Model reference · open weights

OLMoE-0125

LLMs allenai Text gen · MoE 1 build Open weights 302k dl/mo

OLMoE-0125 is an open-weight language model from allenai. OLMoE-1B-7B-0125-Instruct (BF16) weighs 13.8 GB; the smallest configuration that runs it is RTX 4060 Ti 16 GB.

  • OLMoE-0125 is a 6.9B parameter open language model developed by allenai for text generation tasks.
  • It is an English-only model with a 4096 token context length, released under the Apache 2.0 license.
  • The model is a post-trained variant of the OLMoE-1B-7B base model, designed for research and educational use.

Summary of the allenai/OLMoE-1B-7B-0125-Instruct model card, 2026-10-01

What it is

Released byallenai
Released2025-01-27
Parameters6.9B
VRAM13.8 GB for the weights

What it runs on

Memory and cards for OLMoE-1B-7B-0125-Instruct (BF16)

13.8 GBweights, file size
131 MBcache per 1K tokens
523 MBruntime overhead, at least
4,096 tokenscontext max
CardRequests at onceContext maxMemory
4K each
RTX 3060 12 GB——11.6 GB
RTX 4060 Ti 16 GB1all 4K15.4 GB
RTX 3090 24 GB16all 4K23.4 GB
RTX 4090 24 GB16all 4K23.4 GB
RTX 5090 32 GB31all 4K31.0 GB
L40S 48 GB55all 4K44.0 GB
A100 80 GB118all 4K78.2 GB
H100 80 GB113all 4K78.1 GB
RTX PRO 6000 Blackwell 96 GB142all 4K93.8 GB
DGX Spark (GB10) 128 GB unified167all 4K107 GB
H200 141 GB225all 4K138 GB
B200 180 GB295all 4K176 GB
2× RTX 3060 12 GB
tensor parallel
15all 4K11.6 GB a card
2× RTX 4060 Ti 16 GB
tensor parallel
29all 4K15.4 GB a card
Memory needed at each load
Requests at once4K tokens each
114.9 GB
517.0 GB
818.7 GB
1623.0 GB
3231.5 GB
6448.7 GB

One card, with vLLM's small-card settings.

From the model card

What allenai says about OLMoE-0125

Read the model card

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:

StageOLMoE 1B-7B
Base Modelallenai/OLMoE-1B-7B-0125
SFTallenai/OLMoE-1B-7B-0125-SFT
DPOallenai/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:

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:

Or with new lines expanded:

How are you doing?
I'm just a computer program, so I don't have feelings, but I'm functioning as expected. How can I assist you today?

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-InstructOLMoE-1B-7B-0924-InstructOLMoE-1B-7B-0125-DPOOLMoE-1B-7B-0125-SFTOLMoE-1B-7B-0924-SFT
Avg.45.6238.4445.0541.7637.05
MMLU (CoT)55.0854.5754.9355.2654.32
PopQA19.7520.5619.6520.1221.01
TruthfulQA50.5649.1449.9945.4844.66
BigBenchHard (CoT)38.6136.7837.3737.3136.55
DROP47.8734.4848.3848.5734.71
MATH (Flex)21.418.1620.3621.388.15
GSM8K72.4047.3864.5955.7242.46
HumanEval62.3063.0461.9262.5863.72
HumanEval+54.3758.9357.6155.6757.40
IFEval66.3645

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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