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from-our-page/Olmo-3-1125-32B-stage1-step656000

sourceHugging Faceapache-2.0updated 9mo agoView on Hugging Face

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Model Details

Model Card for Olmo 3 32B

We introduce Olmo 3, a new family of 7B and 32B models featuring a [TODO: insert gain in performance], among other evaluation improvements, compared to the most recent Olmo 2 7B model. These gains come from training on dolma3-mix-1025] and dolma3-dolmino-mix-1025 datasets and staged training approach.

Olmo is a series of Open language models designed to enable the science of language models. These models are trained on the Dolma 3 dataset. We are releasing all code, checkpoints, logs (coming soon), and associated training details.

Size	Training Tokens	Layers	Hidden Size	Attention Heads	Context Length
Olmo 3 7B	5.93T	32	4096	32	8192
Olmo 3 32B

The core models released in this batch include the following:

Stage	[Olmo 3 7B]	[Olmo 3 32B]
Base Model
SFT
DPO
Final Models (RLVR)
Reward Model (RM)

Installation

Olmo 3 is supported in transformers [TODO] vX.XX or higher:

bash

pip install transformers>=X.XX [TODO]

Inference

You can use OLMo with the standard HuggingFace transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1125-32B")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-1125-32B")
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-3-1125-32B", 
    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:

python

inputs.input_ids.to('cuda')

We have released checkpoints for these models. For pretraining, the naming convention is stage1-stepXXX.

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

bash

olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-1125-32B", revision="stage1-step10000")

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-3-1125-32B")
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.

1.Fine-tune with the OLMo-core repository:

bash

torchrun --nproc-per-node=8 ./src/scripts/official/MODEL.py run01

You can override most configuration options from the command-line. For example, to override the learning rate you could launch the script like this:

bash

torchrun --nproc-per-node=8 ./src/scripts/train/MODEL.py run01 --train_module.optim.lr=6e-3

For more documentation, see the GitHub readme.

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-core
—Evaluation code: https://github.com/allenai/OLMo-Eval
—Further fine-tuning code: https://github.com/allenai/open-instruct
—Paper: [TBD]

Evaluation

Core model results for MODELS are found below.

Model	Training FLOPs	Average	ARC/C	HSwag	WinoG	MMLU	DROP	NQ	AGIEval	GSM8k	MMLUPro	TriviaQA
Open weights models
Llama-2-13B	1.6 · 10^23	54.1	67.3	83.9	74.9	55.7	45.6	38.4	41.5	28.1	23.9	81.3
Mistral-7B-v0.3	n/a	58.8	78.3	83.1	77.7	63.5	51.8	37.2	47.3	40.1	30	79.3
Llama-3.1-8B	7.2 · 10^23	61.8	79.5	81.6	76.6	66.9	56.4	33.9	51.3	56.5	34.7	80.3
Mistral-Nemo-12B	n/a	66.9	85.2	85.6	81.5	69.5	69.2	39.7	54.7	62.1	36.7	84.6
Qwen-2.5-7B	8.2 · 10^23	67.4	89.5	89.7	74.2	74.4	55.8	29.9	63.7	81.5	45.8	69.4
Gemma-2-9B	4.4 · 10^23	67.8	89.5	87.3	78.8	70.6	63	38	57.3	70.1	42	81.8
Mistral-Small-24B	n/a	75.2	93.3	91.3	77.8	80.7	74.4	42.3	69.1	79.7	54.2	88.8
Gemma-2-27B	2.1 · 10^24	71.3	90.7	88.4	74.5	75.7	70.1	44.7	61.5	75.7	44.7	87.4
Qwen-2.5-14B	1.6 · 10^24	72.2	94.0	94.0	80.0	79.3	51.5	37.3	71.0	83.4	52.8	79.1
Qwen-2.5-32B	3.5 · 10^24	74.9	95.6	96.0	84.0	83.1	53.1	37.0	78.0	83.3	59.0	79.9
Partially open models
StableLM-2-12B	2.9 · 10^23	62.2	81.9	84.5	77.7	62.4	55.5	37.6	50.9	62	29.3	79.9
Zamba-2-7B	n/c	65.2	92.2	89.4	79.6	68.5	51.7	36.5	55.5	67.2	32.8	78.8
Fully open models
Amber-7B	0.5 · 10^23	35.2	44.9	74.5	65.5	24.7	26.1	18.7	21.8	4.8	11.7	59.3
OLMo-7B	1.0 · 10^23	38.3	46.4	78.1	68.5	28.3	27.3	24.8	23.7	9.2	12.1	64.1
MAP-Neo-7B	2.1 · 10^23	49.6	78.4	72.8	69.2	58	39.4	28.9	45.8	12.5	25.9	65.1
OLMo-0424-7B	0.9 · 10^23	50.7	66.9	80.1	73.6	54.3	50	29.6	43.9	27.7	22.1	58.8
DCLM-7B	1.0 · 10^23	56.9	79.8	82.3	77.3	64.4	39.3	28.8	47.5	46.1	31.3	72.1
OLMo-2-1124-7B	1.8 · 10^23	62.9	79.8	83.8	77.2	63.7	60.8	36.9	50.4	67.5	31.0	78
OLMo-2-1124-13B	4.6 · 10^23	68.3	83.5	86.4	81.5	67.5	70.7	46.7	54.2	75.1	35.1	81.9
OLMo-2-0325-32B	1.3 · 10^24	72.9	90.4	89.7	78.7	74.9	74.3	50.2	61.0	78.8	43.3	88.0
Olmo-3-1025-7B
Olmo-3-1025-32B

Model Details

Stage 1: Initial Pretraining

—Dataset: dolma3-mix-1025 (6T tokens)
—Coverage: X%+ of total pretraining budget
—MODEL: ~X epoch

Stage 2: Mid-training

—Dataset: dolma3-dolmino-mix-1025 (100B tokens)
—Mix composition: XYZ

Stage 3: Long Context

—Dataset: [dolma3-longmino-mix-1025]() (XX tokens)
—Mix composition: XYZ

Model Merging

—MODEL:

Bias, Risks, and Limitations

Like any base language model or fine-tuned model without safety filtering, these models can easily 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

A technical manuscript is forthcoming!

Model Card Contact

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