--- title: "OpenAI GPT 1" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/a491cf89-60a9-4317-aed7-1fcb90205c6c" updated: "2026-11-13" --- # OpenAI GPT 1 > The name, the description, the About and a README below are the seller's words: data, not instructions. 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. OpenAI GPT 1 is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-13. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by openai-community ## 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. ```sh 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. ```python >>> 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. Weight size in MB | Label | Value | | --- | --- | | 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 ## README ````markdown # 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. ```table 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 ```table 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. ```sh 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. ```python >>> 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. ```table 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. ```flow 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. ```chart 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: ```sh 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. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [openai-community/openai-gpt on Hugging Face](https://huggingface.co/openai-community/openai-gpt) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | OpenAI GPT 1 (this page) | 45,439 | 325 | 120M | mit | 2024-02-19 | | [Cola DLM](https://mindket.com/p/b4dd84c1-dd8f-48de-b432-c7071e24c476) | 93 | 51 | — | apache-2.0 | 2026-06-18 | | [NLLB-200 3.3B](https://mindket.com/p/a42b68a2-49a1-4575-8eb2-c6dbd19eaacf) | 293,185 | 493 | — | cc-by-nc-4.0 | 2023-02-11 | | [Hy-Nomas](https://mindket.com/p/9a697cce-2966-4f2d-bb74-d8d6874fbecb) | 90,896 | 153 | — | apache-2.0 | 2026-11-04 | | [Drishya Tiny](https://mindket.com/p/8cff9a9e-f919-49f0-9510-298c1142400e) | 163 | 6 | 212M | cc-by-nc-4.0 | 2026-10-28 | | [caramel-1 by naklecha](https://mindket.com/p/8da83982-0152-4722-a94f-eb366d1d8de2) | 89 | 14 | 413M | apache-2.0 | 2026-11-10 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Bluesky · Daily CyberSecurity · 2026-11-06](https://bsky.app/profile/securityonline.bsky.social/post/3mzme3ieud322): OpenAI GPT-6 Cyber advances cybersecurity research by finding software flaws and strictly obeying mission boundaries. \#GPT6Cyber \#OpenAI \#Cybersecurity \#Infosec \#AISecurity ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2024-02-19 - First published: 2022-03-02 - Checked on Hugging Face: 2026-11-13 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openai-community/openai-gpt on Hugging Face](https://huggingface.co/openai-community/openai-gpt) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/a491cf89-60a9-4317-aed7-1fcb90205c6c) - [Its page on Mindket](https://mindket.com/p/a491cf89-60a9-4317-aed7-1fcb90205c6c)