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OLMo3

docs/source/en/model_doc/olmo3.md

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This model was released on {release_date} and added to Hugging Face Transformers on 2025-09-16.

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OLMo3

Olmo3 is an improvement on OLMo2. More details will be released on soon.

[!TIP] Click on the OLMo3 models in the right sidebar for more examples of how to apply OLMo3 to different language tasks.

The example below demonstrates how to generate text with [Pipeline], [AutoModel] and from the command line.

<hfoptions id="usage"> <hfoption id="Pipeline">
python
from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="allenai/TBA",
    device=0,
)

result = pipe("Plants create energy through a process known as")
print(result)
</hfoption> <hfoption id="AutoModel">
python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained(
    "allenai/TBA"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/TBA",
    device_map="auto",
    attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
</hfoption> </hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses torchao to only quantize the weights to 4-bits.

python
#pip install torchao
from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig


torchao_config = TorchAoConfig(
    "int4_weight_only",
    group_size=128
)

tokenizer = AutoTokenizer.from_pretrained(
    "allenai/TBA"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/TBA",
    quantization_config=torchao_config,
    device_map="auto",
    attn_implementation="sdpa"
)
input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_length=50, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))

Notes

  • Load specific intermediate checkpoints by adding the revision parameter to [~PreTrainedModel.from_pretrained].

    py
    from transformers import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained("allenai/TBA", revision="stage1-step140000-tokens294B", device_map="auto")

Olmo3Config

[[autodoc]] Olmo3Config

Olmo3ForCausalLM

[[autodoc]] Olmo3ForCausalLM

Olmo3ForSequenceClassification

[[autodoc]] Olmo3ForSequenceClassification - forward

Olmo3Model

[[autodoc]] Olmo3Model - forward

Olmo3PreTrainedModel

[[autodoc]] Olmo3PreTrainedModel - forward