transformers/docs/source/en/model_doc/olmo2.md

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# OLMo2
[OLMo2](https://huggingface.co/papers/2501.00656) improves on [OLMo](./olmo) by changing the architecture and training recipes of the original models. This includes excluding all biases to improve training stability, non-parametric layer norm, SwiGLU activation function, rotary positional embeddings, and a modified BPE-based tokenizer that masks personal identifiable information. It is pretrained on [Dolma](https://huggingface.co/datasets/allenai/dolma), a dataset of 3T tokens.

You can find all the original OLMo2 checkpoints under the [OLMo2](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc) collection.

> [!TIP]
> Click on the OLMo2 models in the right sidebar for more examples of how to apply OLMo2 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/OLMo-2-0425-1B",
    torch_dtype=torch.float16,
    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/OLMo-2-0425-1B"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/OLMo-2-0425-1B",
    torch_dtype=torch.float16,
    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>
<hfoption id="transformers CLI">

```bash
echo -e "Plants create energy through a process known as" | transformers-cli run --task text-generation --model allenai/OLMo-2-0425-1B --device 0
```

</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/OLMo-2-0425-1B"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/OLMo-2-0425-1B",
    quantization_config=torchao_config,
    torch_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

- OLMo2 uses RMSNorm instead of standard layer norm. The RMSNorm is applied to attention queries and keys, and it is applied after the attention and feedforward layers rather than before.
- OLMo2 requires Transformers v4.48 or higher.
- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`]. 

    ```py
    from transformers import AutoModelForCausalLM
    
    model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")
    ```


## Olmo2Config

[[autodoc]] Olmo2Config

## Olmo2Model

[[autodoc]] Olmo2Model
    - forward

## Olmo2ForCausalLM

[[autodoc]] Olmo2ForCausalLM
    - forward