Olmo-3-1125-32B API & Inference Endpoint

Model Details

Model Card for Olmo 3 32B

We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a 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, and associated training details.

Table with columns: Size, Training Tokens, Layers, Hidden Size, Q Heads, KV Heads, Context Length
Size	Training Tokens	Layers	Hidden Size	Q Heads	KV Heads	Context Length
OLMo 3 7B	5.93 Trillion	32	4096	32	32	65,536
OLMo 3 32B	5.50 Trillion	64	5120	40	8	65,536

The core models released in this batch include the following:

Table with columns: Stage, Olmo 3 7B Think, Olmo 3 32B Think, Olmo 3 7B Instruct
Stage	Olmo 3 7B Think	Olmo 3 32B Think	Olmo 3 7B Instruct
Base Model	Olmo-3-7B	Olmo-3-32B	Olmo-3-7B
SFT	Olmo-3-7B-Think-SFT	Olmo-3-32B-Think-SFT

Installation

Olmo 3 is supported in transformers v4.57.0 or higher:

bash
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-3-1125-32B")
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=0, temperature=1.0, top_p=0.7)
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. The conventions for midtraining and long context are stage2-ingredientY-stepXXX and stage3-stepXXX, respectively.

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.

Fine-tune with the OLMo-core repository:

bash
torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-32B-pretrain.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/official/OLMo3/OLMo-3-1025-32B-pretrain.py run01 --train_module.optim.lr=6e-4

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 2024

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
W&B Report: https://wandb.ai/ai2-llm/Olmo-3-1125-32B/reports/Olmo-3-32B-November-2025--VmlldzoxNTA4NzAxMw
Paper: https://allenai.org/papers/olmo3

Evaluation

Core model results for MODELS are found below.

Table with columns: Model, Olmo 3-Eval Math, BigCodeBench, HumanEval, DeepSeek LeetCode, DS 1000, MBPP, MultiPL HumanEval, MultiPL MBPPP, Olmo 3-Eval Code, ARC MC, MMLU STEM, MedMCQA MC, MedQA MC, SciQ MC, Olmo 3-Eval MC_STEM, MMLU Humanities, MMLU Social Sci., MMLU Other, CSQA MC, PIQA MC, SocialIQA MC, CoQA Gen2MC MC, DROP Gen2MC MC, Jeopardy Gen2MC MC, NaturalQs Gen2MC MC, SQuAD Gen2MC MC, Olmo 3-Eval MC_Non-STEM, HellaSwag RC, Winogrande RC, Lambada, Basic Skills, DROP, Jeopardy, NaturalQs, SQuAD, CoQA, Olmo 3-Eval GenQA, BBH, MMLU Pro MC, Deepmind Math, LBPP
Model	Olmo 3-Eval Math	BigCodeBench	HumanEval	DeepSeek LeetCode	DS 1000	MBPP	MultiPL HumanEval	MultiPL MBPPP	Olmo 3-Eval Code	ARC MC	MMLU STEM	MedMCQA MC	MedQA MC	SciQ MC	Olmo 3-Eval MC_STEM

Model Details

Stage 1: Initial Pretraining

Dataset: dolma3_mix-5.5T-1125
5.50T tokens
Coverage: 94.83%+ of total pretraining budget

Stage 2: Mid-training

Ingredient 1
- Dataset: dolma3-dolmino-mix-1125
- 100B tokens
- Mix composition: web pages, code, math/QA/thinking/instruction/PDFs
Ingredient 2
- Dataset: dolma3-dolmino-mix-1125
- 100B tokens
- Mix composition: web pages, code, math/QA/thinking/instruction/PDFs

Stage 3: Long Context

Dataset: dolma3-longmino-mix-1125
100B tokens
Mix composition: midtraining data and PDFs

Model Merging

7B Model: No merging
32B Model: 2 versions on 100B mix, merged before starting long context run. Final checkpoint is merged 4 final checkpoints.

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! Find the paper at: https://allenai.org/papers/olmo3

Model Card Contact

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

Model Details

Model Card for Olmo 3 32B

We introduce Olmo 3, a new family of 7B and 32B models. This suite includes Base, Instruct, and Think variants. The Base models were trained using a staged training approach.

