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*This model was released on 2025-07-09 and added to Hugging Face Transformers on 2025-10-07.*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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# FlexOlmo

[FlexOlmo](https://huggingface.co/papers/2507.07024) is a new mixture-of-experts (MoE) language model architecture designed to allow distributed training on closed datasets without sharing raw data, and flexible inference where experts can be selectively included or excluded. Each expert is trained independently on its own dataset and later integrated using a domain-informed routing mechanism, avoiding joint training. The model is trained on FlexMix, a corpus combining public data with seven domain-specific closed sets, and is evaluated at scales up to 37B parameters (20B active) across 31 tasks. Results show a 41% relative performance gain from combining public and private experts, a 10.1% improvement over prior model-merging methods, and even better performance than standard MoE baselines trained under unrestricted data access, providing a practical solution for regulated or sensitive data contexts.

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

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="allenai/FlexOlmo-7x7B-1T", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

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

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

tokenizer = AutoTokenizer.from_pretrained("allenai/FlexOlmo-7x7B-1T")

model = AutoModelForCausalLM.from_pretrained("allenai/FlexOlmo-7x7B-1T", dtype="auto",)
inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## FlexOlmoConfig

[[autodoc]] FlexOlmoConfig

## FlexOlmoForCausalLM

[[autodoc]] FlexOlmoForCausalLM

## FlexOlmoModel

[[autodoc]] FlexOlmoModel
    - forward

## FlexOlmoPreTrainedModel

[[autodoc]] FlexOlmoPreTrainedModel
    - forward
