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+    http://www.apache.org/licenses/LICENSE-2.0
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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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+*This model was contributed to Hugging Face Transformers on 2025-09-16.*
+
+<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">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+    </div>
+</div>
+
+# OLMo3
+
+Olmo3 is an improvement on [OLMo2](./olmo2). More details will be released on *soon*.
+
+> [!TIP]
+> Click on the OLMo3 models in the right sidebar for more examples of how to apply OLMo3 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/TBA",
+    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/TBA"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "allenai/TBA",
+    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 [torchao](../quantization/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/TBA"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "allenai/TBA",
+    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
+
+- Load specific intermediate checkpoints by adding the `revision` parameter to [`~PreTrainedModel.from_pretrained`].
+
+    ```py
+    from transformers import AutoModelForCausalLM
+
+    model = AutoModelForCausalLM.from_pretrained("allenai/TBA", revision="stage1-step140000-tokens294B", device_map="auto")
+    ```
+
+## Olmo3Config
+
+[[autodoc]] Olmo3Config
+
+## Olmo3ForCausalLM
+
+[[autodoc]] Olmo3ForCausalLM
+
+## Olmo3ForSequenceClassification
+
+[[autodoc]] Olmo3ForSequenceClassification
+    - forward
+
+## Olmo3Model
+
+[[autodoc]] Olmo3Model
+    - forward
+
+## Olmo3PreTrainedModel
+
+[[autodoc]] Olmo3PreTrainedModel
+    - forward