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* Update Model card for gpt2 * Update link for gpt2 space * fixes docs based on suggestions * Add transformers-cli and quantization example for GPT-2 * Remove resources and flash attention docs and fix typos
207 lines
6.4 KiB
Markdown
207 lines
6.4 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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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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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="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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# GPT-2
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[GPT-2](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf) is a scaled up version of GPT, a causal transformer language model, with 10x more parameters and training data. The model was pretrained on a 40GB dataset to predict the next word in a sequence based on all the previous words. This approach enabled the model to perform many downstream tasks in a zero-shot setting.
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The model architecture uses a unidirectional (causal) attention mechanism where each token can only attend to previous tokens, making it particularly effective for text generation tasks.
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You can find all the original GPT-2 checkpoints under the [OpenAI community](https://huggingface.co/openai-community?search_models=gpt) organization.
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> [!TIP]
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> Click on the GPT-2 models in the right sidebar for more examples of how to apply GPT-2 to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`], and 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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pipeline = pipeline(task="text-generation", model="openai-community/gpt2", torch_dtype=torch.float16, device=0)
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pipeline("Hello, I'm a language model")
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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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model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", torch_dtype=torch.float16, device_map="auto", attn_implementation="sdpa")
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tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")
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input_ids = tokenzier("Hello, I'm a language model". return_tensors="pt").to("cuda")
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[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 "Hello, I'm a language model" | transformers-cli run --task text-generation --model openai-community/gpt2 --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-bits.
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline
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quantization_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="float16",
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bnb_4bit_use_double_quant=True
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)
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model = AutoModelForCausalLM.from_pretrained(
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"openai-community/gpt2-xl",
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quantization_config=quantization_config,
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-xl")
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inputs = tokenizer("Once upon a time, there was a magical forest", return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=100)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Notes
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- Pad inputs on the right because GPT-2 uses absolute position embeddings.
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- GPT-2 can reuse previously computed key-value attention pairs. Access this feature with the [past_key_values](https://huggingface.co/docs/transformers//en/model_doc/gpt2#transformers.GPT2Model.forward.past_key_values) parameter in [`GPT2Model.forward`].
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- Enable the [scale_attn_by_inverse_layer_idx](https://huggingface.co/docs/transformers/en/model_doc/gpt2#transformers.GPT2Config.scale_attn_by_inverse_layer_idx) and [reorder_and_upcast_attn](https://huggingface.co/docs/transformers/en/model_doc/gpt2#transformers.GPT2Config.reorder_and_upcast_attn) parameters to apply the training stability improvements from [Mistral](./mistral).
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## GPT2Config
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[[autodoc]] GPT2Config
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## GPT2Tokenizer
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[[autodoc]] GPT2Tokenizer
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- save_vocabulary
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## GPT2TokenizerFast
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[[autodoc]] GPT2TokenizerFast
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## GPT2 specific outputs
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[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput
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[[autodoc]] models.gpt2.modeling_tf_gpt2.TFGPT2DoubleHeadsModelOutput
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<frameworkcontent>
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<pt>
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## GPT2Model
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[[autodoc]] GPT2Model
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- forward
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## GPT2LMHeadModel
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[[autodoc]] GPT2LMHeadModel
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- forward
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## GPT2DoubleHeadsModel
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[[autodoc]] GPT2DoubleHeadsModel
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- forward
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## GPT2ForQuestionAnswering
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[[autodoc]] GPT2ForQuestionAnswering
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- forward
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## GPT2ForSequenceClassification
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[[autodoc]] GPT2ForSequenceClassification
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- forward
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## GPT2ForTokenClassification
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[[autodoc]] GPT2ForTokenClassification
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- forward
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</pt>
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<tf>
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## TFGPT2Model
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[[autodoc]] TFGPT2Model
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- call
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## TFGPT2LMHeadModel
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[[autodoc]] TFGPT2LMHeadModel
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- call
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## TFGPT2DoubleHeadsModel
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[[autodoc]] TFGPT2DoubleHeadsModel
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- call
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## TFGPT2ForSequenceClassification
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[[autodoc]] TFGPT2ForSequenceClassification
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- call
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## TFSequenceClassifierOutputWithPast
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[[autodoc]] modeling_tf_outputs.TFSequenceClassifierOutputWithPast
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## TFGPT2Tokenizer
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[[autodoc]] TFGPT2Tokenizer
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</tf>
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<jax>
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## FlaxGPT2Model
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[[autodoc]] FlaxGPT2Model
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- __call__
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## FlaxGPT2LMHeadModel
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[[autodoc]] FlaxGPT2LMHeadModel
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- __call__
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</jax>
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</frameworkcontent>
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