Search Results for "run time java"

TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle.

...It is built to operate end-to-end: planning a research strategy, gathering sources, reasoning over conflicting claims, and writing a grounded response. The repository includes evaluation results on multi-step QA and research benchmarks, illustrating how web-time context boosts accuracy. Because the system is modular, you can swap the search component, reader, or policy to fit private deployments or different data domains. It’s aimed at developers who want a transparent, hackable research agent they can run locally or wire into existing workflows.

gpt-oss-safeguard is an open-weight reasoning model family released by OpenAI designed specifically for content safety and moderation tasks. Rather than just outputting a numeric “safety score,” it is trained to reason about content with respect to a user-provided policy, allowing flexible, customizable moderation definitions rather than fixed rules — ideal when different platforms have different safety standards. The model comes in at least two variants: a large 120B-parameter version for...

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 time. It is built as a simple frontend that developers can run locally to test and understand how Realtime API interactions work. ...

...As an FP8 instruct-fine-tuned model, it is optimized for chat, instruction following, and compact agentic tasks while maintaining strong adherence to system prompts. Despite its small size, it delivers efficient real-time performance and can run locally on a single 8GB GPU, with further memory reductions through quantization. It supports dozens of languages across major global regions, making it well-suited for multilingual and embedded applications. The model also provides function calling, clean JSON output, and stable tool-use behavior, enabling it to serve as a small but effective agentic system.

Ministral 3 3B Base 2512 is the smallest model in the Ministral 3 family, offering a compact yet capable multimodal architecture suited for lightweight AI applications. It combines a 3.4B-parameter language model with a 0.4B vision encoder, enabling both text and image understanding in a tiny footprint. As the base pretrained model, it is not fine-tuned for instructions or reasoning, making it the ideal foundation for custom post-training, domain adaptation, or specialized downstream tasks....