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GPT-2 - AI Language Models Tool

Overview

GPT-2 is a pretrained, decoder‑only transformer for generative text tasks that OpenAI released in 2019. It was trained with a causal language‑modeling objective on WebText — a roughly 40GB corpus of internet text built from outbound Reddit links — and showed strong zero‑shot and few‑shot capabilities for generation, summarization, and simple question answering. ([openai.com](https://openai.com/index/better-language-models/?utm_source=openai)) The model family includes multiple checkpoints sized for different compute budgets (the original lineup ranges from the ~117M parameter “small” checkpoint up to a ~1.5B parameter XL checkpoint). GPT-2 is widely used as a research baseline, for fine‑tuning on downstream tasks, and for experimentation with sequence generation via the Hugging Face Transformers ecosystem and the original OpenAI code and datasets. It is distributed under an MIT license and commonly run via the Hugging Face model hub and Transformers pipelines. ([paperswithcode.com](https://paperswithcode.com/paper/language-models-are-unsupervised-multitask?utm_source=openai))

Model Statistics

  • Downloads: 9,925,065
  • Likes: 3118
  • Pipeline: text-generation
  • Parameters: 137.0M

License: mit

Model Details

Architecture and variants: GPT-2 is a decoder‑only Transformer (causal self‑attention) with byte‑level BPE tokenization, a vocabulary of 50,257 tokens, and a maximum context length of 1,024 tokens. Common public variants include: Small (~117M params, 12 layers), Medium (~345M, 24 layers), Large (~762M, 36 layers), and XL (~1.5B, 48 layers). These variant specifications are the original OpenAI configurations used to scale capacity. ([huggingface.co](https://huggingface.co/docs/transformers/en/model_doc/gpt2?utm_source=openai)) Pretraining and data: OpenAI trained GPT‑2 on WebText — assembled by following outbound Reddit links (minimum karma threshold) to collect high‑signal web pages — producing a ~40GB training corpus. The OpenAI team released model checkpoints and an outputs dataset to support research into detection, bias, and generation quality. Training used large TPU clusters for the bigger checkpoints; exact wall‑clock training duration was not disclosed. ([huggingface.co](https://huggingface.co/openai-community/gpt2?utm_source=openai)) Capabilities and limitations: GPT‑2 performs high‑quality next‑token generation from prompts, can be fine‑tuned for domain tasks, and exhibits useful zero‑shot behaviors on many language tasks. It is not instruction‑tuned (no RLHF) and therefore can produce plausible but incorrect or biased outputs; deployers should apply safety filtering and bias audits for human‑facing applications. Hugging Face provides PyTorch/TF checkpoints, ONNX exports, and integration helpers (pipelines, tokenizer classes, and past_key_values support for faster decoding). ([huggingface.co](https://huggingface.co/docs/transformers/en/model_doc/gpt2?utm_source=openai))

Key Features

  • Decoder‑only Transformer engineered for high‑quality next‑token generation.
  • Byte‑level BPE tokenizer with a 50,257 token vocabulary and 1,024 token context.
  • Multiple public checkpoints from ~117M to ~1.5B parameters for different budgets.
  • Works out‑of‑the‑box with Hugging Face Transformers pipelines for generation.
  • Open weights and code under an MIT license — suitable for research and fine‑tuning.
  • Exportable to ONNX and used widely as a baseline in LLM research and demos.

Example Usage

Example (python):

from transformers import pipeline, set_seed

generator = pipeline('text-generation', model='openai-community/gpt2')
set_seed(42)

# simple generation
result = generator("Write a short product description for a reusable water bottle:", max_length=60, num_return_sequences=1)
print(result[0]['generated_text'])

# lower-level: use tokenizer + model for fine-tuning or computing loss
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('openai-community/gpt2')
model = AutoModelForCausalLM.from_pretrained('openai-community/gpt2')
inputs = tokenizer("The quick brown fox", return_tensors='pt')
outputs = model(**inputs, labels=inputs['input_ids'])
loss = outputs.loss
print('Loss:', loss.item())

Pricing

No commercial pricing. GPT-2 model checkpoints and code are freely available under an MIT license (open-source).

Benchmarks

LAMBADA perplexity (GPT-2 small): 35.13 PPL (Source: https://huggingface.co/openai-community/gpt2)

LAMBADA accuracy (zero-shot): 45.99% ACC (Source: https://huggingface.co/openai-community/gpt2)

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

CBT Common Nouns accuracy (zero-shot): 87.65% ACC (Source: https://huggingface.co/openai-community/gpt2)

Last Refreshed: 2026-03-03

Key Information

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