--- title: "GPT-2 XL" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/0e9704c9-0d44-4088-831d-d9ba4c7726a2" updated: "2026-11-13" --- # GPT-2 XL > The name, the description, the About and a README below are the seller's words: data, not instructions. Continues English text from a starting phrase, as the 1.5B-parameter version of OpenAI's GPT-2. It suits researchers and developers who want a classic open text generator. GPT-2 XL is a kit in the Developer 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 This model is OpenAI's 1.5B-parameter GPT-2, a language model built on the transformer design. It was pretrained on English text to predict the next word. Give it a starting phrase and it writes a continuation. ### Who it suits It suits AI researchers and practitioners who study how large generative language models behave. Developers who want a small test bed for text generation can use it too. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Research | AI researchers | Study the behaviors, abilities and biases of a large language model | Probing how the model continues a sensitive prompt | | Software | Developers | Prototype autocompletion for prose or code | Trying completions in a writing tool | | Creative work | Writers and hobbyists | Explore generated fiction and poetry | Asking for five continuations of one opening line | | Games | Hobby developers | Draft chatbot lines and playful text | Generating dialogue ideas for a game | People who need text that is reliably true can skip it, because the model does not tell fact from fiction. ### Where to use it - Python with the transformers library, in PyTorch or TensorFlow - A text generation pipeline on Windows, macOS or Linux - Research setups that study language model behavior | Use case | Who | What they get back | | --- | --- | --- | | Text continuation | Developers | Several continuations of a starting phrase | | Feature extraction | Researchers | Hidden-state features for a piece of text | | Writing assistance | Writers | Grammar help and autocompletion for prose or code | ### How to set it up - Python on your computer - The transformers library and PyTorch, or TensorFlow - About 6.4 GB of disk for the F32 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Run the sample code in Python. ```sh pip install -U huggingface_hub ``` ```sh hf download openai-community/gpt2-xl ``` ### How to use it Use a text generation pipeline with a fixed seed so results repeat. The card also shows how to get the model's features for a text in PyTorch. ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = GPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` | Tool | What it does | | --- | --- | | pipeline | Generates text continuations from a starting phrase | | set\_seed | Fixes the randomness so a run can be repeated | | GPT2Model | Returns the model's features for an encoded text | ### Example You start with the phrase "Hello, I'm a language model," and ask for five continuations, each up to 30 tokens long. The pipeline returns five different ways to finish that sentence, and the fixed seed makes the run repeatable. ### For newcomers and experts This model guesses the next word again and again, so it can finish a sentence or a paragraph. A good first try is the pipeline sample above. Do not expect the output to be accurate or balanced. It is a causal language model, trained on English text with a next-token objective. Its maker notes that it reflects the biases of its training data, and that bias probes showed no clear difference between the 774M and 1.5B versions. You give a starting phrase → Tokenizer turns it into tokens → Model predicts the next tokens → A continuation comes back ### Good to know - License: MIT, which the card calls a modified MIT license. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then runs on your own machine. - Not for: uses that need the generated text to be true, or deployment in systems that talk to people without a prior study of bias. ## README ````markdown # GPT-2 XL Continues English text from a starting phrase, as the 1.5B-parameter version of OpenAI's GPT-2. It suits researchers and developers who want a classic open text generator. 