How to Run OpenAI's GPT-2 Text Generator on Your Computer

OpenAI recently published a blog post on their GPT-2 language model. This tutorial shows you how to run the text generator code yourself. As stated in their blog post:

[GPT-2 is an] unsupervised language model which generates coherent paragraphs of text, achieves state-of-the-art performance on many language modeling benchmarks, and performs rudimentary reading comprehension, machine translation, question answering, and summarization—all without task-specific training.

#1: Install system-wide dependencies

Ensure your system has the following packages installed:

  • CUDA
  • cuDNN
  • NVIDIA graphics drivers

To install all this in one line, you can use Lambda Stack.

#2: Install Python dependencies & GPT-2 code

git clone
cd gpt-2/
python3 -m venv ~/venv-gpt-2
. ~/venv-gpt-2/bin/activate
pip install tensorflow-gpu==1.12
pip install -r requirements.txt
./ 117M

#3: Run the model

Conditionally generated samples from the paper use top-k random sampling with k = 40. You'll want to set k = 40 in Either edit the file manually or use this command:

sed -i 's/top_k=0/top_k=40/g' src/

Now, you're ready to run the model!

python3 src/

Note: OpenAI has only released the smaller (117M) version of their model, which is what we've downloaded already. Hopefully they'll release the full one soon. Here's the smaller model's attempt at writing the rest of this article based on the first paragraph:

(gpt-2) 0|ubuntu@rhodes:gpt-2$ python3 src/
Model prompt >>> OpenAI has recently published a major advance in language modeling with the publication of their GPT-2 model and release of their code. This tutorial shows you how to run the code yourself with GPU enabled TensorFlow.
======================================== SAMPLE 1 ========================================

Step 2: OpenAI Language Abstracts

The code you create in this tutorial can be converted to TensorFlow or TensorFlowProtractor. You will need a 4K image file, the correct one for your GPU and setting some parameters. Now, for the images:

it is important that you set the Frame buffer width and fragment rate used by your image, the text2geometry is used to create fluid or transition box.

The frames .fot from the original .fot file are used. When you close the .fot file, open and press T:

OpenAI really only know the BLSHORT modes, so they are only used at default values, even if they download it into MDP.

Check that that you need to export the data:

OpenAI Int Lum. Text Data

Test RegId

4.6 Ways to create your new TensorFlow library

To create your new TensorFlow library, you will first get an output file, a Lua script. Copy the spicNode, which is run in the YAML, but use into control file program " TensorFlowStar ". open it with vi or command line open and drag the spic_function gist to read the gist, the
program works and you will show the directory you created in the MATLAB config of your THQ programs. and then iterate your code all the way to it's goal using the convolution frontend programs (RNG, Python and the hyperpanos philosophy used to model the cerebral cortex). Next,
you will saw how could you easily set new options for simulation of HBM ImageNet. I used the new data generated by the tip of this post for what you need at initialization phase for some illustration his experiments.

from thq import xhb, BitmapAtlas, TensorFlowDecoratorCode from stroke.ambient import xmap , ASCIIDrawable as ux = sc2.create_png( 144 , 0 , 0 , Float, 16 ).draw( Con http, f, 0.8 ) dx = sc2.read_flip( drawseven , cut, z ) Dx.fill( "RGB", 1.247 ) dxGl = smooth.monochrome_fade( 1.167 , 1.216 , 1.345 ) glEn= bitmap.read_flip( FlatLinear or Caps.Broad

Looks like we'll be keeping our jobs for a while longer :).

Further Reading

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