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license: apache-2.0
language:
- en
pipeline_tag: text-generation
base_model:
- allenai/OLMoE-1B-7B-0125-DPO
library_name: transformers
datasets:
- allenai/RLVR-GSM
---
<img alt="OLMo Logo" src="https://huggingface.co/allenai/OLMoE-1B-7B-0125/resolve/main/olmoe-logo.png" width="242px">
# OLMoE-1B-7B-0125-Instruct
## Release Documentation
OLMoE-1B-7B-0125-Instruct January 2025 is post-trained variant of the [OLMoE-1B-7B January 2025](https://huggingface.co/allenai/OLMoE-1B-7B-0125) model, which has undergone supervised finetuning on an OLMo-specific variant of the [Tülu 3 dataset](allenai/tulu-3-sft-olmo-2-mixture) and further DPO training on [this dataset](https://huggingface.co/datasets/allenai/olmo-2-1124-13b-preference-mix), and finally RLVR training using [this data](https://huggingface.co/datasets/allenai/RLVR-GSM).
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](https://arxiv.org/abs/2409.02060) or [Tülu 3 paper](https://arxiv.org/abs/2411.15124) for more details!
OLMo is a series of **O**pen **L**anguage **Mo**dels 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:
| **Stage** | **OLMoE 1B-7B** |
|----------------------|----------------------------------------------------------------------------------------------------------|
| **Base Model** | [allenai/OLMoE-1B-7B-0125](https://huggingface.co/allenai/OLMoE-1B-7B-0125) |
| **SFT** | [allenai/OLMoE-1B-7B-0125-SFT](https://huggingface.co/allenai/OLMoE-1B-7B-0125-SFT) |
| **DPO** | [allenai/OLMoE-1B-7B-0125-DPO](https://huggingface.co/allenai/OLMoE-1B-7B-0125-DPO) |
| **Final Models (RLVR)** | [allenai/OLMoE-1B-7B-0125-Instruct](https://huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct) |
| **Reward Model (RM)**| [allenai/OLMoE-1B-7B-0125-RM](https://huggingface.co/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:
```
from transformers import AutoModelForCausalLM
olmo_model = AutoModelForCausalLM.from_pretrained("OLMoE-1B-7B-0125-Instruct")
```
### 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|>
```
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
| 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 |
| **PopQA** | 19.75 | 20.56 | 19.65 | 20.12 | **21.01** |
| **TruthfulQA** | **50.56** | 49.14 | 49.99 | 45.48 | 44.66 |
| **BigBenchHard (CoT)** | **38.61** | 36.78 | 37.37 | 37.31 | 36.55 |
| **DROP** | 47.87 | 34.48 | 48.38 | **48.57** | 34.71 |
| **MATH (Flex)** | **21.41** | 8.16 | 20.36 | 21.38 | 8.15 |
| **GSM8K** | **72.40** | 47.38 | 64.59 | 55.72 | 42.46 |
| **HumanEval** | 62.30 | 63.04 | 61.92 | 62.58 | **63.72** |
| **HumanEval+** | 54.37 | **58.93** | 57.61 | 55.67 | 57.40 |
| **IFEval** | **66.36** | 45.29 | 65.62 | 56.56 | 41.22 |
| **AlpacaEval** | 17.99 | 7.54 | **19.50** | 5.83 | 6.38 |
| **Safety (average)** | 90.40 | 51.40 | 91.40 | **94.50** | 65.80 |
## 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](https://allenai.org/responsible-use).
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](https://ai.google.dev/gemma/terms).
## 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}
}
```
|