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Model Card for OLMo 1.7-7B

For transformers versions v4.40.0 or newer, please use OLMo 1.7-7B HF instead.

OLMo 1.7 7B is the latest version of the original OLMo 7B model rocking a 24 point increase in MMLU, among other evaluations improvements, from an improved version of the Dolma dataset and staged training.

OLMo is a series of Open Language Models designed to enable the science of language models. The OLMo models are trained on the Dolma dataset. We release all code, checkpoints, logs, and details involved in training these models.

Model Details

The core models released in this batch are the following:

Size Training Tokens Layers Hidden Size Attention Heads Context Length
OLMo 1B 3 Trillion 16 2048 16 2048
OLMo 7B 2.5 Trillion 32 4096 32 2048
OLMo 7B Twin 2T 2 Trillion 32 4096 32 2048
OLMo 1.7-7B 2.05 Trillion 32 4096 32 4096

Note: OLMo 1.7-7B also includes QKV clipping.

[Coming soon] We are releasing many checkpoints for these models, for every 1000 training steps. The naming convention is step1000-tokens4B.

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

import hf_olmo # pip install ai2-olmo
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-7B", revision="step1000-tokens4B")

All revisions/branches are listed in the file revisions.txt. Or, you can access all the revisions for the models via the following code snippet:

from huggingface_hub import list_repo_refs
out = list_repo_refs("allenai/OLMo-1.7-7B")
branches = [b.name for b in out.branches]

A few revisions were lost due to an error, but the vast majority are present.

Model Description

  • Developed by: Allen Institute for AI (AI2)
  • Supported by: Databricks, Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University, AMD, CSC (Lumi Supercomputer), UW
  • 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 at allenai dot org. Press: press at allenai dot org
  • Date cutoff: Oct. 2023, with most data from Feb./March 2023 based on Dolma dataset version.

Model Sources



Note: The OLMo models will shortly be included in Transformers. When the PR is merged, you will no longer need to use trust_remote_code=True or install ai2-olmo to use the model. Then, install Transformers from source.

Quickly get inference running with the following required installation:

pip install ai2-olmo

Now, proceed as usual with HuggingFace:

import hf_olmo

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-7B")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1.7-7B")
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 the first step to build natural language generation...'

Alternatively, with the pipeline abstraction:

import hf_olmo

from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1.7-7B")
print(olmo_pipe("Language modeling is "))
>> 'Language modeling is a branch of natural language processing that aims to...'

Or, you can make this slightly faster by quantizing the model, e.g. AutoModelForCausalLM.from_pretrained("allenai/OLMo-1.7-7B", torch_dtype=torch.float16, load_in_8bit=True) (requires bitsandbytes). The quantized model is more sensitive to typing / cuda, so it is recommended to pass the inputs as inputs.input_ids.to('cuda') to avoid potential issues.

Note, you may see the following error if ai2-olmo is not installed correctly, which is caused by internal Python check naming. We'll update the code soon to make this error clearer.

    raise ImportError(
ImportError: This modeling file requires the following packages that were not found in your environment: hf_olmo. Run `pip install hf_olmo`


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.

  1. Fine-tune with the OLMo repository:
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} \

For more documentation, see the GitHub readme.

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


Core model results for the new and original 7B model are found below.

Task Llama-7b Llama2-7b Falcon-7b Mpt-7b OLMo-7B Llama2-13b OLMo 1.7-7B
arc_c 44.5 48.5 47.5 46.5 48.5 52.8 42.5
arc_e 67.9 69.5 70.4 70.5 65.4 73.7 67.2
boolq 75.4 80.2 74.6 74.2 73.4 82.2 83.7
copa 91.0 86.0 86.0 85.0 90.0 90.0 86.0
hellaswag 76.2 76.8 75.9 77.6 76.4 78.6 75.5
openbookqa 51.2 48.4 53.0 48.6 50.4 51.8 50.0
piqa 77.2 76.7 78.5 77.3 78.4 79.0 77.5
sciq 93.9 94.5 93.9 93.7 93.8 95.5 96.7
winogrande 70.5 69.4 68.9 69.9 67.9 73.5 69.8
truthfulQA (MC2) 33.9 38.5 34.0 33.0 36.0 36.8 35.8
MMLU (5 shot MC) 31.5 45.0 24.0 30.8 28.3 55.5 52.0
GSM8k 10.0 12.0 4.0 4.5 8.5 25.0 29.0
Full average 60.3 62.1 59.2 59.3 59.8 66.2 63.8

And for the 1B model:

task random StableLM 2 1.6b* Pythia 1B TinyLlama 1.1B OLMo 1B (ours)
arc_challenge 25 43.81 33.11 34.78 34.45
arc_easy 25 63.68 50.18 53.16 58.07
boolq 50 76.6 61.8 64.6 60.7
copa 50 84 72 78 79
hellaswag 25 68.2 44.7 58.7 62.5
openbookqa 25 45.8 37.8 43.6 46.4
piqa 50 74 69.1 71.1 73.7
sciq 25 94.7 86 90.5 88.1
winogrande 50 64.9 53.3 58.9 58.9
Average 36.11 68.41 56.44 61.48 62.42

*Unlike OLMo, Pythia, and TinyLlama, StabilityAI has not disclosed yet the data StableLM was trained on, making comparisons with other efforts challenging.

Model Details


For training data details, please see the Dolma documentation. This model uses the new 1.7 version with more data sources, better deduplication, and quality filtering. During the annealing phase we use a higher quality subset of Dolma with a linearly decaying learning rate to 0.

