OpenMOSS/Awesome-WAM
A curated collection of papers, explainers, and resources on World Action Models for embodied AI
- Stars
- 1.5k
- Forks
- 44
- Open issues
- 4
- Last push
- 1mo ago
About Awesome-WAM
Awesome-WAM is a free, open-source AI resource list by OpenMOSS, written mainly in HTML. A curated collection of papers, explainers, and resources on World Action Models for embodied AI. It has 1.5k GitHub stars and 44 forks, and was last updated 1mo ago. It's released under the MIT license, a standard open-source license.
- License
- MIT
- Language
- HTML
- Stars
- 1,495
- Last update
- 1mo ago
- Runs on Mac
- Not stated
- GPU
- Not stated
- Paid API key
- Not stated
- Self-hostable
- Not stated
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Awesome-WAM FAQ
Is Awesome-WAM free?
Yes. Awesome-WAM is open source and free to use. It's released under the MIT license, a standard open-source license.
Does Awesome-WAM run on a Mac?
The project doesn't say. Check its installation guide before you start.
Does Awesome-WAM need a GPU?
The project doesn't state hardware needs. AI models generally run much faster with a GPU.
Is Awesome-WAM still maintained?
It was last updated 1mo ago. Check recent issues on GitHub to judge how active it is.
What are alternatives to Awesome-WAM?
Similar free, open-source tools include 500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code, Prompt-Engineering-Guide, KG-LLM-Papers, h4cker.