gpt2

Introduction

GPT-2 is a transformer model developed by OpenAI, designed for text generation. It is trained on a large corpus of English data using a causal language modeling (CLM) objective. This model is capable of generating coherent and contextually relevant text based on a given prompt. GPT-2 has several versions, with the smallest containing 124 million parameters.

Architecture

GPT-2 uses a transformer architecture with a self-supervised training approach. The model predicts the next word in a sentence by using a mask mechanism, ensuring predictions for a token only use previous inputs. This architecture allows GPT-2 to develop a deep understanding of the English language, which can be applied to various text generation tasks.

Training

GPT-2 was trained using WebText, a dataset curated by scraping web pages from Reddit links with high karma. The dataset, excluding Wikipedia pages, totals 40GB of text. The model uses byte-level Byte Pair Encoding (BPE) with a vocabulary size of 50,257 and processes inputs as sequences of 1,024 tokens. The larger model was trained on 256 cloud TPU v3 cores.

Guide: Running Locally

To run GPT-2 locally, follow these steps:

1. Install the Transformers library:

   pip install transformers
   

2. Set up the model and tokenizer:

   from transformers import GPT2Tokenizer, GPT2Model
   tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
   model = GPT2Model.from_pretrained('gpt2')
   

3. Tokenize input text:

   text = "Replace me by any text you'd like."
   encoded_input = tokenizer(text, return_tensors='pt')
   

4. Run the model:

   output = model(**encoded_input)
   

For text generation, use the pipeline:

from transformers import pipeline, set_seed
generator = pipeline('text-generation', model='gpt2')
set_seed(42)
output = generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

Cloud GPUs: Consider using cloud GPU services like AWS, Google Cloud, or Azure for faster performance, especially for larger models.

License

GPT-2 is released under the MIT license, allowing for free use, modification, and distribution of the model and its associated code.