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transformers/docs/source/en/model_doc/flex_olmo.md
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transformers/docs/source/en/model_doc/flex_olmo.md
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<!--Copyright 2025 the HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be rendered properly in your Markdown viewer.
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-->
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*This model was released on 2025-07-09 and added to Hugging Face Transformers on 2025-09-18.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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</div>
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</div>
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# FlexOlmo
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[FlexOlmo](https://huggingface.co/papers/2507.07024) is a new class of language models (LMs) that supports (1) distributed training without data sharing, where different model parameters are independently trained on closed datasets, and (2) data-flexible inference, where these parameters along with their associated data can be flexibly included or excluded from model inferences with no further training. FlexOlmo employs a mixture-of-experts (MoE) architecture where each expert is trained independently on closed datasets and later integrated through a new domain-informed routing without any joint training. FlexOlmo is trained on FlexMix, a corpus we curate comprising publicly available datasets alongside seven domain-specific sets, representing realistic approximations of closed sets.
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You can find all the original FlexOlmo checkpoints under the [FlexOlmo](https://huggingface.co/collections/allenai/flexolmo-68471177a386b6e20a54c55f) collection.
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> [!TIP]
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> Click on the FlexOlmo models in the right sidebar for more examples of how to apply FlexOlmo to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`] and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipe = pipeline(
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task="text-generation",
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model="allenai/FlexOlmo-7x7B-1T",
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dtype=torch.bfloat16,
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device=0,
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)
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result = pipe("Plants create energy through a process known as")
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print(result)
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"allenai/FlexOlmo-7x7B-1T"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"allenai/FlexOlmo-7x7B-1T",
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dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids, max_length=50, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model allenai/FlexOlmo-7x7B-1T --device 0
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```
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</hfoption>
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</hfoptions>
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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.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to 4-bits.
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```py
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#pip install torchao
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, TorchAoConfig
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torchao_config = TorchAoConfig(
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"int4_weight_only",
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group_size=128
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"allenai/FlexOlmo-7x7B-1T"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"allenai/FlexOlmo-7x7B-1T",
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quantization_config=torchao_config,
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dtype=torch.bfloat16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids, max_length=50, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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## FlexOlmoConfig
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[[autodoc]] FlexOlmoConfig
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## FlexOlmoForCausalLM
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[[autodoc]] FlexOlmoForCausalLM
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## FlexOlmoModel
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[[autodoc]] FlexOlmoModel
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- forward
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## FlexOlmoPreTrainedModel
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[[autodoc]] FlexOlmoPreTrainedModel
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- forward
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