Dream 7B

Dream 7B

WebsiteFreeLarge Language Models (LLMs)Research Tools
Dream 7B is a groundbreaking 7-billion-parameter diffusion language model that matches or exceeds top-tier autoregressive models while offering superior planning abilities and flexible inference capabilities.
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https://hkunlp.github.io/blog/2025/dream?ref=aipure
Dream 7B

Product Information

Updated:Jun 16, 2025

Dream 7B Monthly Traffic Trends

Dream 7B received 15.8k visits last month, demonstrating a Slight Growth of 15.2%. Based on our analysis, this trend aligns with typical market dynamics in the AI tools sector.
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What is Dream 7B

Dream 7B, developed jointly by the University of Hong Kong and Huawei Noah's Ark Lab, represents the most powerful open diffusion large language model to date. Released in 2025, it is trained on 580 billion tokens from diverse datasets including Dolma v1.7, OpenCoder, and DCLM-Baseline. The model comes in two versions: a base model (Dream-v0-Base-7B) and a supervised fine-tuned instruction model (Dream-v0-Instruct-7B), both openly available to the research community.

Key Features of Dream 7B

Dream 7B is a groundbreaking open-source diffusion large language model developed by HKU NLP and Huawei Noah's Ark Lab, featuring 7 billion parameters. It represents a significant departure from traditional autoregressive models by using discrete diffusion modeling, enabling parallel token generation and bidirectional context understanding. The model demonstrates competitive performance comparable to leading autoregressive models in general tasks, mathematics, and coding, while offering unique advantages in planning abilities and flexible inference capabilities.
Bidirectional Contextual Modeling: Enables richer integration of information from both directions during text generation, enhancing global coherence across generated content
Flexible Generation Control: Supports various generation modes including completion, infilling, and arbitrary order generation through its iterative refinement process
Quality-Speed Trade-off: Offers adjustable inference steps allowing users to balance between generation speed and output quality based on their needs
Context-adaptive Token-level Noise Rescheduling: Dynamically adjusts noise levels for individual tokens based on contextual information, improving generation accuracy

Use Cases of Dream 7B

Complex Problem Solving: Particularly effective for tasks requiring multiple constraints or specific objectives, such as Sudoku solving and mathematical reasoning
Code Generation: Capable of generating and completing code snippets with strong performance comparable to specialized coding models
Text Completion and Editing: Flexible text generation capabilities make it suitable for various content creation and editing tasks, with ability to fill in gaps or complete partial content

Pros

Superior planning capabilities compared to similar-sized autoregressive models
Flexible inference options with controllable generation order
Competitive performance across general, math, and coding tasks

Cons

Requires careful learning rate tuning during training
Computational intensity during training (required 96 NVIDIA H800 GPUs)
Still needs more exploration in post-training techniques

How to Use Dream 7B

Install required dependencies: Install PyTorch and Transformers library from Hugging Face
Import necessary libraries: Import torch and transformers libraries: import torch from transformers import AutoModel, AutoTokenizer
Load the model: Load either the base model 'Dream-org/Dream-v0-Base-7B' or instruction-tuned model 'Dream-org/Dream-v0-Instruct-7B': model_path = 'Dream-org/Dream-v0-Instruct-7B' model = AutoModel.from_pretrained(model_path, torch_dtype=torch.bfloat16, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
Move model to GPU and set to eval mode: model = model.to('cuda').eval()
Prepare input: Format your input as messages list: messages = [{'role': 'user', 'content': 'Your prompt here'}]
Tokenize input: inputs = tokenizer.apply_chat_template(messages, return_tensors='pt', return_dict=True, add_generation_prompt=True)
Generate output: The model supports flexible generation modes including completion, infilling, and controlled generation order. You can adjust diffusion steps to trade off between quality and speed.
Optional: Adjust inference parameters: You can customize the generation by adjusting parameters like number of diffusion steps - fewer steps for faster but coarser results, more steps for higher quality outputs

Dream 7B FAQs

Dream 7B is the most powerful open diffusion large language model to date, developed jointly by The University of Hong Kong and Huawei Noah's Ark Lab. It's a 7B parameter model that matches or exceeds top-tier Autoregressive language models of similar size on general, math, and coding abilities.

Analytics of Dream 7B Website

Dream 7B Traffic & Rankings
15.8K
Monthly Visits
#1198386
Global Rank
-
Category Rank
Traffic Trends: Feb 2025-May 2025
Dream 7B User Insights
00:04:18
Avg. Visit Duration
2.94
Pages Per Visit
49.02%
User Bounce Rate
Top Regions of Dream 7B

  1. US: 56.28%

  2. IN: 14.77%

  3. DE: 14.4%

  4. VN: 7.69%

  5. JP: 3.41%

  6. Others: 3.45%

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