--- title: "GPT-2" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725" updated: "2026-11-12" --- # GPT-2 > The name, the description, the About and a README below are the seller's words: data, not instructions. Generates English text from a prompt with the smallest GPT-2 model, pretrained to predict the next word. Suits learners, researchers and developers who want a small base model to run or fine-tune. GPT-2 is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by openai-community ## About ### What it is GPT-2 is a language model pretrained on a large body of English text with one goal: predict the next word. This is the smallest GPT-2 version. You give it a prompt and it continues the text, or you fine-tune it for a task of your own. ### Who it suits Students, researchers and developers who want a small, well-known base model for text generation, feature extraction or fine-tuning. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Education | Students | Learn how a language model generates text | Run the pipeline example and read the output | | Research | NLP researchers | Use it as a baseline for fine-tuning or bias studies | Fine-tune it on a small text set | | Software | Developers | Prototype text generation features | Continue a short prompt in an app | If you need factual or safe-to-publish answers, you can skip it, since the card warns against that use. ### Where to use it - Python with the transformers library, using PyTorch or TensorFlow - Windows, macOS or Linux - A text generation pipeline for quick tests | Use case | Who | What they get back | | --- | --- | --- | | Text continuation | Learners | Several continuations of a prompt | | Feature extraction | Researchers | Hidden features of a given text | | Fine-tuning base | Developers | A starting point for a task-specific model | ### How to set it up - Python with pip - The transformers library, plus PyTorch or TensorFlow - Modest memory, since the weights are about 548 MB in F32 1. Install the Hugging Face hub package. 2. Download the model, or let the transformers pipeline fetch it by name. 3. Run the pipeline example. ```sh pip install -U huggingface_hub hf download openai-community/gpt2 ``` ### How to use it Use the text generation pipeline. Generation has some randomness, so the card sets a seed to get repeatable output. ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` | Tool | What it does | | --- | --- | | pipeline | Runs text generation in a few lines | | set\_seed | Makes the random output repeatable | | GPT2Tokenizer and GPT2Model | Give you the features of a text in PyTorch | ### Example You start a sentence such as a greeting from a language model and ask for 5 continuations of up to 30 tokens. You get five different completions, each one different because of the randomness. ### For newcomers and experts If you are new, run the pipeline example and change the prompt. Expect fluent but sometimes odd text. The model predicts each token from the ones before it, using an internal mask so that token i only sees tokens 1 to i. Its training targets are the same text shifted one token to the right. It learns an inner picture of English that you can reuse for other tasks, though it is best at generating text from a prompt. Raw English text → Targets are the text shifted by one token → A mask hides future tokens → The model learns the next token → You prompt it to continue ### Good to know - Hugging Face lists about 137M parameters, and the card calls this the smallest GPT-2 - The training data is not released, and it holds a lot of unfiltered internet content - The model does not tell fact from fiction, so its text can be false or biased - License: MIT - Cost to run: none named - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: uses that need the text to be true, or putting it into systems that talk to people before studying its biases ## README ````markdown # GPT-2 Generates English text from a prompt with the smallest GPT-2 model, pretrained to predict the next word. Suits learners, researchers and developers who want a small base model to run or fine-tune. 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: GPT-2 is a language model pretrained on a large body of English text with one goal: predict the next word. This is the smallest GPT-2 version. You give it a prompt and it continues the text, or you fine-tune it for a task of your own. Who it suits: Students, researchers and developers who want a small, well-known base model for text generation, feature extraction or fine-tuning. ```table Industry | Who uses it | How they use it | Example Education | Students | Learn how a language model generates text | Run the pipeline example and read the output Research | NLP researchers | Use it as a baseline for fine-tuning or bias studies | Fine-tune it on a small text set Software | Developers | Prototype text generation features | Continue a short prompt in an app ``` If you need factual or safe-to-publish answers, you can skip it, since the card warns against that use. Where to use it: - Python with the transformers library, using PyTorch or TensorFlow - Windows, macOS or Linux - A text generation pipeline for quick tests ```table Use case | Who | What they get back Text continuation | Learners | Several continuations of a prompt Feature extraction | Researchers | Hidden features of a given text Fine-tuning base | Developers | A starting point for a task-specific model ``` How to set it up: - Python with pip - The transformers library, plus PyTorch or TensorFlow - Modest memory, since the weights are about 548 MB in F32 1. Install the Hugging Face hub package. 