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* transformers-cli -> transformers * Chat command works with positional argument * update doc references to transformers-cli * doc headers * deepspeed --------- Co-authored-by: Joao Gante <joao@huggingface.co>
137 lines
4.1 KiB
Markdown
137 lines
4.1 KiB
Markdown
<!--Copyright 2020 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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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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-->
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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="TensorFlow" src="https://img.shields.io/badge/TensorFlow-FF6F00?style=flat&logo=tensorflow&logoColor=white">
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<img alt="Flax" src="https://img.shields.io/badge/Flax-29a79b.svg?style=flat&logo=data:image/png;base64,...">
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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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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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</div>
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</div>
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# GPT
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[GPT (Generative Pre-trained Transformer)](https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf) focuses on effectively learning text representations and transferring them to tasks. This model trains the Transformer decoder to predict the next word, and then fine-tuned on labeled data.
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GPT can generate high-quality text, making it well-suited for a variety of natural language understanding tasks such as textual entailment, question answering, semantic similarity, and document classification.
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You can find all the original GPT checkpoints under the [OpenAI community](https://huggingface.co/openai-community/openai-gpt) organization.
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> [!TIP]
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> Click on the GPT models in the right sidebar for more examples of how to apply GPT to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```python
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import torch
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from transformers import pipeline
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generator = pipeline(task="text-generation", model="openai-community/gpt", torch_dtype=torch.float16, device=0)
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output = generator("The future of AI is", max_length=50, do_sample=True)
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print(output[0]["generated_text"])
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```
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</hfoption>
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<hfoption id="AutoModel">
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt")
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model = AutoModelForCausalLM.from_pretrained("openai-community/openai-gpt", torch_dtype=torch.float16)
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inputs = tokenizer("The future of AI is", return_tensors="pt")
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outputs = model.generate(**inputs, max_length=50)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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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 "The future of AI is" | transformers run --task text-generation --model openai-community/openai-gpt --device 0
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```
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</hfoption>
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</hfoptions>
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## Notes
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- Inputs should be padded on the right because GPT uses absolute position embeddings.
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## OpenAIGPTConfig
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[[autodoc]] OpenAIGPTConfig
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## OpenAIGPTModel
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[[autodoc]] OpenAIGPTModel
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- forward
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## OpenAIGPTLMHeadModel
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[[autodoc]] OpenAIGPTLMHeadModel
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- forward
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## OpenAIGPTDoubleHeadsModel
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[[autodoc]] OpenAIGPTDoubleHeadsModel
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- forward
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## OpenAIGPTForSequenceClassification
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[[autodoc]] OpenAIGPTForSequenceClassification
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- forward
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## OpenAIGPTTokenizer
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[[autodoc]] OpenAIGPTTokenizer
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## OpenAIGPTTokenizerFast
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[[autodoc]] OpenAIGPTTokenizerFast
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## TFOpenAIGPTModel
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[[autodoc]] TFOpenAIGPTModel
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- call
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## TFOpenAIGPTLMHeadModel
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[[autodoc]] TFOpenAIGPTLMHeadModel
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- call
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## TFOpenAIGPTDoubleHeadsModel
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[[autodoc]] TFOpenAIGPTDoubleHeadsModel
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- call
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## TFOpenAIGPTForSequenceClassification
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[[autodoc]] TFOpenAIGPTForSequenceClassification
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- call
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