Search Results for "app-release.apk"

...The repository provides multiple reference implementations—including PyTorch, Triton, and Metal—for educational and experimental use, as well as example clients and tools like a terminal chat app and a Responses API server.

...The repo provides training, inference, and evaluation code for MobileCLIP models trained on DataCompDR, and for newer MobileCLIP2 models trained on DFNDR. It includes an iOS demo app and Core ML artifacts to showcase practical, offline photo search and classification on iPhone-class hardware. Project notes highlight latency/accuracy trade-offs, with MobileCLIP2 variants matching or surpassing larger baselines at notably lower parameter counts and runtime on mobile devices. A companion “mobileclip-dr” repository details large-scale, distributed data-generation pipelines used to reinforce datasets across billions of samples on thousands of GPUs. ...

...Each example is designed to be easily runnable with minimal setup—requiring only Python, a virtual environment, and an API key. The repository also includes environment setup guides and example scripts, such as a simple Flask web app for chat interactions, allowing developers to test OpenAI API integrations locally. Overall, openai-quickstart-python serves as an essential starting point for developers looking to prototype and experiment with OpenAI-powered apps.

openai-realtime-console is a developer tool created by OpenAI that provides a web-based console for experimenting with the Realtime API. The Realtime API enables low-latency, interactive communication with language models, supporting use cases such as live conversations, real-time transcription, and interactive applications. This console serves as a reference implementation, showing how to establish WebRTC or WebSocket connections, send audio or text inputs, and receive model outputs in real...

Jan-v1-edge is a lightweight agentic language model developed by JanHQ, designed for fast and reliable on-device execution. It is the second release in the Jan Family and was distilled from the larger Jan-v1 model, retaining strong reasoning and problem-solving capabilities while reducing its computational footprint. The model was refined through a two-stage post-training process: Supervised Fine-Tuning (SFT) to transfer knowledge from Jan-v1, followed by Reinforcement Learning with...