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

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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# OLMo3

Olmo3 is an improvement on [OLMo2](./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">

```py
import torch
from transformers import pipeline

pipe = pipeline(
    task="text-generation",
    model="allenai/TBA",
    dtype=torch.bfloat16,
    device=0,
)

result = pipe("Plants create energy through a process known as")
print(result)
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

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

model = AutoModelForCausalLM.from_pretrained(
    "allenai/TBA",
    dtype=torch.bfloat16,
    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](../quantization/overview) overview for more available quantization backends.

The example below uses [torchao](../quantization/torchao) to only quantize the weights to 4-bits.

```py

#pip install torchao
import torch
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,
    dtype=torch.bfloat16,
    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")
    ```

## Olmo3Config

[[autodoc]] Olmo3Config

## Olmo3ForCausalLM

[[autodoc]] Olmo3ForCausalLM

## Olmo3Model

[[autodoc]] Olmo3Model
    - forward

## Olmo3PreTrainedModel

[[autodoc]] Olmo3PreTrainedModel
    - forward
