kuleshov-group/bd3lms

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1029 stars · 78 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

BD3-LMs is a research implementation of Block Discrete Denoising Diffusion Language Models that interpolate between autoregressive and diffusion language models. It provides training and evaluation code, noise schedules, samplers supporting arbitrary-length generation, and baseline implementations of AR, SEDD, MDLM, and SSD-LM.

Use cases

• train a block diffusion language model
• generate arbitrary-length text with diffusion models
• compare autoregressive vs diffusion language model baselines
• experiment with discrete denoising diffusion for text
• reproduce ICLR 2025 block diffusion results
• tune block size to trade off generation quality and speed
• evaluate likelihoods of diffusion language models

When to choose

• you need flexible-length generation with KV caching and parallel sampling
• you want state-of-the-art diffusion language modeling likelihoods
• you are researching hybrid autoregressive-diffusion models
• you need baseline implementations of SEDD, MDLM, or AR models

When to avoid

• you just need a production-ready LLM for inference
• you want a simple autoregressive model without diffusion
• you need a plug-and-play chatbot or API service
• you lack GPU resources for training language models

Facets

library · maturity active

llm-training llm-inference machine-learning deep-learning benchmarking large-language-models machine-learning deep-learning python diffusion-language-models block-diffusion discrete-diffusion iclr-2025 research-code text-generation kv-caching huggingface natural-language-processing research gpu linux

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