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Coco Lm Pytorch

Coco Lm Pytorch is a Python library implementing COCO-LM language-model pretraining in PyTorch. Its trainer combines masked language modeling, contrastive learning and corrective learning for generator and discriminator models.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Coco Lm Pytorch — 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 it when you want to explore COCO-LM’s combined pretraining objectives in a PyTorch workflow and can supply transformer models and tokenized input.

Who it is for

  • Researchers experimenting with self-supervised language-model pretraining.
  • Python and PyTorch developers building transformer pretraining workflows.

Documented uses

  • Configure generator and discriminator models for COCO-LM training.
  • Compute a combined training loss and backpropagate it.
  • Save the trained discriminator as a model artifact.
Before you choose

Requirements & limits

The repository identifies the project as MIT-licensed open-source Python software. The README provides pip installation commands.

  • The documented example builds its transformer models with x-transformers.
  • The example requires reserved IDs for classification, masking and padding tokens.
  • The README labels the implementation “wip.”

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 and train the documented COCO-LM example

Configure generator and discriminator transformers, compute a COCO-LM training loss, and save the discriminator model.

Before you start

  • Python and PyTorch
  • The coco-lm-pytorch package
  • The x-transformers package used by the README example
  • Generator and discriminator transformer models, plus token IDs for classification, masking and padding

Steps

  1. Install coco-lm-pytorch and x-transformers using the README’s pip commands.

  2. Create generator and discriminator models with x-transformers; the example recommends a generator roughly one-quarter to one-half the discriminator’s size.

  3. Tie the models’ token and positional embeddings, then instantiate COCO with the discriminator settings, reserved token IDs and objective weights.

  4. Pass token data to the trainer, backpropagate its returned loss, and save the discriminator with torch.save.

Expected outcome

A COCO-LM loss suitable for backpropagation and a saved discriminator model artifact.

Guide limitations

  • The README labels the implementation “wip.”
  • The example uses randomly generated token IDs and does not document dataset preparation or tokenization.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
18 October 2026
Original website title
lucidrains/coco-lm-pytorch · Official homepage title · retained in its source language
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