Dream 7B: Diffusion Large Language Models
Paper • 2508.15487 • Published • 1
Bridge-7b-Diffusion
A fine-tuned DREAM 7B masked diffusion language model trained with the ReverseThought objective.
What is ReverseThought?
Given a question and its answer, the model learns to produce the step-by-step reasoning chain that bridges the question to the answer. This trains the model to generate coherent chain-of-thought reasoning via DREAM's masked diffusion process.
- Input: Question + Answer
- Output: Detailed reasoning trace connecting them
Training Details
- Base model: Dream-org/Dream-v0-Instruct-7B
- Training data: 75,000 examples from KIMI-K2.5-1000000x (General-Distillation subset)
- Objective: DREAM masked diffusion with CART time reweighting
- Hardware: 8x NVIDIA H100 80GB
- Epochs: 3
- Batch size: 128
- Learning rate: 2e-6 (cosine schedule)
- Max sequence length: 2048 tokens
- Precision: bf16 mixed precision (FSDP)
Usage
from transformers import AutoModel, AutoTokenizer
model = AutoModel.from_pretrained("WilhelmH/Bridge-7b-Diffusion", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("WilhelmH/Bridge-7b-Diffusion", trust_remote_code=True)
Architecture
This is a masked diffusion language model (not autoregressive). It uses bidirectional attention and generates text by iteratively denoising masked tokens. See the DREAM paper for details.
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Base model
Dream-org/Dream-v0-Instruct-7B