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+*This model was published in HF papers on 2022-10-19 and contributed to Hugging Face Transformers on 2022-12-05.*
+
+<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>
+
+# BioGPT
+
+[BioGPT](https://huggingface.co/papers/2210.10341) is a generative Transformer model based on [GPT-2](./gpt2) and pretrained on 15 million PubMed abstracts. It is designed for biomedical language tasks.
+
+You can find all the original BioGPT checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=biogpt) organization.
+
+> [!TIP]
+> Click on the BioGPT models in the right sidebar for more examples of how to apply BioGPT to different language tasks.
+
+The example below demonstrates how to generate biomedical text with [`Pipeline`], [`AutoModel`], and also from the command line.
+
+<hfoptions id="usage">
+<hfoption id="Pipeline">
+
+```python
+from transformers import pipeline
+
+
+generator = pipeline(
+    task="text-generation",
+    model="microsoft/biogpt",
+    device=0,
+)
+result = generator("Ibuprofen is best used for", truncation=True, max_length=50, do_sample=True)[0]["generated_text"]
+print(result)
+```
+
+</hfoption>
+<hfoption id="AutoModel">
+
+```python
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/biogpt")
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/biogpt",
+    device_map="auto",
+    attn_implementation="sdpa"
+)
+
+input_text = "Ibuprofen is best used for"
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+
+with torch.no_grad():
+    generated_ids = model.generate(**inputs, max_length=50)
+
+output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(output)
+```
+
+</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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bit precision.
+
+```python
+import torch
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bit_use_double_quant=True
+)
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large")
+model = AutoModelForCausalLM.from_pretrained(
+    "microsoft/BioGPT-Large",
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+
+input_text = "Ibuprofen is best used for"
+inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
+with torch.no_grad():
+    generated_ids = model.generate(**inputs, max_length=50)
+output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+print(output)
+```
+
+## Notes
+
+- Pad inputs on the right because BioGPT uses absolute position embeddings.
+- BioGPT can reuse previously computed key-value attention pairs. Access this feature with the [past_key_values](https://huggingface.co/docs/transformers/main/en/model_doc/biogpt#transformers.BioGptModel.forward.past_key_values) parameter in [`BioGPTModel.forward`].
+
+## BioGptConfig
+
+[[autodoc]] BioGptConfig
+
+## BioGptTokenizer
+
+[[autodoc]] BioGptTokenizer
+    - save_vocabulary
+
+## BioGptModel
+
+[[autodoc]] BioGptModel
+    - forward
+
+## BioGptForCausalLM
+
+[[autodoc]] BioGptForCausalLM
+    - forward
+
+## BioGptForTokenClassification
+
+[[autodoc]] BioGptForTokenClassification
+    - forward
+
+## BioGptForSequenceClassification
+
+[[autodoc]] BioGptForSequenceClassification
+    - forward