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 (coming soon), and details involved in training these models. This model has been converted from allenai/OLMo-1B for the Hugging Face Transformers format.

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

We are releasing many checkpoints for these models, for every 1000 training steps. These have not yet been converted into Hugging Face Transformers format, but are available in allenai/OLMo-1B.

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: Feb./March 2023 based on Dolma dataset version.

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: Link
Technical blog post: https://blog.allenai.org/olmo-open-language-model-87ccfc95f580
W&B Logs: https://wandb.ai/ai2-llm/OLMo-1B/reports/OLMo-1B--Vmlldzo2NzY1Njk1

Uses

Inference

Quickly get inference running with the following:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/OLMo-1B-hf")
tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-1B-hf")
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:

python

from transformers import pipeline
olmo_pipe = pipeline("text-generation", model="allenai/OLMo-1B-hf")
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-1B-hf", 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.

Fine-tuning

This model does not directly support our fine-tuning processes. Model fine-tuning can be done from the final checkpoint or many intermediate checkpoints of allenai/OLMo-1B.

Evaluation

Core model results for the 7B model are found below.

	Llama 7B	Llama 2 7B	Falcon 7B	MPT 7B	OLMo 7B (ours)
arc_challenge	44.5	39.8	47.5	46.5	48.5
arc_easy	57.0	57.7	70.4	70.5	65.4
boolq	73.1	73.5	74.6	74.2	73.4
copa	85.0	87.0	86.0	85.0	90
hellaswag	74.5	74.5	75.9	77.6	76.4
openbookqa	49.8	48.4	53.0	48.6	50.2
piqa	76.3	76.4	78.5	77.3	78.4
sciq	89.5	90.8	93.9	93.7	93.8
winogrande	68.2	67.3	68.9	69.9	67.9
Core tasks average	68.7	68.4	72.1	71.5	71.6
truthfulQA (MC2)	33.9	38.5	34.0	33	36.0
MMLU (5 shot MC)	31.5	45.0	24.0	30.8	28.3
GSM8k (mixed eval.)	10.0 (8shot CoT)	12.0 (8shot CoT)	4.0 (5 shot)	4.5 (5 shot)	8.5 (8shot CoT)
Full average	57.8	59.3	59.2	59.3	59.8

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

Data

For training data details, please see the Dolma documentation.

Architecture

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

Hyperparameters

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.

Citation

BibTeX:

@article{Groeneveld2023OLMo,
  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 Strubell, Emma and Subramani, Nishant and Wortsman, Mitchell and Dasigi, Pradeep and Lambert, Nathan and Richardson, Kyle and Zettlemoyer, Luke and Dodge, Jesse and Lo, Kyle and Soldaini, Luca and Smith, Noah A. and Hajishirzi, Hannaneh},
  journal={Preprint},
  year={2024}
}

APA:

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., Strubell, E., Subramani, N., Wortsman, M., Dasigi, P., Lambert, N., Richardson, K., Dodge, J., Zettlemoyer, L., 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, Akshita or Shane, {nathanl, akshitab, shanea} at allenai dot org.