Dream

Visit Tool

Dream is an Open Source & Models tool that provides a 7B diffusion large language model. It offers competitive performance compared to leading autoregressive models of similar size, with available training and evaluation code.

Claim this tool

No Views Yet

At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is Dream?

Dream is a 7B diffusion large language model designed for research and development in language modeling. It offers competitive performance against leading autoregressive models of similar size. The project provides comprehensive training and evaluation code, including specific implementations for Dream-Coder (a 7B dLLM for code) and DreamOn (which addresses variable-length generation and infilling). Dream's implementation is based on the Huggingface transformers library, requiring specific versions of transformers and PyTorch. It supports various diffusion sampling strategies like `maskgit_plus` and `entropy` for controlling token generation order, and includes options for fine-tuning on custom datasets.

Best used for

Ideal for AI researchers and developers who need to experiment with diffusion large language models, fine-tune them on custom datasets, and evaluate their performance. Especially valuable for those exploring advanced text generation techniques and code generation within an open-source framework.

Common actions

develop language models

fine-tune models

evaluate LLM performance

generate text

experiment with diffusion

workflowsdeepfakeautomated workflowcollaborationlow-code/no-codegithub copilotface swapping"AI Agents"open-source

Capabilities

Key features

7B diffusion LLM
Training code available
Evaluation code available
Variable-length generation
Multiple sampling strategies
Fine-tuning support
Huggingface transformers integration

Target Audience

professordeveloperresearcher

Integrations

huggingface-transformers

Pricing & Plans

Open Source

Free

FAQs

What are the hardware requirements to run Dream?

Running the Dream model requires a GPU with at least 20GB of memory. The implementation is based on the Huggingface transformers library, and specific versions of transformers (4.46.2) and PyTorch (2.5.1) are recommended for full compatibility.

Can Dream be fine-tuned on custom datasets?

Yes, Dream supports fine-tuning. The training code is released, allowing users to easily fine-tune the model on their own datasets. An example script for SFT training on a single node with 8 GPUs is provided.

What are Dream-Coder and DreamOn?

Dream-Coder is a fully open 7B diffusion large language model specifically designed for code generation, trained exclusively on public data. DreamOn is a component that addresses the challenges of variable-length generation and infilling within diffusion language models.

Trending

Subcategories trending in Coding & Development

Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Also listed in

This tool also appears in

Research & Education › Academic Research

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce