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OpenAI GPT 1 - AI Language Models Tool

Overview

OpenAI GPT-1 (often shortened to "GPT 1") is the original decoder-only transformer from OpenAI that demonstrated the practical value of unsupervised generative pre-training followed by supervised fine-tuning for many NLP tasks. Released with the paper "Improving Language Understanding by Generative Pre-Training" (June 2018), GPT-1 showed that a relatively compact autoregressive Transformer could learn transferable contextual representations from raw text and yield large absolute gains on benchmarks after task-specific fine-tuning (e.g., Story Cloze, RACE). (See the original paper: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.) The model is available as an open checkpoint on the Hugging Face Hub (model id openai-community/openai-gpt), distributed under an MIT license and provided in PyTorch and TensorFlow formats for local inference and fine-tuning. The Hugging Face model card includes usage examples, documented limitations (bias, safety), and technical notes about training compute and environmental impact. (Hugging Face model card: https://huggingface.co/openai-community/openai-gpt.)

Model Statistics

  • Downloads: 225,134
  • Likes: 286
  • Pipeline: text-generation
  • Parameters: 119.7M

License: mit

Model Details

Architecture and size: GPT-1 is a decoder-only (causal) Transformer with 12 layers, hidden dimension 768, 12 attention heads, a feed-forward inner size of 3072, and a 512-token context window — roughly 117 million parameters in the canonical configuration. Tokenization used Byte Pair Encoding with a ~40k vocabulary. (Original paper: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf.) Training: The model was pre-trained on the BooksCorpus dataset (≈7,000 unpublished books, long contiguous text) using a next-token (language modeling) objective and then fine-tuned on downstream discriminative tasks with task-aware input transformations. Optimization used Adam with a warmup-then-cosine schedule; reported total compute for training was ~0.96 petaflop-days. (Paper and Hugging Face model card: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf; https://huggingface.co/openai-community/openai-gpt.) Capabilities and limits: GPT-1 established the pretrain-then-finetune recipe and performs well after fine-tuning on classification, NLI, QA, and commonsense tasks, but it is small by modern standards and inherits known LLM issues (hallucination, social biases, safety risks). The checkpoint is distributed for research and practical local use (PyTorch/TensorFlow). (Hugging Face: https://huggingface.co/openai-community/openai-gpt.)

Key Features

  • Decoder-only (causal) Transformer with 12 layers and 512-token context window.
  • ≈117M parameters — small enough for local fine-tuning and experimentation.
  • Pretrained on BooksCorpus for long-range narrative coherence and context.
  • Demonstrated large absolute gains via pretrain-then-finetune on NLU tasks.
  • Open checkpoint under MIT license; provided in PyTorch and TensorFlow formats.

Example Usage

Example (python):

from transformers import pipeline, set_seed

# Use the Hugging Face checkpoint (openai-community/openai-gpt)
# Requires `transformers` and a compatible backend (PyTorch or TF)

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

prompt = "In the near future, researchers discovered that"
outputs = generator(prompt, max_length=60, num_return_sequences=2)
for i, out in enumerate(outputs):
    print(f"== Sample {i+1} ==\n{out['generated_text']}\n")

# See model card for tokenizer/model class usage examples: https://huggingface.co/openai-community/openai-gpt

Benchmarks

Parameters: ≈117 million (Source: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)

Story Cloze Test (absolute improvement): +8.9% (absolute) vs prior SOTA reported in paper (Source: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)

RACE (question-answering; absolute improvement): +5.7% (absolute) vs prior SOTA reported in paper (Source: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)

GLUE (reported overall improvement): Reported +5.5% on GLUE (per paper) (Source: https://cdn.openai.com/research-covers/language-unsupervised/language_understanding_paper.pdf)

Hugging Face distribution activity: Downloads last month: 225,134; Likes: 286 (Hugging Face model page) (Source: https://huggingface.co/openai-community/openai-gpt)

Last Refreshed: 2026-03-03

Key Information

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