OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Models

Pplm

PPLM is a Python implementation of plug-and-play language-model steering that combines a pretrained unconditional language model with small attribute models to guide generated text toward objectives such as a topic or sentiment, without retraining or fine-tuning the base model.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Pplm — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use PPLM when an experiment calls for steering GPT-2 generation toward a topic or attribute without fine-tuning the base language model.

Who it is for

  • Researchers experimenting with controlled text generation.
  • Developers exploring GPT-2 topic or sentiment steering through command-line examples.
  • Practitioners adapting PPLM within a Hugging Face Transformers workflow.

Documented uses

  • Generate GPT-2 text steered toward a chosen bag-of-words topic.
  • Condition generated text on discriminator attributes such as positive or negative sentiment.
  • Compare steering settings and investigate repetition in generated output.
  • Set up the repository's documented Python dependencies before using its examples.
Before you choose

Requirements & limits

The repository is public and identifies its license as Apache-2.0. Its README also provides a Colab notebook described as usable without setup.

  • The repository documents both bag-of-words topic control and discriminator-based sentiment control.
  • The README describes changing the step size to adjust steering strength and offers parameter suggestions for repetitive output.
  • Models in the repository root differ from those used for the paper analysis; the README directs baseline users to a separate paper_code folder and notes that parameters may require tuning.
  • The README says PPLM is integrated into Hugging Face Transformers, but does not establish compatibility with every version.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Set up the PPLM repository

Install the Python dependencies listed by the repository before following its command-line examples.

Before you start

  • Python environment suitable for the repository requirements file.
  • A copy of the PPLM repository.

Steps

  1. From the repository directory, run the command below.

    Documented command
    pip install -r requirements.txt

  2. Continue with the repository's documented bag-of-words or discriminator example command, selecting the method relevant to the experiment.

Expected outcome

The repository's listed dependencies are installed, leaving the documented CLI examples as the next stage.

Guide limitations

  • The README does not specify Python or dependency versions in the cited setup instructions.
  • Installing requirements does not establish that an example runs successfully in a particular environment.
  • For paper-baseline reproduction, use the models in the paper_code folder and account for the README's parameter-tuning guidance.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
uber-research/PPLM · Official homepage title · retained in its source language
How we review sourcesSuggest a correction