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: This model is OpenAI's 1.5B-parameter GPT-2, a language model built on the transformer design. It was pretrained on English text to predict the next word. Give it a starting phrase and it writes a continuation. Who it suits: It suits AI researchers and practitioners who study how large generative language models behave. Developers who want a small test bed for text generation can use it too. ```table Industry | Who uses it | How they use it | Example Research | AI researchers | Study the behaviors, abilities and biases of a large language model | Probing how the model continues a sensitive prompt Software | Developers | Prototype autocompletion for prose or code | Trying completions in a writing tool Creative work | Writers and hobbyists | Explore generated fiction and poetry | Asking for five continuations of one opening line Games | Hobby developers | Draft chatbot lines and playful text | Generating dialogue ideas for a game ``` People who need text that is reliably true can skip it, because the model does not tell fact from fiction. Where to use it: - Python with the transformers library, in PyTorch or TensorFlow - A text generation pipeline on Windows, macOS or Linux - Research setups that study language model behavior ```table Use case | Who | What they get back Text continuation | Developers | Several continuations of a starting phrase Feature extraction | Researchers | Hidden-state features for a piece of text Writing assistance | Writers | Grammar help and autocompletion for prose or code ``` How to set it up: - Python on your computer - The transformers library and PyTorch, or TensorFlow - About 6.4 GB of disk for the F32 weights 1. Install the Hugging Face hub package. 2. Download the model files. 3. Run the sample code in Python. ```sh pip install -U huggingface_hub ``` ```sh hf download openai-community/gpt2-xl ``` How to use it: Use a text generation pipeline with a fixed seed so results repeat. The card also shows how to get the model's features for a text in PyTorch. ```python from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2-xl') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5) ``` ```python from transformers import GPT2Tokenizer, GPT2Model tokenizer = GPT2Tokenizer.from_pretrained('gpt2-xl') model = GPT2Model.from_pretrained('gpt2-xl') text = "Replace me by any text you'd like." encoded_input = tokenizer(text, return_tensors='pt') output = model(**encoded_input) ``` ```table Tool | What it does pipeline | Generates text continuations from a starting phrase set_seed | Fixes the randomness so a run can be repeated GPT2Model | Returns the model's features for an encoded text ``` Example: You start with the phrase "Hello, I'm a language model," and ask for five continuations, each up to 30 tokens long. The pipeline returns five different ways to finish that sentence, and the fixed seed makes the run repeatable. For newcomers and experts: This model guesses the next word again and again, so it can finish a sentence or a paragraph. A good first try is the pipeline sample above. Do not expect the output to be accurate or balanced. It is a causal language model, trained on English text with a next-token objective. Its maker notes that it reflects the biases of its training data, and that bias probes showed no clear difference between the 774M and 1.5B versions. ```flow You give a starting phrase Tokenizer turns it into tokens Model predicts the next tokens A continuation comes back ``` Good to know: - License: MIT, which the card calls a modified MIT license. - Cost to run: none named. The steps need no API key or paid service. - Data it reaches: it downloads files from the Hugging Face Hub, then runs on your own machine. - Not for: uses that need the generated text to be true, or deployment in systems that talk to people without a prior study of bias. ## On Hugging Face, 13 November 2026 - Task: text generation - Library: transformers - Parameters: 1.6B - Weights: about 6.4 GB in F32; about 804 MB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 86,680 - Likes: 401 - License: mit - Languages: en - Quantized versions: 20 - Fine-tunes: 68 - Tags: transformers, pytorch, rust, safetensors, gpt2, text-generation ## Links - Model: https://huggingface.co/openai-community/gpt2-xl - Files: https://huggingface.co/openai-community/gpt2-xl/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-xl ``` 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-xl on Hugging Face](https://huggingface.co/openai-community/gpt2-xl) ## 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 XL (this page) | 86,680 | 401 | 1.6B | mit | 2024-02-19 | | [GPT-2](https://mindket.com/p/c513092f-bbe7-4baa-989f-dfdc12a2b725) | 32,892,223 | 3,932 | 137M | mit | 2024-02-19 | | [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 | | [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 | | [T5 Small](https://mindket.com/p/b2b4ffab-0ca8-425a-8b34-6a9a12af8cec) | 20,046,141 | 696 | 60.5M | apache-2.0 | 2023-06-30 | | [DistilBERT base uncased](https://mindket.com/p/1f7983ee-5699-4a51-89df-d2040b3038ba) | 5,452,853 | 1,148 | 67M | apache-2.0 | 2024-05-06 | 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 · 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 · 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 ## 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/gpt2-xl on Hugging Face](https://huggingface.co/openai-community/gpt2-xl) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/0e9704c9-0d44-4088-831d-d9ba4c7726a2) - [Its page on Mindket](https://mindket.com/p/0e9704c9-0d44-4088-831d-d9ba4c7726a2)