This model was released on 2019-02-14 and added to Hugging Face Transformers on 2020-11-16.

GPT-2

GPT-2 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. The blog post released by OpenAI can be found here.

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.

You can find all the original GPT-2 checkpoints under the OpenAI community organization.

[!TIP] Click on the GPT-2 models in the right sidebar for more examples of how to apply GPT-2 to different language tasks.

The example below demonstrates how to generate text with [Pipeline] or the [AutoModel], and from the command line.

from transformers import pipeline


pipeline = pipeline(task="text-generation", model="openai-community/gpt2", device=0)
pipeline("Hello, I'm a language model")

from transformers import AutoModelForCausalLM, AutoTokenizer


model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2", device_map="auto", attn_implementation="sdpa")
tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2")

input_ids = tokenizer("Hello, I'm a language model", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))

One can also serve the model using vLLM with the transformers backend.

vllm serve openai-community/gpt2 --model-imp transformers

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to only quantize the weights to 4-bits.

from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype="float16",
    bnb_4bit_use_double_quant=True
)

model = AutoModelForCausalLM.from_pretrained(
    "openai-community/gpt2-xl",
    quantization_config=quantization_config,
    device_map="auto"
)

tokenizer = AutoTokenizer.from_pretrained("openai-community/gpt2-xl")
inputs = tokenizer("Once upon a time, there was a magical forest", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Notes

• Pad inputs on the right because GPT-2 uses absolute position embeddings.
• GPT-2 can reuse previously computed key-value attention pairs. Access this feature with the past_key_values parameter in [GPT2Model.forward].
• Enable the scale_attn_by_inverse_layer_idx and reorder_and_upcast_attn parameters to apply the training stability improvements from Mistral.

GPT2Config

[[autodoc]] GPT2Config

GPT2Tokenizer

[[autodoc]] GPT2Tokenizer - save_vocabulary

GPT2TokenizerFast

[[autodoc]] GPT2TokenizerFast

GPT2 specific outputs

[[autodoc]] models.gpt2.modeling_gpt2.GPT2DoubleHeadsModelOutput

GPT2Model

[[autodoc]] GPT2Model - forward

GPT2LMHeadModel

[[autodoc]] GPT2LMHeadModel - forward

GPT2DoubleHeadsModel

[[autodoc]] GPT2DoubleHeadsModel - forward

GPT2ForQuestionAnswering

[[autodoc]] GPT2ForQuestionAnswering - forward

GPT2ForSequenceClassification

[[autodoc]] GPT2ForSequenceClassification - forward

GPT2ForTokenClassification

[[autodoc]] GPT2ForTokenClassification - forward