allenai

OLMoE-1B-7B-0125-Instruct

README

License: apache-2.0

Release Documentation

OLMoE-1B-7B-0125-Instruct January 2025 is post-trained variant of the OLMoE-1B-7B January 2025 model, which has undergone supervised finetuning on an OLMo-specific variant of the Tülu 3 dataset and further DPO training on this dataset, and finally RLVR training using this data. 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 OLMoE 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 (coming soon), and associated training details. The core models released in this batch include the following:

Table with columns: Stage, OLMoE 1B-7B
Stage	OLMoE 1B-7B
Base Model	allenai/OLMoE-1B-7B-0125
SFT	allenai/OLMoE-1B-7B-0125-SFT
DPO	allenai/OLMoE-1B-7B-0125-DPO
Final Models (RLVR)	allenai/OLMoE-1B-7B-0125-Instruct
Reward Model (RM)	allenai/OLMoE-1B-7B-0125-RM

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/OLMoE-1B-7B-0125-DPO

Model Sources

Project Page: https://allenai.org/olmo
Repositories:
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/olmes
- Further fine-tuning code: https://github.com/allenai/open-instruct
Paper: https://arxiv.org/abs/2409.02060
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:

bash

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:

text

from transformers import AutoModelForCausalLMolmo_model = AutoModelForCausalLM.from_pretrained("OLMoE-1B-7B-0125-Instruct")

Chat template

The chat template for our models is formatted as:

text

<|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:

text

<|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|>

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:

text

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

Table with columns: Benchmark (eval), OLMoE-1B-7B-0125-Instruct, OLMoE-1B-7B-0924-Instruct, OLMoE-1B-7B-0125-DPO, OLMoE-1B-7B-0125-SFT, OLMoE-1B-7B-0924-SFT
Benchmark (eval)	OLMoE-1B-7B-0125-Instruct	OLMoE-1B-7B-0924-Instruct	OLMoE-1B-7B-0125-DPO	OLMoE-1B-7B-0125-SFT	OLMoE-1B-7B-0924-SFT
Avg.	45.62	38.44	45.05	41.76	37.05
MMLU (CoT)	55.08	54.57	54.93	55.26	54.32

License and use

OLMoE is licensed under the Apache 2.0 license. OLMoE 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

@misc{muennighoff2024olmoeopenmixtureofexpertslanguage,      title={OLMoE: Open Mixture-of-Experts Language Models},       author={Niklas Muennighoff and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Jacob Morrison and Sewon Min and Weijia Shi and Pete Walsh and Oyvind Tafjord and Nathan Lambert and Yuling Gu and Shane Arora and Akshita Bhagia and Dustin Schwenk and David Wadden and Alexander Wettig and Binyuan Hui and Tim Dettmers and Douwe Kiela and Ali Farhadi and Noah A. Smith and Pang Wei Koh and Amanpreet Singh and Hannaneh Hajishirzi},      year={2024},      eprint={2409.02060},      archivePrefix={arXiv},      primaryClass={cs.CL},      url={https://arxiv.org/abs/2409.02060}, }@article{lambert2024tulu3,  title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},  author = {    Nathan Lambert and     Jacob Morrison and     Valentina Pyatkin and     Shengyi Huang and     Hamish Ivison and     Faeze Brahman and     Lester James V. Miranda and     Alisa Liu and     Nouha Dziri and     Shane Lyu and     Yuling Gu and     Saumya Malik and     Victoria Graf and     Jena D. Hwang and     Jiangjiang Yang and    Ronan Le Bras and    Oyvind Tafjord and    Chris Wilhelm and    Luca Soldaini and     Noah A. Smith and     Yizhong Wang and     Pradeep Dasigi and     Hannaneh Hajishirzi  },  year = {2024},  email = {tulu@allenai.org}}

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Model Details

Model Provider

allenai

Model Tree

Base

allenai/OLMoE-1B-7B-0125-DPO

Fine-tuned

this model

Input Modalities

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Output Modalities

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README

License: apache-2.0

Release Documentation

Table with columns: Stage, OLMoE 1B-7B
Stage	OLMoE 1B-7B
Base Model	allenai/OLMoE-1B-7B-0125
SFT	allenai/OLMoE-1B-7B-0125-SFT
DPO	allenai/OLMoE-1B-7B-0125-DPO
Final Models (RLVR)	allenai/OLMoE-1B-7B-0125-Instruct
Reward Model (RM)	allenai/OLMoE-1B-7B-0125-RM

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/OLMoE-1B-7B-0125-DPO

Model Sources

Project Page: https://allenai.org/olmo
Repositories:
- Core repo (training, inference, fine-tuning etc.): https://github.com/allenai/OLMo
- Evaluation code: https://github.com/allenai/olmes
- Further fine-tuning code: https://github.com/allenai/open-instruct
Paper: https://arxiv.org/abs/2409.02060
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:

bash

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:

text

from transformers import AutoModelForCausalLMolmo_model = AutoModelForCausalLM.from_pretrained("OLMoE-1B-7B-0125-Instruct")

Chat template

The chat template for our models is formatted as:

text

<|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:

text

<|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|>

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:

text

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

Performance

Table with columns: Benchmark (eval), OLMoE-1B-7B-0125-Instruct, OLMoE-1B-7B-0924-Instruct, OLMoE-1B-7B-0125-DPO, OLMoE-1B-7B-0125-SFT, OLMoE-1B-7B-0924-SFT
Benchmark (eval)	OLMoE-1B-7B-0125-Instruct	OLMoE-1B-7B-0924-Instruct	OLMoE-1B-7B-0125-DPO	OLMoE-1B-7B-0125-SFT	OLMoE-1B-7B-0924-SFT
Avg.	45.62	38.44	45.05	41.76	37.05
MMLU (CoT)	55.08	54.57	54.93	55.26	54.32

License and use

Citation

bibtex

@misc{muennighoff2024olmoeopenmixtureofexpertslanguage,      title={OLMoE: Open Mixture-of-Experts Language Models},       author={Niklas Muennighoff and Luca Soldaini and Dirk Groeneveld and Kyle Lo and Jacob Morrison and Sewon Min and Weijia Shi and Pete Walsh and Oyvind Tafjord and Nathan Lambert and Yuling Gu and Shane Arora and Akshita Bhagia and Dustin Schwenk and David Wadden and Alexander Wettig and Binyuan Hui and Tim Dettmers and Douwe Kiela and Ali Farhadi and Noah A. Smith and Pang Wei Koh and Amanpreet Singh and Hannaneh Hajishirzi},      year={2024},      eprint={2409.02060},      archivePrefix={arXiv},      primaryClass={cs.CL},      url={https://arxiv.org/abs/2409.02060}, }@article{lambert2024tulu3,  title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},  author = {    Nathan Lambert and     Jacob Morrison and     Valentina Pyatkin and     Shengyi Huang and     Hamish Ivison and     Faeze Brahman and     Lester James V. Miranda and     Alisa Liu and     Nouha Dziri and     Shane Lyu and     Yuling Gu and     Saumya Malik and     Victoria Graf and     Jena D. Hwang and     Jiangjiang Yang and    Ronan Le Bras and    Oyvind Tafjord and    Chris Wilhelm and    Luca Soldaini and     Noah A. Smith and     Yizhong Wang and     Pradeep Dasigi and     Hannaneh Hajishirzi  },  year = {2024},  email = {tulu@allenai.org}}