Model reference · open weights
Olmo-3-Think-DPO is an open-weight language model from allenai. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.
Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.
What it is
| Released by | allenai |
|---|---|
| Type | Language models |
| Task | Text gen |
| Parameters (lead) | 7.3B |
| Context | 64k tokens |
| Runs with | transformers |
| Based on | allenai/Olmo-3-7B-Think-SFT |
| Released | 2025-11-18 |
| Popularity | 7k downloads / month |
| Licence | Open weights |
About
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 |
Olmo 3 is supported in transformers 4.57.0 or higher:
pip install transformers>=4.57.0
You can use OLMo with the standard HuggingFace transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3-7B-Think-DPO")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3-7B-Think-DPO")
message = ["Who would win in a fight - a dinosaur or a cow named Moo Moo?"]
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])
>> '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-3-7B-Think-DPO",
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')
The default system prompt for this model is:
You are Olmo, a helpful AI assistant built by Ai2. Your date cutoff is December 2024, and your model weights are available at https://huggingface.co/allenai.
The chat template for this model is formatted as:
You are Olmo, a helpful AI assistant built by Ai2. Your date cutoff is December 2024, and your model weights are available at https://huggingface.co/allenai.
Who would win in a fight - a dinosaur or a cow named Moo Moo?
Hmm, first I need to break this down. Let me think about the different factors involved here.....
Moo Moo the cow would certinaly win.
olmo@allenai.org. Press: press@allenai.org| Skill | Benchmark | Olmo 3 Think 7B SFT | Olmo 3 Think 7B DPO | Olmo 3 Think 7B | OpenThinker3-7B | Nemotron-Nano-9B-v2 | DeepSeek-R1-Distill-Qwen-7B | Qwen 3 8B (reasoning) | Qwen 3 VL 8B Thinker | OpenReasoning Nemotron 7B |
|---|---|---|---|---|---|---|---|---|---|---|
| Math | MATH | 94.4 | 92.4 | 95.1 | 94.5 | 94.4 | 87.9 | 95.1 | 95.2 | 94.6 |
| AIME 2024 | 69.6 | 74.6 | 71.6 | 67.7 | 72.1 | 54.9 | 74.0 | 70.9 | 77.0 | |
| AIME 2025 | 57.6 | 62.7 | 64.6 | 57.2 | 58.9 | 40.2 | 67.8 | 61.5 | 73.1 | |
| OMEGA | 45.0 | 40.5 | 37.8 | 38.4 | 42.4 | 28.5 | 43.4 | 38.1 | 43.2 | |
| Reasoning | BBH | 84.1 | 83.7 | 86.6 | 77.1 | 86.2 | 73.5 | 84.4 | 86.8 | 81.3 |
| ZebraLogic | 57.9 | 60.6 | 66.5 | 34.9 | 60.8 | 26.1 | 85.2 | 91.2 | 22.4 | |
| AGI Eval | 77.2 | 79.1 | 81.5 | 78.6 | 83.1 | 69.5 | 87.0 | 90.1 | 81.4 | |
| Coding | HumanEval+ | 88.2 | 91.4 | 89.9 | 87.4 | 89.7 | 83.0 | 80.2 | 83.7 | 89.7 |
| MBPP+ | 63.2 | 63.0 | 64.7 | 61.4 | 66.1 | 63.5 | 69.1 | 63.0 | 61.2 | |
| LCB v3 | 67.8 | 75.1 | 75.2 | 68.0 | 83.4 | 58.8 | 86.2 | 85.5 | 82.3 | |
| IF | IFEval | 77.9 | 75.9 | 88.2 | 51.7 | 86.0 | 59.6 | 87.4 | 85.5 | 42.5 |
| IFBench | 30 |
From the published model card. Full card on the HuggingFace links in the sidebar.
Using it via the API
Once AxForge deploys olmo-3-think-dpo for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (olmo-3-think-dpo below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"olmo-3-think-dpo","messages":[{"role":"user","content":"Hello"}]}'
Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.