Awesome-Transformers

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awesome-transformers is an Open Source tool that provides a curated list of awesome transformer models. It serves as a resource for AI researchers and practitioners looking for various transformer architectures and implementations.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is awesome-transformers?

awesome-transformers is a curated list of transformer models, serving as a valuable resource for AI researchers and practitioners. The repository categorizes models into Encoder, Decoder, Encoder+Decoder, Multimodal, Vision, Audio, and Recommendation types, making it easy to navigate. Each entry includes the model name, a short description, links to the model and paper, the source company or group, and the model license. This comprehensive list helps users identify suitable models for fine-tuning and provides crucial information regarding their open-source status and commercial use restrictions.

Best used for

Ideal for developers and data scientists who need to explore a wide range of transformer models, understand their capabilities, and identify suitable architectures for specific tasks. Especially valuable for those looking for open-source options and detailed licensing information for research and development.

Common actions

discover transformer models

research AI models

find open-source models

open-sourceautomated workflowdeepfakeworkflowslow-code/no-codecollaboration"AI Agents"github copilotface swapping

Capabilities

Key features

Curated transformer model list
Categorized model types
Model descriptions
Links to papers
License information

Target Audience

developerdata scientist

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What types of transformer models are listed in awesome-transformers?

The list includes various categories such as Encoder (e.g., BERT, RoBERTa), Decoder (e.g., GPT, LLaMa), Encoder+Decoder (e.g., T5, Bart), Multimodal (e.g., CLIP, Donut), Vision (e.g., DiT, DETR), Audio (e.g., Whisper, VALL-E), Recommendation (P5), and Grounded Situation Recognition models.

What information is provided for each transformer model entry?

Each entry typically includes the model's name, a brief description (often from its paper), links to the model (e.g., HuggingFace or GitHub) and its research paper, the original source company or group, and the specific model license, which helps in understanding usage restrictions.

Does awesome-transformers only list fully open-source models?

No, the repository aims to make users aware of models that are not entirely open source, such as those with non-commercial licenses or other restrictions. It tracks the original source and license to provide clarity on usage terms.

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