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GPT-2 - AI Language Models Tool

Overview

GPT-2 is a decoder-only, transformer-based language model family first released by OpenAI in February 2019. It was trained with a causal language modeling objective (predict the next token) on the WebText corpus and demonstrated strong zero-shot and prompt-based text generation capabilities for summarization, continuation, translation and other tasks. The original OpenAI release included multiple sizes — from the small 124M parameter checkpoint up to 1.5B parameters — enabling a trade-off between speed and generation quality. ([bibbase.org](https://bibbase.org/network/publication/radford-wu-child-luan-amodei-sutskever-languagemodelsareunsupervisedmultitasklearners-2019?utm_source=openai)) Today GPT-2 remains widely used as an accessible, open-weight baseline for research, fine-tuning experiments, and lightweight generation tasks. The Hugging Face-hosted community release (openai-community/gpt2) provides the pretrained checkpoints, tokenizer, model card, usage examples, and a large ecosystem of fine-tuned forks and quantized builds; the Hugging Face page also lists community metrics such as likes and monthly downloads showing continued adoption. Users choose GPT-2 for fast local inference, educational experiments, and as a starting point for task-specific fine-tuning, while being mindful of known limitations like factual hallucination and dataset bias. ([huggingface.co](https://huggingface.co/openai-community/gpt2))

Model Statistics

  • Downloads: 6,848,835
  • Likes: 3083
  • Pipeline: text-generation
  • Parameters: 137.0M

License: mit

Model Details

Architecture and variants: GPT-2 is a decoder-only Transformer available in multiple sized variants. The commonly distributed “gpt2” (small) checkpoint has ~124–127M parameters with 12 layers, 12 attention heads, and a 768-dimensional embedding; medium, large and XL variants scale these dimensions up (e.g., 355M, 774M, 1.5B parameters). All variants use absolute position embeddings and a 1024-token context window. ([renenyffenegger.ch](https://renenyffenegger.ch/notes/development/Artificial-intelligence/language-model/LLM/GPT/2?utm_source=openai)) Tokenizer and input format: GPT-2 uses a byte-level Byte-Pair Encoding (BPE) tokenizer with a vocabulary of 50,257 tokens and a special end-of-text token. Typical usage pads on the right and can leverage past_key_values for incremental generation to avoid re-computing attention states. ([huggingface.co](https://huggingface.co/openai-community/gpt2)) Training and license: GPT-2 was trained on WebText (web pages linked from Reddit posts with ≥3 karma, ~40GB of text) using self-supervised next-token prediction on TPU infrastructure. The community-distributed Hugging Face checkpoints are released under a permissive MIT-style license per the original repository. ([huggingface.co](https://huggingface.co/openai-community/gpt2)) Capabilities and limitations: GPT-2 produces fluent continuations and can be fine-tuned for classification or generation. It is prone to factual errors, repetition, and reproduces biases present in WebText; it is not designed for tasks that require guaranteed factual correctness or robust safety without additional mitigation. ([huggingface.co](https://huggingface.co/openai-community/gpt2))

Key Features

  • Multiple pretrained sizes from ~124M to 1.5B parameters for compute/quality trade-offs.
  • Byte-level BPE tokenizer with a 50,257 token vocabulary and 1024-token context.
  • Decoder-only transformer optimized for next-token (causal) generation.
  • Permissive MIT-style license and many community forks, quantized builds, and fine-tunes.
  • Well-supported by Hugging Face Transformers pipeline for easy inference and fine-tuning.

Example Usage

Example (python):

from transformers import pipeline, set_seed

# Load the community GPT-2 checkpoint hosted on Hugging Face
generator = pipeline('text-generation', model='openai-community/gpt2')
set_seed(42)

prompt = "In the future, researchers will"
outputs = generator(prompt, max_length=60, num_return_sequences=3, do_sample=True, top_k=50)

for i, out in enumerate(outputs):
    print(f"--- Sample {i+1} ---")
    print(out['generated_text'])

# Notes:
# - Replace 'openai-community/gpt2' with another GPT-2 variant (e.g., 'gpt2-medium') or a local path.
# - Use past_key_values for efficient incremental decoding in interactive settings.
# (See the Hugging Face model card and Transformers docs for more usage patterns.)

Benchmarks

LAMBADA accuracy (zero-shot): 45.99% (small variant reported in model card table) (Source: https://huggingface.co/openai-community/gpt2)

LAMBADA perplexity: 35.13 (reported) (Source: https://huggingface.co/openai-community/gpt2)

WikiText-2 perplexity: 29.41 (reported) (Source: https://huggingface.co/openai-community/gpt2)

Penn Treebank (PTB) perplexity: 65.85 (reported) (Source: https://huggingface.co/openai-community/gpt2)

Hugging Face community usage (downloads last month): 6,848,835 (downloads last month on HF model page at time of check) (Source: https://huggingface.co/openai-community/gpt2)

Last Refreshed: 2026-01-16

Key Information

  • Category: Language Models
  • Type: AI Language Models Tool
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