We introduce Olmo 3, a new family of 7B and 32B models both Instruct and Think variants. Long chain-of-thought thinking improves reasoning tasks like math and coding.

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

The core models released in this batch include the following:

Stage	Olmo 3 7B Think	Olmo (3/3.1) 32B Think	Olmo 3 7B Instruct	Olmo 3.1 32B Instruct
Base Model	Olmo-3-7B	Olmo-3-32B	Olmo-3-7B	Olmo-3-32B
SFT	Olmo-3-7B-Think-SFT	Olmo-3-32B-Think-SFT	Olmo-3-7B-Instruct-SFT	Olmo-3.1-32B-Instruct-SFT
DPO	Olmo-3-7B-Think-DPO	Olmo-3-32B-Think-DPO	Olmo-3-7B-Instruct-DPO	Olmo-3.1-32B-Instruct-DPO
Final Models (RLVR)	Olmo-3-7B-Think	Olmo-3-32B-Think Olmo-3.1-32B-Think	Olmo-3-7B-Instruct	Olmo-3.1-32B-Instruct

Installation

Olmo 3 is supported in transformers 4.57.0 or higher:

pip install transformers>=4.57.0

Inference

You can use OLMo with the standard HuggingFace transformers library:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3.1-32B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3.1-32B-Instruct")
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-3.1-32B-Instruct", 
    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 post-training, the naming convention is step_XXXX. NOTE: For this model, due to a checkpointing issue, we only are releasing the final few checkpoints. See our other RL jobs for more detailed intermediate checkpoint suite.

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

olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3.1-32B-Instruct", revision="step_1375")

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-3.1-32B-Instruct")
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-core repository:

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:

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: This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.
Contact: Technical inquiries: olmo@allenai.org. Press: press@allenai.org
Date cutoff: Dec. 2024.

Model Sources

Project Page: https://allenai.org/olmo
Repositories:
- Open-Instruct for DPO and RLVR: https://github.com/allenai/open-instruct
- OLMo-Core for pre-training and SFT: https://github.com/allenai/OLMo-core
- OLMo-Eval for evaluation: https://github.com/allenai/OLMo-Eval
Paper:: https://allenai.org/papers/olmo3

Evaluation

Metric	Olmo 3.1 32B Instruct SFT	Olmo 3.1 32B Instruct DPO	Olmo 3.1 32B Instruct	Apertus 70B	Qwen 3 32B (No Think)	Qwen 3 VL 32B Instruct	Qwen 2.5 32B	Gemma 3 27B	Gemma 2 27B	OLMo 2 32B
Math
MATH	74.4	86.6	93.4	36.2	84.3	95.1	80.2	87.4	51.5	49.2
AIME 2024	12.7	35.2	67.8	0.31	27.9	75.4	15.7	28.9	4.7	4.6
AIME 2025	8.2	23.3	57.9	0.1	21.3	64.2	13.4	22.9	0.9	0.9
OMEGA	15.5	33.3	42.2	5.6	23.4	44.0	19.2	24.0	9.1	9.8
Reasoning
BigBenchHard	69.0	82.1	84.0	57.0	80.4	89.0	80.9	82.4	66.0	65.6
ZebraLogic	30.6	51.1	61.7	9.0	28.4	86.7	24.1	24.8	17.2	13.3
AGI Eval English	71.7	79.4	79.5	61.6	82.4	89.4	78.9	76.9	70.9	68.4
Coding
HumanEvalPlus	80.8	85.7	86.7	42.9	83.9	89.3	82.6	79.2	67.5	44.4
MBPP+	61.5	63.6	65.1	45.8	67.9	69.0	66.6	65.7	61.2	49.0
LiveCodeBench v3	35.4	49.6	54.7	9.7	57.5	70.2	49.9	39.0	28.7	10.6
IF
IFEval	87.7	87.3	88.8	70.4	87.5	88.1	81.9	85.4	62.1	85.8
IFBench	29.7	36.3	39.7	26.0	31.3	37.2	36.7	31.3	27.8	36.4
Knowledge & QA
MMLU	79.0	81.9	80.9	70.2	85.8	88.7	84.6	74.6	76.1	77.1
PopQA	23.7	28.5	25.0	33.5	25.9	25.7	28.0	30.2	30.4	37.2
GPQA	41.3	47.9	48.6	27.9	54.4	61.4	44.6	45.0	39.9	36.4
Chat
AlpacaEval 2 LC	42.2	69.7	59.8	19.9	67.9	84.3	81.9	65.5	39.8	38.0
Safety	92.1	88.9	89.5	77.1	81.6	85.8	82.2	68.8	74.4	84.2

Model Details

Stage 1: SFT

supervised fine-tuning on the Dolci-Think-SFT-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
Datasets: Dolci-Think-SFT-7B, Dolci-Instruct-SFT

Stage 2:DPO

direct preference optimization on the Dolci-Think-DPO-7B dataset. This dataset consits of math, code, chat, and general knowledge queries.
Datasets: Dolci-Think-DPO-7B, Dolci-Instruct-DPO

Stage 3: RLVR

reinforcement learning from verifiable rewards on the Dolci-Think-RL-7B dataset. This dataset consits of math, code, instruction-following, and general chat queries.
Datasets: Dolci-Think-RL-7B, Dolci-Instruct-RL

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.

License

This model is licensed under Apache 2.0. It is intended for research and educational use in accordance with Ai2's Responsible Use Guidelines.

Citation

A technical manuscript is forthcoming!

Model Card Contact

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

cyankiwi/Olmo-3.1-32B-Instruct-AWQ-4bit

Model Details

Model Card for Olmo-3.1-32B-Instruct

Installation

Inference

Fine-tuning

Model Description

Model Sources

Evaluation

Model Details

Stage 1: SFT

Stage 2:DPO

Stage 3: RLVR

Bias, Risks, and Limitations

License

Citation

Model Card Contact

cyankiwi/
Olmo-3.1-32B-Instruct-AWQ-4bit