Table with columns: Size, Training Tokens, Layers, Hidden Size, Q Heads, KV Heads, Context Length
Size	Training Tokens	Layers	Hidden Size	Q Heads	KV Heads	Context Length
OLMo 3 7B	5.93 Trillion	32	4096	32	32	65,536
OLMo 3 32B	5.50 Trillion	64	5120	40	8	65,536

The core models released in this batch include the following:

Table with columns: Stage, Olmo 3 7B Think, Olmo 3 32B Think, Olmo 3 7B Instruct
Stage	Olmo 3 7B Think	Olmo 3 32B Think	Olmo 3 7B Instruct
Base Model	Olmo-3-7B	Olmo-3-32B	Olmo-3-7B
SFT	Olmo-3-7B-Think-SFT	Olmo-3-32B-Think-SFT

Installation

Olmo 3 is supported in transformers v4.57.0 or higher:

bash
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-3-1125-32B")
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=0, temperature=1.0, top_p=0.7)
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')

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.

Fine-tune with the OLMo-core repository:

bash
torchrun --nproc-per-node=8 ./src/scripts/official/OLMo3/OLMo-3-1025-32B-pretrain.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/official/OLMo3/OLMo-3-1025-32B-pretrain.py run01 --train_module.optim.lr=6e-4

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 2024

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
W&B Report: https://wandb.ai/ai2-llm/Olmo-3-1125-32B/reports/Olmo-3-32B-November-2025--VmlldzoxNTA4NzAxMw
Paper: https://allenai.org/papers/olmo3

Evaluation

Core model results for MODELS are found below.

Table with columns: Model, Olmo 3-Eval Math, BigCodeBench, HumanEval, DeepSeek LeetCode, DS 1000, MBPP, MultiPL HumanEval, MultiPL MBPPP, Olmo 3-Eval Code, ARC MC, MMLU STEM, MedMCQA MC, MedQA MC, SciQ MC, Olmo 3-Eval MC_STEM, MMLU Humanities, MMLU Social Sci., MMLU Other, CSQA MC, PIQA MC, SocialIQA MC, CoQA Gen2MC MC, DROP Gen2MC MC, Jeopardy Gen2MC MC, NaturalQs Gen2MC MC, SQuAD Gen2MC MC, Olmo 3-Eval MC_Non-STEM, HellaSwag RC, Winogrande RC, Lambada, Basic Skills, DROP, Jeopardy, NaturalQs, SQuAD, CoQA, Olmo 3-Eval GenQA, BBH, MMLU Pro MC, Deepmind Math, LBPP
Model	Olmo 3-Eval Math	BigCodeBench	HumanEval	DeepSeek LeetCode	DS 1000	MBPP	MultiPL HumanEval	MultiPL MBPPP	Olmo 3-Eval Code	ARC MC	MMLU STEM	MedMCQA MC	MedQA MC	SciQ MC	Olmo 3-Eval MC_STEM

Model Details

Stage 1: Initial Pretraining

Dataset: dolma3_mix-5.5T-1125
5.50T tokens
Coverage: 94.83%+ of total pretraining budget

Stage 2: Mid-training

Ingredient 1
- Dataset: dolma3-dolmino-mix-1125
- 100B tokens
- Mix composition: web pages, code, math/QA/thinking/instruction/PDFs
Ingredient 2
- Dataset: dolma3-dolmino-mix-1125
- 100B tokens
- Mix composition: web pages, code, math/QA/thinking/instruction/PDFs

Stage 3: Long Context

Dataset: dolma3-longmino-mix-1125
100B tokens
Mix composition: midtraining data and PDFs

Model Merging

7B Model: No merging
32B Model: 2 versions on 100B mix, merged before starting long context run. Final checkpoint is merged 4 final checkpoints.

Bias, Risks, and Limitations

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! Find the paper at: https://allenai.org/papers/olmo3

