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+<!--Copyright 2024 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 2024-12-18 and contributed to Hugging Face Transformers on 2025-07-15.*
+
+<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>
+
+# ModernBERT Decoder
+
+ModernBERT Decoder has the same architecture as [ModernBERT](https://huggingface.co/papers/2412.13663) but it is trained from scratch with a causal language modeling objective from the [Ettin paper](https://huggingface.co/papers/2507.11412). This allows for using the same architecture to compare encoders and decoders. This model is the decoder architecture implementation of ModernBERT, designed for autoregressive text generation tasks.
+
+ModernBERT Decoder uses sliding window attention and rotary positional embeddings for efficiency and to handle longer sequences.
+
+You can find all the original ModernBERT Decoder checkpoints under the [jhu-clsp](https://huggingface.co/collections/jhu-clsp/encoders-vs-decoders-the-ettin-suite-686303e16142257eed8e6aeb) collection.
+
+> [!TIP]
+> This model was contributed by [orionw](https://huggingface.co/orionweller).
+>
+> Click on the ModernBERT Decoder models in the right sidebar for more examples of how to apply ModernBERT Decoder to different text generation tasks.
+
+The example below demonstrates how to use ModernBERT Decoder for text generation with [`Pipeline`], [`AutoModel`] (with and without quantization), and from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+generator = pipeline(
+    task="text-generation",
+    model="jhu-clsp/ettin-decoder-17m",
+    device=0
+)
+generator("The future of artificial intelligence is", max_length=50, num_return_sequences=1)
+
+# For sequence classification
+classifier = pipeline(
+    task="text-classification",
+    model="jhu-clsp/ettin-decoder-17m",
+    device=0
+)
+classifier("This movie is really great!")
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-17m")
+model = AutoModelForCausalLM.from_pretrained(
+    "jhu-clsp/ettin-decoder-17m",
+    device_map="auto",
+)
+
+prompt = "The future of artificial intelligence is"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=50,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+
+# For sequence classification
+from transformers import AutoModelForSequenceClassification
+
+
+classifier_model = AutoModelForSequenceClassification.from_pretrained(
+    "jhu-clsp/ettin-decoder-17m",
+    device_map="auto",
+    num_labels=2
+)
+
+text = "This movie is really great!"
+inputs = tokenizer(text, return_tensors="pt").to(classifier_model.device)
+
+with torch.no_grad():
+    outputs = classifier_model(**inputs)
+    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+    predicted_class = torch.argmax(predictions, dim=-1)
+
+print(f"Predicted class: {predicted_class.item()}")
+print(f"Prediction probabilities: {predictions}")
+```
+
+</hfoption>
+
+<hfoption id="AutoModel (w/quantization)">
+
+```python
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+
+quantization_config = BitsAndBytesConfig(
+    load_in_8bit=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("jhu-clsp/ettin-decoder-1b")
+model = AutoModelForCausalLM.from_pretrained(
+    "jhu-clsp/ettin-decoder-1b",
+    device_map="auto",
+    quantization_config=quantization_config
+)
+
+prompt = "The future of artificial intelligence is"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_length=50,
+        num_return_sequences=1,
+        temperature=0.7,
+        do_sample=True,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(f"Generated text: {generated_text}")
+```
+
+</hfoption>
+</hfoptions>
+
+## ModernBertDecoderConfig
+
+[[autodoc]] ModernBertDecoderConfig
+
+## ModernBertDecoderModel
+
+[[autodoc]] ModernBertDecoderModel
+    - forward
+
+## ModernBertDecoderForCausalLM
+
+[[autodoc]] ModernBertDecoderForCausalLM
+    - forward
+
+## ModernBertDecoderForSequenceClassification
+
+[[autodoc]] ModernBertDecoderForSequenceClassification
+    - forward