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transformers/docs/source/en/model_doc/olmo2.md
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transformers/docs/source/en/model_doc/olmo2.md
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<!--Copyright 2024 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. 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 distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and 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
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rendered properly in your Markdown viewer.
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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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<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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# OLMo2
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[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.
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You can find all the original OLMo2 checkpoints under the [OLMo2](https://huggingface.co/collections/allenai/olmo-2-674117b93ab84e98afc72edc) collection.
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> [!TIP]
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> Click on the OLMo2 models in the right sidebar for more examples of how to apply OLMo2 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/OLMo-2-0425-1B",
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dtype=torch.float16,
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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/OLMo-2-0425-1B"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"allenai/OLMo-2-0425-1B",
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dtype=torch.float16,
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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/OLMo-2-0425-1B --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/OLMo-2-0425-1B"
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)
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model = AutoModelForCausalLM.from_pretrained(
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"allenai/OLMo-2-0425-1B",
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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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## Notes
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- 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.
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- OLMo2 requires Transformers v4.48 or higher.
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- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`].
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```py
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained("allenai/OLMo-2-0425-1B", revision="stage1-step140000-tokens294B")
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```
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## Olmo2Config
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[[autodoc]] Olmo2Config
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## Olmo2Model
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[[autodoc]] Olmo2Model
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- forward
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## Olmo2ForCausalLM
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[[autodoc]] Olmo2ForCausalLM
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- forward
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