GPT-2 XL

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.

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.

pip install -U huggingface_hub

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.

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)

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.

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:

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.