---
license: apache-2.0
base_model: Dream-org/Dream-v0-Instruct-7B
tags:
  - diffusion
  - reasoning
  - reversethought
  - dream
datasets:
  - ianncity/KIMI-K2.5-1000000x
pipeline_tag: text-generation
---

# Bridge-7b-Diffusion

A fine-tuned [DREAM 7B](https://huggingface.co/Dream-org/Dream-v0-Instruct-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](https://huggingface.co/datasets/ianncity/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

```python
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](https://arxiv.org/abs/2508.15487) for details.