ZHZisZZ/dllm

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2672 stars · 278 forks observed · 2026-08-28

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

dLLM is a Python library that unifies training, inference, and evaluation of diffusion language models such as LLaDA and Dream. It builds on Hugging Face transformers Trainer and lm-evaluation-harness, with support for LoRA, DeepSpeed, FSDP, and algorithms like MDLM, BD3LM, and diffu-GRPO.

Use cases

• train a masked diffusion language model
• fine-tune LLaDA or Dream with LoRA
• evaluate diffusion LMs on benchmarks
• run GRPO reinforcement learning on diffusion language models
• implement block diffusion or edit flows training algorithms
• accelerate diffusion LM inference with caching

When to choose

• you want a reproducible, unified pipeline for diffusion language model training and evaluation
• you need recipes for open-weight diffusion models like LLaDA and Dream
• you want to experiment with masked diffusion or block diffusion training algorithms

When to avoid

• you only need standard autoregressive LLM training, which existing frameworks handle better
• you need production inference serving rather than research training and evaluation

Facets

library · maturity active

llm-training machine-learning llm-inference rag large-language-models machine-learning deep-learning python diffusion-language-models masked-diffusion llada dream grpo lora deepspeed fsdp lm-evaluation-harness natural-language-processing gpu

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