AI Model / On-device / Edge LLM
An open 45M-parameter agentic LLM for tiny devices, built for tool calling, device use, and structured extraction. It ships as a 14 MB binary and is positioned for phones, wearables, smart home devices, robots, and other low-RAM hardware.
The page clearly presents a real AI product with a specific use case: an on-device agentic model for tool calling, device control, and structured extraction. It includes concrete evidence of size, RAM use, supported device classes, performance claims, licensing, and a playground/demo, which makes it useful for a public listing.
The crawl does not show the full docs, model access flow, or installation steps in enough detail to tell how a builder would integrate it end to end from the page alone.
Last evaluated 31 days ago. Current rank #705. Down 3 spots in the rankings.
A 150M ModernBERT-based token classification model that extracts verbatim evidence spans for RAG and related pipelines, reducing the need for an LLM call to highlight answers.
JetBrains introduces Mellum2, an open-source 12B model built for software engineering workflows, routing, Q&A, RAG, sub-agents, and private deployment.
An open-source 26M parameter function-calling model designed to run on very small devices, with a local quickstart, Python usage, and a playground for testing and finetuning.
A vision-language foundation model for autonomous driving that combines 3D perception, driving VQA, and motion planning in one framework. The repo shows performance tables, model structure, and links to the paper, blog, and model downloads.
