This model was contributed to Hugging Face Transformers on 2020-11-16.
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
.
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
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.
Set use_kernels=True in [~PreTrainedModel.from_pretrained] to replace supported layers with optimized kernels from the Hub. Refer to
to learn more.
The example below demonstrates how to generate text with [Pipeline] or the [AutoModel], and from the command line.
fromtransformersimportpipelinepipeline=pipeline(task="text-generation", model="openai-community/gpt2", device=0) pipeline("Hello, I'm a language model")fromtransformersimportAutoModelForCausalLM, AutoTokenizermodel=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 transformersQuantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the
overview for more available quantization backends.
The example below uses
to only quantize the weights to 4-bits.
fromtransformersimportAutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfigquantization_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 [~GPT2Model.forward#past_key_values] parameter in [GPT2Model.forward].
Enable the [~GPT2Config#scale_attn_by_inverse_layer_idx] and [~GPT2Config#reorder_and_upcast_attn] parameters to apply the training stability improvements from
.
GPT2Config
[[autodoc]] GPT2Config
GPT2Tokenizer
[[autodoc]] GPT2Tokenizer - save_vocabulary
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