---

tags:

• unsloth

license: apache-2.0 base_model:

• allenai/Olmo-3-32B-Think

language:

• en

---

Model Details

Model Card for Olmo 3 32B Think

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 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	Olmo-3-7B-Instruct-SFT
DPO	Olmo-3-7B-Think-DPO	Olmo-3-32B-Think-DPO	Olmo-3-7B-Instruct-DPO
Final Models (RLVR)	Olmo-3-7B-Think	Olmo-3-32B-Think	Olmo-3-7B-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:

from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-32B-Think")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-32B-Think")
message = ["Who would win in a fight - a dinosaur or a cow named Moo Moo?"]
inputs = tokenizer(message, returntensors='pt', returntokentypeids=False)
optional verifying cuda

inputs = {k: v.to('cuda') for k,v in inputs.items()}

olmo = olmo.to('cuda')

response = olmo.generate(inputs, maxnewtokens=100, dosample=True, topk=50, top_p=0.95)
print(tokenizer.batchdecode(response, skipspecial_tokens=True)[0])
>> '<think>Okay, so the question is who would win in a fight...'

For faster performance, you can quantize the model using the following method:

AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-32B-Think", 
    torch_dtype=torch.float16, 
    loadin8bit=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_XXX.

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

olmo = AutoModelForCausalLM.frompretrained("allenai/Olmo-3-32B-Think", revision="step300")

Or, you can access all the revisions for the models via the following code snippet:

from huggingfacehub import listrepo_refs
out = listreporefs("allenai/Olmo-3-32B-Think")
branches = [b.name for b in out.branches]

Chat template

Default System Message

The default system prompt for this model is:

<|im_start|>system
You are a helpful AI assistant.<|im_end|>

Chat Format

The chat template for this model is formatted as:

<|im_start|>system
You are a helpful AI assistant.
<|im_start|>user
Who would win in a fight - a dinosaur or a cow named Moo Moo?<|im_end|>
<|im_start|>assistant
<think>Okay, so the question is who would win in a fight between a dinosaur and a cow named Moo Moo.
Hmm, first I need to break this down. Let me think about the different factors involved here..... </think>
Moo Moo the cow would certinaly win.
<|endoftext|>

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: [email protected]. Press: [email protected]
• 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: [TBD]

Evaluation

Benchmark	Olmo 3 Think 32B SFT	Olmo 3 Think 32B DPO	Olmo 3 Think 32B	Qwen 3 32B	Qwen 3 VL 32B Thinking	Qwen 2.5 32B	Gemma 3 27B Instruct	Gemma 2 27B Instruct	Olmo 2 32B Instruct	DeepSeek-R1-Distill-Qwen-32B
Math
MATH	95.6	95.9	96.1	95.4	96.7	80.2	87.4	51.5	49.2	92.6
AIME 2024	73.5	76.0	76.8	80.8	86.3	15.7	28.9	4.7	4.6	70.3
AIME 2025	66.2	70.7	72.5	70.9	78.8	13.4	22.9	0.9	0.9	56.3
OMEGA	43.1	45.2	50.8	47.7	50.8	19.2	24.0	9.1	9.8	38.9
Reasoning
BigBenchHard	88.8	89.1	89.8	90.6	91.1	80.9	82.4	66.0	65.6	89.7
ZebraLogic	70.5	74.5	76.0	88.3	96.1	24.1	24.8	17.2	13.3	69.4
AGI Eval English	85.9	87.8	88.2	90.0	92.2	78.9	76.9	70.9	68.4	88.1
Coding
HumanEvalPlus	90.0	91.6	91.4	91.2	90.6	82.6	79.2	67.5	44.4	92.3
MBPP+	66.7	67.2	68.0	70.6	66.2	66.6	65.7	61.2	49.0	70.1
LiveCodeBench v3	75.8	81.9	83.5	90.2	84.8	49.9	39.0	28.7	10.6	79.5
IF
IFEval	83.9	80.6	89.0	86.5	85.5	81.9	85.4	62.1	85.8	78.7
IFBench	37.0	34.4	47.6	37.3	55.1	36.7	31.3	27.8	36.4	23.8
Knowledge & QA
MMLU	85.3	85.2	85.4	88.8	90.1	84.6	74.6	76.1	77.1	88.0
PopQA	33.1	37.0	31.9	30.7	32.2	28.0	30.2	30.4	37.2	26.7
GPQA	55.7	57.6	58.1	67.3	67.4	44.6	45.0	39.9	36.4	61.8
Chat
AlpacaEval 2 LC	69.1	78.6	74.2	75.6	80.9	81.9	65.5	39.8	38.0	26.2
Safety	64.8	65.3	68.8	69.0	82.7	81.9	68.6	74.3	83.8	63.6

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-7B

#### 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-7B

#### 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-7B

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 [email protected].