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OpenAI GPT 1 - AI Language Models Tool

Overview

OpenAI GPT-1 (often written as GPT or "Generative Pre‑trained Transformer 1") is the first released transformer-based, decoder-only language model from OpenAI that demonstrated the effectiveness of unsupervised pre‑training followed by supervised fine‑tuning for natural language understanding. GPT-1 introduced the generative pre-training approach and showed sizable transfer gains across multiple benchmarks (for example, improvements on the Stories Cloze Test, RACE and MultiNLI), establishing a template later scaled by GPT-2 and subsequent families. ([paperswithcode.com](https://paperswithcode.com/paper/improving-language-understanding-by?utm_source=openai)) The model is available as open weights under an MIT license and is distributed on Hugging Face with ready-to-run checkpoints and examples for both PyTorch and TensorFlow via the Hugging Face Transformers library. The Hugging Face model card contains usage snippets, training / environmental notes (compute = 0.96 petaflop‑days reported), and risk/limitations guidance, and the community continues to use the model for lightweight research, fine‑tuning experiments, and as a teaching example for transformer-based language modeling. ([huggingface.co](https://huggingface.co/openai-community/openai-gpt?utm_source=openai))

Model Statistics

  • Downloads: 28,005
  • Likes: 284
  • Pipeline: text-generation
  • Parameters: 119.7M

License: mit

Model Details

Architecture and scale: GPT-1 is a decoder-only transformer (causal self-attention) trained as a language model. The canonical configuration uses 12 transformer layers (blocks), a hidden dimension of 768, 12 attention heads, and feed-forward inner dimension 3072, yielding roughly 117–120 million parameters. Maximum sequence length during training was 512 tokens and a byte-pair encoding (BPE) vocabulary with ~40,000 merges was used. ([huggingface.co](https://huggingface.co/openai-gpt?utm_source=openai)) Training and data: The model was pre-trained on the BooksCorpus dataset (a collection of ~7,000 unpublished books chosen for long contiguous text), using unsupervised language modeling, followed by task-specific supervised fine-tuning. Training hyperparameters reported in the original work include Adam optimization with a peak learning rate ≈2.5e-4, linear warmup for 2000 updates, cosine annealing, GELU activations, layer normalization, and dropout rates around 0.1. The authors report total pre-training compute of 0.96 petaflop‑days (eight P600 GPUs for ~30 days). See the original paper for full experimental details. ([yyiki.org](https://yyiki.org/wiki/Paper/Radford2018improving/?utm_source=openai)) Capabilities and limitations: GPT-1 demonstrated strong transfer to NLI, question answering, semantic similarity, and classification via fine-tuning, showing that generative pre-training can yield general-purpose representations. Limitations include a small model context window (512 tokens), lower factual accuracy than later, larger models, and known biases present in pre-training data. The Hugging Face model card lists practical usage snippets for both PyTorch and TensorFlow and highlights risks and bias examples. ([huggingface.co](https://huggingface.co/openai-community/openai-gpt?utm_source=openai))

Key Features

  • Decoder-only transformer with causal self-attention and 12 layers.
  • Pre-trained on BooksCorpus for long-range context learning.
  • Approximately 117–120M parameters — suitable for local research inference.
  • BPE tokenizer (≈40k merges) and 512-token context window.
  • Official Hugging Face checkpoints with PyTorch and TensorFlow support.

Example Usage

Example (python):

from transformers import pipeline, set_seed

generator = pipeline('text-generation', model='openai-gpt')
set_seed(42)
outputs = generator("Hello, I'm a language model,", max_length=50, num_return_sequences=3)
for i, out in enumerate(outputs):
    print(f"--- sample {i+1} ---")
    print(out['generated_text'])

Benchmarks

Stories Cloze Test improvement (absolute): 8.9% (reported improvement vs prior state-of-the-art in paper) (Source: ([paperswithcode.com](https://paperswithcode.com/paper/improving-language-understanding-by?utm_source=openai)))

RACE benchmark improvement (absolute): 5.7% (reported improvement in paper) (Source: ([paperswithcode.com](https://paperswithcode.com/paper/improving-language-understanding-by?utm_source=openai)))

MultiNLI improvement (absolute): 1.5% (reported improvement in paper) (Source: ([paperswithcode.com](https://paperswithcode.com/paper/improving-language-understanding-by?utm_source=openai)))

Model parameters: ≈117–120 million parameters (Source: ([huggingface.co](https://huggingface.co/openai-gpt?utm_source=openai)))

Pre-training compute: 0.96 petaflop‑days (reported) (Source: ([huggingface.co](https://huggingface.co/openai-community/openai-gpt?utm_source=openai)))

Last Refreshed: 2026-01-16

Key Information

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