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:
You can use OLMo with the standard HuggingFace transformers library:
from transformers import AutoModelForCausalLM, AutoTokenizer
olmo = AutoModelForCausalLM.from_pretrained("allenai/Olmo-3.1-32B-Think")
tokenizer = AutoTokenizer.from_pretrained("allenai/Olmo-3.1-32B-Think")
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])
>> '<think>Okay, so the question is who would win in a fight...'
For faster performance, you can quantize the model using the following method:
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:
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|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.<|im_end|>
<|endoftext|>
reinforcement learning from verifiable rewards on the Dolci-Think-RL-7B dataset. This dataset consits of math, code, instruction-following, and general chat queries.
from vllm import LLM, SamplingParams
model_id = "allenai/Olmo-3.1-32B-Think"
llm = LLM(model=model_id)
sampling_params = SamplingParams(
temperature=0.6,
top_p=0.95,
max_tokens=32768,
)
prompt = "Who would win in a fight - a dinosaur or a cow named MooMoo?"
outputs = llm.generate(prompt, sampling_params)
print(outputs[0].outputs[0].text)
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.
unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.olmoSubject: 