Staged training / annealing

In contrast to OLMo 1.0, we trained OLMo 1.7 with a two-stage curriculum:

  • In the first stage, we trained the model from scratch on the Dolma 1.7 dataset. We set a cosine learning rate schedule with a warmup of 2500 steps, a peak learning rate of 3e-4, and a cosine decay to 3e-5 after 3T tokens. We cut off this stage after 2T tokens, when the learning rate is still high.
  • At this point we switch to the second stage, in which we train on a higher-quality subset of Dolma 1.7 (see below) for another 50B tokens, while linearly decaying the learning rate to 0. Our high-quality subset includes (1) using all available Wikipedia, OpenWebMath and Flan data, (2) removing Dolma CC, CC News, and Megawika, and (3) rebalancing remaining sources to achieve approximately equal proportions of each. See exact token counts and relative proportions of this second stage mix below. Both stages contribute equally to the final performance of the OLMo model. After the first stage, OLMo 1.7 already outperforms OLMo 1.0. The second stage consistently adds 2 to 3 points of performance on top.


OLMo 7B architecture with peer models for comparison.

OLMo 7B Llama 2 7B OpenLM 7B Falcon 7B PaLM 8B
d_model 4096 4096 4096 4544 4096
num heads 32 32 32 71 16
num layers 32 32 32 32 32
MLP ratio ~8/3 ~8/3 ~8/3 4 4
LayerNorm type non-parametric LN RMSNorm parametric LN parametric LN parametric LN
pos embeddings RoPE RoPE RoPE RoPE RoPE
attention variant full GQA full MQA MQA
biases none none in LN only in LN only none
block type sequential sequential sequential parallel parallel
activation SwiGLU SwiGLU SwiGLU GeLU SwiGLU
sequence length 2048 4096 2048 2048 2048
batch size (instances) 2160 1024 2048 2304 512
batch size (tokens) ~4M ~4M ~4M ~4M ~1M
weight tying no no no no yes


AdamW optimizer parameters are shown below.

Size Peak LR Betas Epsilon Weight Decay
1B 4.0E-4 (0.9, 0.95) 1.0E-5 0.1
7B 3.0E-4 (0.9, 0.99) 1.0E-5 0.1

Optimizer settings comparison with peer models.

OLMo 7B Llama 2 7B OpenLM 7B Falcon 7B
warmup steps 5000 2000 2000 1000
peak LR 3.0E-04 3.0E-04 3.0E-04 6.0E-04
minimum LR 3.0E-05 3.0E-05 3.0E-05 1.2E-05
weight decay 0.1 0.1 0.1 0.1
beta1 0.9 0.9 0.9 0.99
beta2 0.95 0.95 0.95 0.999
epsilon 1.0E-05 1.0E-05 1.0E-05 1.0E-05
LR schedule linear cosine cosine cosine
gradient clipping global 1.0 global 1.0 global 1.0 global 1.0
gradient reduce dtype FP32 FP32 FP32 BF16
optimizer state dtype FP32 most likely FP32 FP32 FP32

Environmental Impact

OLMo 7B variants were either trained on MI250X GPUs at the LUMI supercomputer, or A100-40GB GPUs provided by MosaicML. A summary of the environmental impact. Further details are available in the paper.

GPU Type Power Consumption From GPUs Carbon Intensity (kg CO₂e/KWh) Carbon Emissions (tCO₂eq)
OLMo 7B Twin MI250X (LUMI supercomputer) 135 MWh 0* 0*
OLMo 7B A100-40GB (MosaicML) 104 MWh 0.656 75.05

Bias, Risks, and Limitations

Like any base language model or fine-tuned model without safety filtering, it is relatively easy for a user to prompt these models to generate harmful and generally sensitive content. Such content can also be produced unintentionally, especially in the case of bias, so we recommend users consider the risks of applications of this technology.

Otherwise, many facts from OLMo or any LLM will often not be true, so they should be checked.



  title={OLMo: Accelerating the Science of Language Models},
  author={Groeneveld, Dirk and Beltagy, Iz and Walsh, Pete and Bhagia, Akshita and Kinney, Rodney and Tafjord, Oyvind and Jha, Ananya Harsh and Ivison, Hamish and Magnusson, Ian and Wang, Yizhong and Arora, Shane and Atkinson, David and Authur, Russell and Chandu, Khyathi and Cohan, Arman and Dumas, Jennifer and Elazar, Yanai and Gu, Yuling and Hessel, Jack and Khot, Tushar and Merrill, William and Morrison, Jacob and Muennighoff, Niklas and Naik, Aakanksha and Nam, Crystal and Peters, Matthew E. and Pyatkin, Valentina and Ravichander, Abhilasha and Schwenk, Dustin and Shah, Saurabh and Smith, Will and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},


Groeneveld, D., Beltagy, I., Walsh, P., Bhagia, A., Kinney, R., Tafjord, O., Jha, A., Ivison, H., Magnusson, I., Wang, Y., Arora, S., Atkinson, D., Authur, R., Chandu, K., Cohan, A., Dumas, J., Elazar, Y., Gu, Y., Hessel, J., Khot, T., Merrill, W., Morrison, J., Muennighoff, N., Naik, A., Nam, C., Peters, M., Pyatkin, V., Ravichander, A., Schwenk, D., Shah, S., Smith, W., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Lo, K., Soldaini, L., Smith, N., & Hajishirzi, H. (2024). OLMo: Accelerating the Science of Language Models. Preprint.

Model Card Contact

For errors in this model card, contact Nathan, {nathanl} at allenai dot org.

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