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OLMo2

docs/source/en/model_doc/olmo2.md

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This model was released on 2024-12-31 and added to Hugging Face Transformers on 2024-11-25.

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OLMo2

OLMo2 improves on 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, a dataset of 3T tokens.

You can find all the original OLMo2 checkpoints under the OLMo2 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">
python
from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="allenai/OLMo-2-0425-1B",
    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/OLMo-2-0425-1B"
)

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

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

  • 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", device_map="auto")

Olmo2Config

[[autodoc]] Olmo2Config

Olmo2Model

[[autodoc]] Olmo2Model - forward

Olmo2ForCausalLM

[[autodoc]] Olmo2ForCausalLM - forward

Olmo2ForSequenceClassification

[[autodoc]] Olmo2ForSequenceClassification - forward