OpenAI GPT 1

OpenAI GPT 1

Continues English text you start, using the first transformer language model OpenAI released, at 120M parameters. For people who study early language models or learn the transformers library.

Made by openai-community on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face.

About

What it is: OpenAI GPT 1, also known as GPT-1, was OpenAI's first language model built on the transformer design. It reads text in one direction and was pre-trained on a large body of text. It has 120M parameters and works in English.

Who it suits: It suits people who want a small, old, openly licensed language model to study or test. The Hugging Face record lists 13 fine-tuned versions made by others.

Industry | Who uses it | How they use it | Example
Education | Students and teachers | Study how an early transformer language model writes text | Generating five continuations of one sentence and comparing them
Research | Language researchers | Use it as a baseline for language modeling tasks | Comparing it with the GPT2 models the card lists as related
Software | Developers | Test text generation code on a small model, about 479 MB in F32 | Running the text generation pipeline once to check a setup
Machine learning | Fine-tuners | Start a fine-tune from a small base | One of the 13 fine-tuned versions listed on Hugging Face

You can skip it if you need factual output, because the card says its training did not aim at truth.

Where to use it:

• Python with the transformers library, using PyTorch or TensorFlow
• The transformers text generation pipeline
• Your own computer, on Windows, macOS or Linux with Python

Use case | Who | What they get back
Continue an opening line | Developers | Several continuations of the text you start
Read hidden states | Machine learning students | The last hidden states for a sentence
Language modeling experiments | Researchers | A small baseline model to compare against

How to set it up:

• Python and pip
• The transformers library, plus PyTorch or TensorFlow, since the card's code uses them
• About 479 MB of disk for the F32 weights

1. Install the Hugging Face hub package.
2. Download the model files to your computer.
3. Run the card's Python code against the model name openai-gpt.

pip install -U huggingface_hub
hf download openai-community/openai-gpt

How to use it: The card says you can use the model straight from a text generation pipeline. It sets a seed first, because generation involves some randomness and you want repeatable results.

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

For hidden states, the card also shows PyTorch and TensorFlow code that loads the tokenizer and model and reads the last hidden state.

Tool | What it does
pipeline('text-generation', model='openai-gpt') | Builds a text generator from the model
set_seed(42) | Fixes the randomness so a run can be repeated
OpenAIGPTTokenizer | Turns your text into tokens the model reads
OpenAIGPTModel | Returns hidden states in PyTorch
TFOpenAIGPTModel | Returns hidden states in TensorFlow

Example: You give the pipeline the opening "Hello, I'm a language model," and ask for five continuations with a length limit of 30. It returns five different short pieces of story like text, each starting with your line.

For newcomers and experts: It continues a sentence you start. It is small and old, so the text drifts and cannot be trusted as fact. For a first try, download the files and run the pipeline lines with an opening line of your own.

Underneath, it is a causal transformer, so each word is predicted from the words before it. The model's paper tests it on inference, question answering, similarity and classification tasks. The card names the GPT2 family as related models, and the sample sets a seed so random sampling gives the same text each time.

Your opening line goes in
The tokenizer turns it into tokens
The model predicts the next tokens
The pipeline samples continuations
Continuation text comes back

Good to know: The card carries a content warning, since text the model writes can be disturbing or offensive and can repeat stereotypes.

Title: Weight size in MB
F32: 479 MB
4-bit estimate: 60 MB

• License: MIT
• Cost to run: no API key or paid service is named, the model runs on your own computer
• Data it reaches: downloads model files from Hugging Face and works on the text you give it
• Not for: generating factual content about people or events, which the card calls out of scope

On Hugging Face, 13 November 2026

• Task: text generation
• Library: transformers
• Parameters: 120M
• Weights: about 479 MB in F32; about 60 MB at 4-bit (estimate)
• Formats: Safetensors
• Downloads in the last 30 days: 45,439
• Likes: 325
• License: mit
• Languages: en
• Fine-tunes: 13
• Tags: transformers, pytorch, rust, safetensors, openai-gpt, text-generation

Links

• Model: https://huggingface.co/openai-community/openai-gpt
• Files: https://huggingface.co/openai-community/openai-gpt/tree/main

Get the model

Download its files with Hugging Face's command line:

pip install -U huggingface_hub
hf download openai-community/openai-gpt

Then follow the usage in its model card. Nothing in this kit runs by itself.