DreamLM/Dream

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1265 stars · 76 forks observed · 2026-08-28

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

Dream 7B is an open diffusion large language model (dLLM) with base and instruct checkpoints, plus inference and training code built on Hugging Face transformers. It matches or exceeds similarly sized autoregressive models on general, math, and coding tasks while offering diffusion-based planning and infilling flexibility.

Use cases

• run a diffusion language model for text generation
• fine-tune a 7B dLLM on my own dataset
• compare diffusion vs autoregressive LLM performance
• experiment with parallel and infilling text generation
• generate code with an open diffusion model
• research non-autoregressive language model architectures

When to choose

• you want to experiment with diffusion-based text generation instead of autoregressive decoding
• you need an open-weights 7B model with strong math, coding, and planning ability
• you want training code to fine-tune a dLLM on custom data
• you need variable-length generation or infilling capabilities

When to avoid

• you need production-ready, widely supported inference like standard autoregressive LLMs
• you lack a GPU with at least 20GB memory
• you need the latest transformers/torch versions, which are untested
• you need multimodal or long-context capabilities beyond this model's scope

Facets

library · maturity active

llm-inference llm-training machine-learning deep-learning large-language-models artificial-intelligence deep-learning python diffusion-language-model text-generation huggingface-transformers 7b-model open-weights natural-language-processing gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="DreamLM/Dream")