2. Download the model, or let the transformers pipeline fetch it by name. 3. Run the pipeline example. ```sh pip install -U huggingface_hub hf download openai-community/gpt2 ``` How to use it: Use the text generation pipeline. Generation has some randomness, so the card sets a seed to get repeatable output. ```python >>> from transformers import pipeline, set_seed >>> generator = pipeline('text-generation', model='gpt2') >>> set_seed(42) >>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` ```table Tool | What it does pipeline | Runs text generation in a few lines set_seed | Makes the random output repeatable GPT2Tokenizer and GPT2Model | Give you the features of a text in PyTorch ``` Example: You start a sentence such as a greeting from a language model and ask for 5 continuations of up to 30 tokens. You get five different completions, each one different because of the randomness. For newcomers and experts: If you are new, run the pipeline example and change the prompt. Expect fluent but sometimes odd text. The model predicts each token from the ones before it, using an internal mask so that token i only sees tokens 1 to i. Its training targets are the same text shifted one token to the right. It learns an inner picture of English that you can reuse for other tasks, though it is best at generating text from a prompt. ```flow Raw English text Targets are the text shifted by one token A mask hides future tokens The model learns the next token You prompt it to continue ``` Good to know: - Hugging Face lists about 137M parameters, and the card calls this the smallest GPT-2 - The training data is not released, and it holds a lot of unfiltered internet content - The model does not tell fact from fiction, so its text can be false or biased - License: MIT - Cost to run: none named - Data it reaches: your prompts and the files downloaded from Hugging Face - Not for: uses that need the text to be true, or putting it into systems that talk to people before studying its biases ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 137M - Weights: about 548 MB in F32; about 69 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 32,892,223 - Likes: 3,932 - License: mit - Languages: en - Quantized versions: 99 - Fine-tunes: 2,354 - Tags: transformers, pytorch, tflite, rust, onnx, safetensors, gpt2, text-generation, exbert ## Links - Model: https://huggingface.co/openai-community/gpt2 - Files: https://huggingface.co/openai-community/gpt2/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download openai-community/gpt2 ``` 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/gpt2 on Hugging Face](https://huggingface.co/openai-community/gpt2) ## 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 | | --- | --- | --- | --- | --- | --- | | GPT-2 (this page) | 32,892,223 | 3,932 | 137M | mit | 2024-02-19 | | [BERT base model (uncased)](https://mindket.com/p/d5887db0-760c-4639-8457-d0da6a3a2699) | 31,346,528 | 3,071 | 110M | apache-2.0 | 2024-02-19 | | [GPT-2 XL](https://mindket.com/p/0e9704c9-0d44-4088-831d-d9ba4c7726a2) | 86,680 | 401 | 1.6B | mit | 2024-02-19 | | [DistilBERT base uncased](https://mindket.com/p/1f7983ee-5699-4a51-89df-d2040b3038ba) | 5,452,853 | 1,148 | 67M | apache-2.0 | 2024-05-06 | | [all-MiniLM-L6-v2](https://mindket.com/p/ee5187e2-9f20-47be-ae7e-3a6007276a39) | 174,740,184 | 6,104 | 22.7M | apache-2.0 | 2026-06-01 | | [T5 Small](https://mindket.com/p/b2b4ffab-0ca8-425a-8b34-6a9a12af8cec) | 20,046,141 | 696 | 60.5M | apache-2.0 | 2023-06-30 | 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. - [Hacker News · doener · 2024-05-01](https://news.ycombinator.com/item?id=40222015): GPT-2 - [Bluesky · Christine Justice · 2026-08-21](https://bsky.app/profile/christinejustice.bsky.social/post/3mtm6opg2622x): 'Control AI' Connor Leahy was recently on Democracy Now\! He reverse engineered OpenAI GPT2 in college in 2019. aI is here, we need to control it. Data centers need to be controlled too. With environmental conservation \& not at a cost to the community. We have to deal with both AI \& data centers. - [Hacker News · daveguy · 2026-04-16](https://news.ycombinator.com/item?id=47799673): Claude Opus 4.7: Here is the XL model. 20x the size of the medium model. Still just 2B parameters, but on the bright side it was trained pre-wordslop. https://huggingface.co/openai-community/gpt2-xl - [Hacker News · wat10000 · 2026-04-16](https://news.ycombinator.com/item?id=47795844): Claude Opus 4.7: You can run GPT2\! Here's the medium model: https://huggingface.co/openai-community/gpt2-medium I will now have it continue this comment: I've been running gps for a long time, and I always liked that there was something in my pocket (and not just me). One day when driving to work on the highway… - [Hacker News · subroutine · 2026-04-08](https://news.ycombinator.com/item?id=47686468): OpenAI says its new model GPT-2 is too dangerous to release (2019): I'm confused. From what I can tell GPT-2 was released in 2019... https://huggingface.co/openai-community/gpt2 https://github.com/openai/gpt-2/blob/master/model\_card.md - [Hacker News · thatjoeoverthr · 2025-07-09](https://news.ycombinator.com/item?id=44508906): Smollm3: Smol, multilingual, long-context reasoner LLM: Behold https://huggingface.co/openai-community/gpt2-xl - [Hacker News · janalsncm · 2025-07-08](https://news.ycombinator.com/item?id=44505067): Smollm3: Smol, multilingual, long-context reasoner LLM: It’s not locked behind a service though. https://huggingface.co/openai-community/gpt2/blob/main/model... - [Hacker News · SushiHippie · 2024-04-30](https://news.ycombinator.com/item?id=40205870): GPT-4.5 or GPT-5 being tested on LMSYS?: The old models also had only "gpt2"/"gpt2-large" and so on as "identifiers" https://huggingface.co/openai-community ## 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: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open openai-community/gpt2 on Hugging Face](https://huggingface.co/openai-community/gpt2) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/c513092f-bbe7-4baa-989f-dfdc12a2b725) - [Its page on Mindket](https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725)