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+<!--Copyright 2020 The HuggingFace Team. All rights reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
+an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
+specific language governing permissions and limitations under the License.
+
+⚠️ 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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+*This model was published in HF papers on 2019-10-02 and contributed to Hugging Face Transformers on 2020-11-16.*
+
+<div style="float: right;">
+    <div class="flex flex-wrap space-x-1">
+        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+    </div>
+</div>
+
+# DistilBERT
+
+[DistilBERT](https://huggingface.co/papers/1910.01108) is pretrained by knowledge distillation to create a smaller model with faster inference and requires less compute to train. Through a triple loss objective during pretraining, language modeling loss, distillation loss, cosine-distance loss, DistilBERT demonstrates similar performance to a larger transformer language model.
+
+You can find all the original DistilBERT checkpoints under the [DistilBERT](https://huggingface.co/distilbert) organization.
+
+> [!TIP]
+> Click on the DistilBERT models in the right sidebar for more examples of how to apply DistilBERT to different language tasks.
+
+The example below demonstrates how to classify text with [`Pipeline`], [`AutoModel`], and from the command line.
+
+<hfoptions id="usage">
+
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+classifier = pipeline(
+    task="text-classification",
+    model="distilbert-base-uncased-finetuned-sst-2-english",
+    device=0
+)
+
+result = classifier("I love using Hugging Face Transformers!")
+print(result)
+# Output: [{'label': 'POSITIVE', 'score': 0.9998}]
+```
+
+</hfoption>
+
+<hfoption id="AutoModel">
+
+```python
+import torch
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+)
+model = AutoModelForSequenceClassification.from_pretrained(
+    "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+inputs = tokenizer("I love using Hugging Face Transformers!", return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model(**inputs)
+
+predicted_class_id = torch.argmax(outputs.logits, dim=-1).item()
+predicted_label = model.config.id2label[predicted_class_id]
+print(f"Predicted label: {predicted_label}")
+```
+
+</hfoption>
+
+</hfoptions>
+
+## Notes
+
+- DistilBERT doesn't have `token_type_ids`, you don't need to indicate which token belongs to which segment. Just
+  separate your segments with the separation token `tokenizer.sep_token` (or `[SEP]`).
+- DistilBERT doesn't have options to select the input positions (`position_ids` input). This could be added if
+  necessary though, just let us know if you need this option.
+
+## DistilBertConfig
+
+[[autodoc]] DistilBertConfig
+
+## DistilBertTokenizer
+
+[[autodoc]] DistilBertTokenizer
+
+## DistilBertTokenizerFast
+
+[[autodoc]] DistilBertTokenizerFast
+
+## DistilBertModel
+
+[[autodoc]] DistilBertModel
+    - forward
+
+## DistilBertForMaskedLM
+
+[[autodoc]] DistilBertForMaskedLM
+    - forward
+
+## DistilBertForSequenceClassification
+
+[[autodoc]] DistilBertForSequenceClassification
+    - forward
+
+## DistilBertForMultipleChoice
+
+[[autodoc]] DistilBertForMultipleChoice
+    - forward
+
+## DistilBertForTokenClassification
+
+[[autodoc]] DistilBertForTokenClassification
+    - forward
+
+## DistilBertForQuestionAnswering
+
+[[autodoc]] DistilBertForQuestionAnswering
+    - forward