OLMo 2 is the latest in a series of Open Language Models designed to enable the science of language models. We have released all code, checkpoints, logs, and associated training details on GitHub.

Size	Training Tokens	Layers	Hidden Size	Attention Heads	Context Length
OLMo 2-1B	4 Trillion	16	2048	16	4096
OLMo 2-7B	4 Trillion	32	4096	32	4096
OLMo 2-13B	5 Trillion	40	5120	40	4096
OLMo 2-32B	6 Trillion	64	5120	40	4096

The core models released in this batch include the following:

Stage	OLMo 2 1B	OLMo 2 7B	OLMo 2 13B	OLMo 2 32B
Base Model	allenai/OLMo-2-0425-1B	allenai/OLMo-2-1124-7B	allenai/OLMo-2-1124-13B	allenai/OLMo-2-0325-32B
SFT	allenai/OLMo-2-0425-1B-SFT	allenai/OLMo-2-1124-7B-SFT	allenai/OLMo-2-1124-13B-SFT	allenai/OLMo-2-0325-32B-SFT
DPO	allenai/OLMo-2-0425-1B-DPO	allenai/OLMo-2-1124-7B-DPO	allenai/OLMo-2-1124-13B-DPO	allenai/OLMo-2-0325-32B-DPO
Final Models (RLVR)	allenai/OLMo-2-0425-1B-Instruct	allenai/OLMo-2-1124-7B-Instruct	allenai/OLMo-2-1124-13B-Instruct	allenai/OLMo-2-0325-32B-Instruct
Reward Model (RM)		allenai/OLMo-2-1124-7B-RM	(Same as 7B)

Installation

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

Inference

You can use OLMo with the standard HuggingFace transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-2-0425-1B")
message = ["Language modeling is "]
inputs = tokenizer(message, return_tensors='pt', return_token_type_ids=False)
# optional verifying cuda
# inputs = {k: v.to('cuda') for k,v in inputs.items()}
# olmo = olmo.to('cuda')
response = olmo.generate(**inputs, max_new_tokens=100, do_sample=True, top_k=50, top_p=0.95)
print(tokenizer.batch_decode(response, skip_special_tokens=True)[0])
>> 'Language modeling is  a key component of any text-based application, but its effectiveness...'

For faster performance, you can quantize the model using the following method:

python

AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B",
    torch_dtype=torch.float16,
    load_in_8bit=True)  # Requires bitsandbytes

The quantized model is more sensitive to data types and CUDA operations. To avoid potential issues, it's recommended to pass the inputs directly to CUDA using:

inputs.input_ids.to('cuda')

We have released checkpoints for these models. For pretraining, the naming convention is stage1-stepXXX-tokensYYYB. For checkpoints with ingredients of the soup, the naming convention is stage2-ingredientN-stepXXX-tokensYYYB

To load a specific model revision with HuggingFace, simply add the argument revision:

olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")

Or, you can access all the revisions for the models via the following code snippet:

python

from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/OLMo-2-0425-1B")
branches = [b.name for b in out.branches]

Fine-tuning

Model fine-tuning can be done from the final checkpoint (the main revision of this model) or many intermediate checkpoints. Two recipes for tuning are available.

Fine-tune with the OLMo repository:

bash

torchrun --nproc_per_node=8 scripts/train.py {path_to_train_config} \
    --data.paths=[{path_to_data}/input_ids.npy] \
    --data.label_mask_paths=[{path_to_data}/label_mask.npy] \
    --load_path={path_to_checkpoint} \
    --reset_trainer_state

For more documentation, see the GitHub README.

Further fine-tuning support is being developing in AI2's Open Instruct repository. Details are here.

Model Description

Developed by: Allen Institute for AI (Ai2)
Model type: a Transformer style autoregressive language model.
Language(s) (NLP): English
License: The code and model are released under Apache 2.0.
Contact: Technical inquiries: olmo@allenai.org. Press: press@allenai.org
Date cutoff: Dec. 2023.

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/OLMo-Eval
- Further fine-tuning code: https://github.com/allenai/open-instruct
Paper: https://arxiv.org/abs/2501.00656

Evaluation

Core model results for OLMo 2 1B are found below.

Instruct Model	Avg	FLOP×10²³	AE2	BBH	DROP	GSM8K	IFE	MATH	MMLU	Safety	PQA	TQA
Closed API models
GPT-3.5 Turbo 0125	60.5	n/a	38.7	66.6	70.2	74.3	66.9	41.2	70.2	69.1	45.0	62.9
GPT 4o Mini 0724	65.7	n/a	49.7	65.9	36.3	83.0	83.5	67.9	82.2	84.9	39.0	64.8
Open weights models 1-1.7B Parameters
SmolLM2 1.7B	34.2	1.1	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	1.2	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	6.7	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	1.7	7.4	45.8	13.4	66.2	44.2	40.6	59.7	77.6	15.5	46.5
Fully-open models
OLMo 1B 0724	24.4	0.22	2.4	29.9	27.9	10.8	25.3	2.2	36.6	52.0	12.1	44.3
OLMo 2 1B	42.7	0.35	9.1	35.0	34.6	68.3	70.1	20.7	40.0	87.6	12.9	48.7

Model Details

Training

	OLMo 2 1B	OLMo 2 7B	OLMo 2 13B	OLMo 2 32B
Pretraining Stage 1	4 trillion tokens (1 epoch)	4 trillion tokens (1 epoch)	5 trillion tokens (1.2 epochs)	6 trillion tokens (1.5 epochs)
Pretraining Stage 2	50B tokens	50B tokens (3 runs) merged	100B tokens (3 runs) 300B tokens (1 run) merged	100B tokens (3 runs) 300B tokens (1 run) merged
Post-training	SFT+DPO+GRPO (preference mix)	SFT + DPO + PPO (preference mix)	SFT + DPO + PPO (preference mix)	SFT + DPO + GRPO (preference mix)

Stage 1: Initial Pretraining

Dataset: OLMo-mix-1124 (3.9T tokens)
Coverage: 95%+ of total pretraining budget
1B Model: ~1 epoch

Stage 2: Mid-training

Dataset: Dolmino-Mix-1124
One training mix:
- 50B tokens
Mix composition: 50% high-quality web data + academic/Q&A/instruction/math content

Model Merging

1B Model: only 1 version is trained on a 50B mix (ingredient 3), we did not merge. Ingredients 1 and 2 are just exploratory runs.

Bias, Risks, and Limitations

Like any base or fine-tuned language model, AI can be prompted by users to generate harmful and sensitive content. Such content may also be produced unintentionally, especially in cases involving bias, so we recommend that users consider the risks when applying this technology. Additionally, many statements from OLMo or any LLM are often inaccurate, so facts should be verified.

Citation

@misc{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},
}

Model Card Contact

For errors in this model card, contact olmo@allenai.org.

lczh/OLMo-2-0425-1B-topk

Model Details

Model Card for OLMo 2 1B

Installation

Inference

Fine-tuning

Model Description

Model Sources

Evaluation

Model Details

Training

Stage 1: Initial Pretraining

Stage 2: Mid-training

Model Merging

Bias, Risks, and Limitations

Citation

Model Card Contact

lczh/
OLMo-2-0425-1B-topk