Ternlight – 7 MB embedding model that runs in browser (WASM) (ternlight-demo.vercel.app)

Article summary

Ternlight is a 7 MB embedding model that can run in a web browser, allowing for fast and offline semantic search without the need for API calls. The model uses ternary quantization-aware training and can embed text in milliseconds. It is available as a single npm package and can be used for various applications such as search, FAQ/intent matching, and clustering. The model's performance is comparable to other small embedding models, with a claimed embedding latency of around 5 ms.

Main themes

• Embedding models
• Semantic search
• Browser-based AI
• Model compression
• Offline capabilities
• Natural language processing

What commenters say

• The model's small size and fast embedding latency make it a promising solution for offline semantic search applications.
• The use of ternary quantization-aware training is a key factor in achieving the model's high performance and small size.
• Some users are concerned about the potential for websites to use models like Ternlight to run resource-intensive tasks on users' devices without their consent.
• Others see the model as a useful tool for enabling fast and accurate search capabilities in web applications, and are interested in exploring its potential uses.
• The model's limitations, such as its primary training on English language data, may impact its effectiveness for certain use cases.
• Some users have reported issues with the model's performance on certain devices or browsers, highlighting the need for further testing and optimization.
• The development of standardized APIs for AI models like Ternlight could help to facilitate their adoption and ensure consistent performance across different platforms.
• The trade-off between model size and performance is a key consideration for developers looking to integrate AI capabilities into their web applications.