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transformers/docs/source/en/model_doc/biogpt.md
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transformers/docs/source/en/model_doc/biogpt.md
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<!--Copyright 2022 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 2022-10-19 and added to Hugging Face Transformers on 2022-12-05.*
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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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# BioGPT
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[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.
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You can find all the original BioGPT checkpoints under the [Microsoft](https://huggingface.co/microsoft?search_models=biogpt) organization.
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> [!TIP]
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> Click on the BioGPT models in the right sidebar for more examples of how to apply BioGPT to different language tasks.
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The example below demonstrates how to generate biomedical text with [`Pipeline`], [`AutoModel`], and also 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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generator = pipeline(
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task="text-generation",
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model="microsoft/biogpt",
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dtype=torch.float16,
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device=0,
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)
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result = generator("Ibuprofen is best used for", truncation=True, max_length=50, do_sample=True)[0]["generated_text"]
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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("microsoft/biogpt")
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/biogpt",
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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_text = "Ibuprofen is best used for"
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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generated_ids = model.generate(**inputs, max_length=50)
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output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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print(output)
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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 "Ibuprofen is best used for" | transformers run --task text-generation --model microsoft/biogpt --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 [bitsandbytes](../quantization/bitsandbytes) to only quantize the weights to 4-bit precision.
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```py
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.bfloat16,
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bnb_4bit_use_double_quant=True
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)
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tokenizer = AutoTokenizer.from_pretrained("microsoft/BioGPT-Large")
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/BioGPT-Large",
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quantization_config=bnb_config,
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dtype=torch.bfloat16,
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device_map="auto"
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)
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input_text = "Ibuprofen is best used for"
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inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
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with torch.no_grad():
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generated_ids = model.generate(**inputs, max_length=50)
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output = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
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print(output)
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```
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## Notes
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- Pad inputs on the right because BioGPT uses absolute position embeddings.
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- 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`].
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- The `head_mask` argument is ignored when using an attention implementation other than "eager". If you want to use `head_mask`, make sure `attn_implementation="eager"`).
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```py
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from transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(
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"microsoft/biogpt",
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attn_implementation="eager"
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)
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## BioGptConfig
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[[autodoc]] BioGptConfig
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## BioGptTokenizer
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[[autodoc]] BioGptTokenizer
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- save_vocabulary
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## BioGptModel
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[[autodoc]] BioGptModel
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- forward
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## BioGptForCausalLM
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[[autodoc]] BioGptForCausalLM
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- forward
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## BioGptForTokenClassification
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[[autodoc]] BioGptForTokenClassification
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- forward
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## BioGptForSequenceClassification
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[[autodoc]] BioGptForSequenceClassification
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- forward
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