Model Card Contact

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

Open-weight Models
Qwen-2.5-32B	64.7	48.1	65.6	8.0	43.3	69.8	49.7	53.6	48.3	97.0	79.7	68.8	68.4	97.1	82.2	85.0	88.4	81.2	89.9	93.3	86.6	96.8	86.6	97.0	79.9	97.9	89.3	86.3	87.5	76.2	94.2	53.7	74.0	39.3	64.9	40.4	68.5	81.1	61.1	40.7	40.3
Gemma-3-27B	63.2	44.0	62.1	5.8	34.3	60.0	37.7	47.2	41.6	95.8	74.9	64.7	68.7	96.8	80.2	80.5	86.2	80.2	79.0	90.3	81.2	95.8	84.6	95.9	82.0	97.7	86.7	86.0	91.3	77.5	94.9	75.9	82.1	49.2	92.4	12.4	73.5	77.4	53.1	30.4	17.7
Mistral-3.1-24B	59.5	46.4	65.5	0.1	36.3	61.9	39.0	47.7	42.4	96.2	70.1	68.8	70.4	96.3	81.5	82.7	88.6	81.9	80.5	91.0	81.0	94.9	86.5	97.2	84.6	97.9	87.9	86.2	90.8	79.3	91.9	74.9	80.3	45.1	92.6	61.1	78.0	81.4	58.9	35.3	30.3
Seed-36B	15.3	50.7	71.3	13.0	44.0	72.0	69.2	63.8	54.9	97.3	82.8	69.6	70.1	97.1	83.4	85.7	90.1	82.4	81.1	92.5	84.9	96.9	90.1	96.2	81.4	98.1	89.0	84.8	89.3	76.1	96.0	76.1	77.4	30.7	89.1	64.4	76.0	85.0	62.2	31.3	42.6
Gemma-2-27B	57.5	43.4	57.5	4.7	29.7	61.7	40.3	49.7	41.0	94.1	65.8	61.8	61.0	95.1	75.6	79.3	85.8	76.9	78.1	89.0	81.0	94.3	66.6	92.0	74.5	97.5	83.2	86.7	90.8	76.9	93.2	73.2	80.7	47.1	93.0	14.9	72.9	74.8	47.6	27.6	19.7
Llama-3.1-70B	62.0	43.4	57.4	0.2	29.5	55.5	32.2	35.9	36.3	95.2	70.0	67.8	72.3	95.4	80.1	83.4	87.4	79.4	79.0	91.5	83.5	95.1	70.3	97.1	82.4	97.7	86.1	88.4	91.7	79.6	92.4	78.3	84.0	53.1	92.9	73.9	81.6	80.8	50.4	40.3	11.8
Fully-open Models
Marin-32B	49.3	34.5	52.3	1.3	26.3	52.1	18.5	30.5	30.8	93.4	68.4	61.8	60.8	95.1	75.9	78.9	83.7	75.4	80.1	90.5	82.4	93.9	71.0	95.3	81.0	97.6	84.5	87.2	90.5	76.7	91.1	76.5	80.5	55.1	94.4	70.7	80.3	70.1	48.1	26.7	17.3
Apertus-70B	39.7	24.0	32.5	1.2	17.8	37.6	18.4	31.3	23.3	90.7	57.8	55.9	52.4	93.3	70.0	74.1	79.2	70.1	76.9	79.0	79.3	87.5	56.5	93.2	71.9	95.7	78.5	84.5	87.7	74.8	87.5	56.3	77.2	43.1	90.7	72.8	75.0	58.8	39.6	20.1	8.1
OLMo 2-32B	53.9	22.2	29.4	0.8	20.4	37.1	10.5	23.2	20.5	94.4	64.7	60.2	62.2	95.1	75.3	79.7	84.5	75.6	81.2	87.7	82.3	94.4	68.6	96.6	78.6	97.4	84.2	87.5	89.4	77.0	88.7	76.3	79.1	51.4	94.0	68.7	79.1	64.6	46.9	22.0	8.2
Olmo 3-32B	61.6	43.9	66.5	1.9	29.7	60.2	35.9	41.8	40.0	94.7	70.8	57.6	53.8	95.5	74.5	78.3	83.9	75.1	82.3	85.6	83.9	96.4	87.2	92.3	78.0	98.2	85.6	84.8	90.3	75.7	93.5	81.0	75.3	48.7	94.5	74.1	79.8	77.6	49.6	30.1	21.7

Olmo-3-1125-32B

README

Model Details

Model Card for Olmo 3 32B

Installation

Inference

Fine-tuning

Model Description

Model Sources

Evaluation

Model Details

Stage 1: Initial Pretraining

Stage 2: Mid-training

Stage 3: Long Context

Model Merging

Bias, Risks, and Limitations

License

Citation

Model Card Contact

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README

Model Details

Model Card for Olmo 3 32B

Installation

Inference

Fine-tuning

Model Description

Model Sources

Evaluation

Model Details

Stage 1: Initial Pretraining

Stage 2: Mid-training

Stage 3: Long Context

Model Merging

Bias, Risks, and Limitations

License

Citation

Model Card Contact