Search Results for "flash"

...The repository typically serves as a launch point for running the model, understanding its intended use cases, and reproducing or extending its evaluation on reasoning and agent-style tasks. In short, MiMo-V2-Flash targets the “high-speed, high-competence” lane for modern LLM applications.

Tencent-Hunyuan-Large is the flagship open-source large language model family from Tencent Hunyuan, offering both pre-trained and instruct (fine-tuned) variants. It is designed with long-context capabilities, quantization support, and high performance on benchmarks across general reasoning, mathematics, language understanding, and Chinese / multilingual tasks. It aims to provide competitive capability with efficient deployment and inference. FP8 quantization support to reduce memory usage...

GLM-4.6V represents the latest generation of the GLM-V family and marks a major step forward in multimodal AI by combining advanced vision-language understanding with native “tool-call” capabilities, long-context reasoning, and strong generalization across domains. Unlike many vision-language models that treat images and text separately or require intermediate conversions, GLM-4.6V allows inputs such as images, screenshots or document pages directly as part of its reasoning pipeline — and...

Earnest is a A PHP-MYSQL AI EXPERT SYSTEM for creating powerful expert system easily. Its simple PHP API can also be called from JAVA, browsers or other software to perform GIS and other comparisons. Also has ability to create XML results.

The FlashServer is a part of the MASSim project of the Computational Intelligence Group at the TU Clausthal, Germany and his main task is the visualization of the games as Flash videos. Currently the FS is not in use during the Multi-Agent Contest.

"Netlands Project" is a <b>Graphical MUD engine</b>, it comes with a distributed world server, an advanced IA server, a Macromedia Flash MX client and a predefined world named WOARPS, full of ready to go adventures.

...OpenVLA is MIT-licensed, fully open-source, and designed collaboratively by Stanford, Berkeley, Google DeepMind, and TRI. Deployment is facilitated via Python and Hugging Face tools, with flash attention support for efficient inference.