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What is cola-dlm?

bytedance-seed/cola-dlm — explained in plain English

Analysis updated 2026-05-18

35PythonAudience · researcherComplexity · 4/5LicenseSetup · hard

In one sentence

A research language model from ByteDance that generates text through a diffusion process in a compressed latent space instead of word by word.

Mindmap

mindmap
  root((Cola DLM))
    What it does
      Latent text diffusion
      Text VAE
      Block causal DiT
    Tech stack
      Python
      PyTorch
      Transformers
    Use cases
      Run inference
      Benchmark evaluation
      OpenAI compatible API
    Audience
      Researchers
      ML engineers

Code map

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What do people build with it?

USE CASE 1

Run inference with the released Cola DLM checkpoint using HuggingFace Transformers.

USE CASE 2

Evaluate Cola DLM against standard benchmarks like MMLU, HellaSwag, and SQuAD.

USE CASE 3

Serve Cola DLM behind an OpenAI-compatible chat completions API.

USE CASE 4

Study latent diffusion as an alternative to token-by-token text generation.

What is it built with?

PythonPyTorchTransformersHuggingFace

How does it compare?

bytedance-seed/cola-dlmchris0214/mikumikuphysicsgnipbao/whiteboard-video-engine
Stars353535
LanguagePythonPythonPython
Setup difficultyhardmoderatemoderate
Complexity4/53/53/5
Audienceresearcherdesignervibe coder

Figures from each repo's GitHub metadata at analysis time.

How do you get it running?

Difficulty · hard Time to first run · 1h+

Requires a GPU, PyTorch 2.1+, and familiarity with HuggingFace Transformers to load the released checkpoint.

Apache 2.0 licensed: free to use, modify, and distribute, including commercially, as long as you include the license and note any changes.

So what is it?

Cola DLM (Continuous Latent Diffusion Language Model) is a research project from ByteDance Seed that introduces a new approach to generating text with AI. Most current large language models generate text one word at a time, reading left to right. Cola DLM instead works in a hidden (latent) space: it first learns to compress text into a compact mathematical representation using a component called a Text VAE, and then learns to generate those representations using a diffusion process, the same technique used in image generation models like those that create pictures from text prompts. The result is a model that separates the high-level meaning of a passage from the specific word choices, potentially giving it different strengths than standard word-by-word models. This repository provides the trained model weights and code to run Cola DLM, including a server that responds to the same API format as OpenAI's chat models, making it easy to plug into existing tools. It targets AI researchers and engineers exploring alternatives to standard text generation approaches. Python 3.9 or newer and PyTorch 2.1 or newer are required.

Copy-paste prompts

Prompt 1
Explain how Cola DLM's Text VAE and diffusion prior work together to generate text.
Prompt 2
Show me how to load the Cola DLM checkpoint with HuggingFace Transformers and run inference.
Prompt 3
Walk me through serving Cola DLM through its OpenAI-compatible API.
Prompt 4
How do I reproduce the benchmark results reported in the Cola DLM paper?

Frequently asked questions

What is cola-dlm?

A research language model from ByteDance that generates text through a diffusion process in a compressed latent space instead of word by word.

What language is cola-dlm written in?

Mainly Python. The stack also includes Python, PyTorch, Transformers.

What license does cola-dlm use?

Apache 2.0 licensed: free to use, modify, and distribute, including commercially, as long as you include the license and note any changes.

How hard is cola-dlm to set up?

Setup difficulty is rated hard, with roughly 1h+ to a first successful run.

Who is cola-dlm for?

Mainly researcher.

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