huggingface--transformers/docs/source/en/model_doc/olmo.md

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*This model was published in HF papers on 2024-02-01 and contributed to Hugging Face Transformers on 2024-04-17.*

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# OLMo

[OLMo](https://huggingface.co/papers/2402.00838) is a 7B-parameter dense language model. It uses SwiGLU activations, non-parametric layer normalization, rotary positional embeddings, and a BPE tokenizer that masks personally identifiable information. It is pretrained on [Dolma](https://huggingface.co/datasets/allenai/dolma), a 3T-token dataset. OLMo was released to provide complete transparency of not just the model weights but the training data, training code, and evaluation code to enable more research on language models.

You can find all the original OLMo checkpoints under the [OLMo](https://huggingface.co/collections/allenai/olmo-suite-65aeaae8fe5b6b2122b46778) collection.

> [!TIP]
> This model was contributed by [shanearora](https://huggingface.co/shanearora).
>
> Click on the OLMo models in the right sidebar for more examples of how to apply OLMo to different language tasks.

The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`] class.

<hfoptions id="usage">
<hfoption id="Pipeline">

```python
from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="allenai/OLMo-7B-hf",
    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-7B-hf"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/OLMo-7B-hf",
    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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bits.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained(
    "allenai/OLMo-7B-hf",
    attn_implementation="sdpa",
    device_map="auto",
    quantization_config=quantization_config
)

tokenizer = AutoTokenizer.from_pretrained("allenai/OLMo-7B-hf")

inputs = tokenizer("Bitcoin is", return_tensors="pt").to(model.device)
inputs = {k: v.to(model.device) for k, v in inputs.items()}

output = model.generate(**inputs, max_length=64)

print(tokenizer.decode(output[0]))
```

## OlmoConfig

[[autodoc]] OlmoConfig

## OlmoModel

[[autodoc]] OlmoModel
    - forward

## OlmoForCausalLM

[[autodoc]] OlmoForCausalLM
    - forward

## OlmoForSequenceClassification

[[autodoc]] OlmoForSequenceClassification
    - forward