-2-0325-32B-DPO

Name: OLMo-2-0325-32B-DPO
Rating: 2.5 (304 reviews)

allenai/olmo-2-0325-32b-preference-mix

2501.00656

2411.15124

allenai/OLMo-2-0325-32B-SFT

finetune

apache-2.0

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OLMo Logo

OLMo 2 32B DPO March 2025 is post-trained variant of the OLMo-2 32B March 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO 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.
Language(s) (NLP): Primarily English
License: Apache 2.0
Finetuned from model: allenai/OLMo-2-0325-32B-SFT

Model Sources

Project Page: https://allenai.org/olmo
Repositories:
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo-core
- Evaluation code: https://github.com/allenai/olmes
- Further fine-tuning code: https://github.com/allenai/open-instruct
Paper: https://arxiv.org/abs/2501.00656
Demo: https://playground.allenai.org/

Installation

OLMo 2 will be supported in the next version of Transformers, and you need to install it from the main branch using:

pip install --upgrade git+https://github.com/huggingface/transformers.git

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-2-0325-32B-DPO")

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|>\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:

<|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.

System prompt

In Ai2 demos, we use this system prompt by default:

You are OLMo 2, a helpful and harmless AI Assistant built by the Allen Institute for AI.

The model has not been trained with a specific system prompt in mind.

Bias, Risks, and Limitations

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). See the Falcon 180B model card for an example of this.

Performance

Model	Average	AlpacaEval 2 LC	BBH	DROP	GSM8k	IFEval	MATH	MMLU	Safety	PopQA	TruthQA
Closed API models
GPT-3.5 Turbo 0125	59.6	38.7	66.6	70.2	74.3	66.9	41.2	70.2	69.1	45.0	62.9
GPT 4o Mini 2024-07-18	65.7	49.7	65.9	36.3	83.0	83.5	67.9	82.2	84.9	39.0	64.8
Open weights models
Mistral-Nemo-Instruct-2407	50.9	45.8	54.6	23.6	81.4	64.5	31.9	70.0	52.7	26.9	57.7
Ministral-8B-Instruct	52.1	31.4	56.2	56.2	80.0	56.4	40.0	68.5	56.2	20.2	55.5
Gemma-2-27b-it	61.3	49.0	72.7	67.5	80.7	63.2	35.1	70.7	75.9	33.9	64.6
Qwen2.5-32B	66.5	39.1	82.3	48.3	87.5	82.4	77.9	84.7	82.4	26.1	70.6
Mistral-Small-24B	67.6	43.2	80.1	78.5	87.2	77.3	65.9	83.7	66.5	24.4	68.1
Llama-3.1-70B	70.0	32.9	83.0	77.0	94.5	88.0	56.2	85.2	76.4	46.5	66.8
Llama-3.3-70B	73.0	36.5	85.8	78.0	93.6	90.8	71.8	85.9	70.4	48.2	66.1
Gemma-3-27b-it	-	63.4	83.7	69.2	91.1	-	-	81.8	-	30.9	-
Fully open models
OLMo-2-7B-1124-Instruct	55.7	31.0	48.5	58.9	85.2	75.6	31.3	63.9	81.2	24.6	56.3
OLMo-2-13B-1124-Instruct	61.4	37.5	58.4	72.1	87.4	80.4	39.7	68.6	77.5	28.8	63.9
OLMo-2-32B-0325-SFT	61.7	16.9	69.7	77.2	78.4	72.4	35.9	76.1	93.8	35.4	61.3
OLMo-2-32B-0325-DPO	68.8	44.1	70.2	77.5	85.7	83.8	46.8	78.0	91.9	36.4	73.5
OLMo-2-32B-0325-Instruct	68.8	42.8	70.6	78.0	87.6	85.6	49.7	77.3	85.9	37.5	73.2

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. This model has been fine-tuned using a dataset mix with outputs generated from third party models and are subject to additional terms: Gemma Terms of Use.

Citation

bibtex

@article{olmo20242olmo2furious,
      title={2 OLMo 2 Furious}, 
      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},
      year={2024},
      eprint={2501.00656},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2501.00656}, 
}