Search Results for "jd-core"

Megatron-LM is a GPU-optimized deep learning framework from NVIDIA designed to train extremely large transformer-based language models efficiently at scale. The repository provides both a reference training implementation and Megatron Core, a composable library of high-performance building blocks for custom large-model pipelines. It supports advanced parallelism strategies including tensor, pipeline, data, expert, and context parallelism, enabling training across massive multi-GPU and multi-node clusters. The framework includes mixed-precision training options such as FP16, BF16, FP8, and FP4 to maximize performance and memory efficiency on modern hardware. ...

Qualify is a lightweight micro-CRM prototype and automation framework geared toward helping small teams and individual users manage leads, tasks, customer interactions, and simple sales pipelines without the overhead of traditional enterprise platforms. Rather than trying to replicate every feature of a full CRM suite, it focuses on core workflows like capturing leads, tracking stages, logging interactions, and generating reminders or alerts for follow-ups so that users spend more time building relationships and less time navigating interface complexity. Built with simplicity and extensibility in mind, the project includes REST APIs, database models, and a user interface that can be extended or embedded into existing internal systems. ...

AllenNLP makes it easy to design and evaluate new deep learning models for nearly any NLP problem, along with the infrastructure to easily run them in the cloud or on your laptop. AllenNLP includes reference implementations of high quality models for both core NLP problems (e.g. semantic role labeling) and NLP applications (e.g. textual entailment). AllenNLP supports loading "plugins" dynamically. A plugin is just a Python package that provides custom registered classes or additional allennlp subcommands. There is ecosystem of open source plugins, some of which are maintained by the AllenNLP team here at AI2, and some of which are maintained by the broader community. ...

...It is built to support both researchers and developers who want to train, evaluate, and extend GANs efficiently across diverse datasets such as CelebA-HQ, FashionGen, DTD, and CIFAR-10. In addition to core GAN training, the repository includes tools for model evaluation, such as Inception Score and SWD metrics, as well as advanced features like GDPP for diverse generation and AC-GAN conditioning for class-specific synthesis. The framework also supports “inspirational generation,” enabling style or content transfer from reference images through pre-trained models.

Parallel Content-Based matching algorithm running on multi-core CPUs and on CUDA GPUs. It includes a translator from the SIENA publish/subscribe system events and subscriptions and a large number of workloads to test its performance.

A C++ library to scan Homeric Greek (dactylic hexameter) and output the meter.

Yans is a extensible, mixed-level, Monte-Carlo Method based, discrete event network simulator. Yans is divided into interacting layers, namely: core, topology, nodes, monitors; and is driven by a config-file that describes the setup of the network.