Search Results for "express"

TensorFlow Extended (TFX) is a Google-production-scale machine learning platform based on TensorFlow. It provides a configuration framework to express ML pipelines consisting of TFX components. TFX pipelines can be orchestrated using Apache Airflow and Kubeflow Pipelines. Both the components themselves and the integrations with orchestration systems can be extended. TFX components interact with an ML Metadata backend that keeps a record of component runs, input and output artifacts, and runtime configuration. ...

Burr makes it easy to develop applications that make decisions (chatbots, agents, simulations, etc...) from simple python building blocks. Burr works well for any application that uses LLMs and can integrate with any of your favorite frameworks. Burr includes a UI that can track/monitor/trace your system in real-time, along with pluggable persisters (e.g. for memory) to save & load application state.

LOTUS is an open-source framework and query engine designed to enable efficient processing of structured and unstructured datasets using large language models. The system provides a declarative programming model that allows developers to express complex AI data operations using high-level commands rather than manually orchestrating model calls. It offers a Python interface with a Pandas-like API, making it familiar for data scientists and engineers already working with data analysis libraries. The core concept of the framework is the use of semantic operators, which extend traditional relational database operations to support reasoning over text and other unstructured data. ...

...It accomplishes this through two primitive abstractions; Agents and handoffs. An Agent encompasses instructions and tools, and can at any point choose to hand off a conversation to another Agent. These primitives are powerful enough to express rich dynamics between tools and networks of agents, allowing you to build scalable, real-world solutions while avoiding a steep learning curve. Approaches similar to Swarm are best suited for situations dealing with a large number of independent capabilities and instructions. Swarm runs (almost) entirely on the client and, much like the Chat Completions API, does not store state between calls.

vits_chinese is an implementation of the VITS end-to-end text-to-speech (TTS) architecture tailored for Chinese (and possibly multilingual) speech synthesis. VITS is a model combining variational autoencoders (VAEs), normalizing flows, adversarial learning, and a stochastic duration predictor — a design that enables generation of natural, expressive speech, capturing variations in rhythm and prosody. By customizing or porting VITS for Chinese, this project aims to produce high-quality TTS...

...This project mainly focuses on TensorFlow2 reconstruction for the Chinese version of this book. In addition, this project also refers to the project Dive-into-DL-PyTorch , which refactored PyTorch in the Chinese version of this book, and I would like to express my gratitude here. This repository mainly contains two folders, code and docs (plus some data stored in data). The code folder is the relevant jupyter notebook code for each chapter (based on TensorFlow2); the docs folder is the relevant content in the book.

CakeChat is a backend for chatbots that are able to express emotions via conversations. The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context.

Leanstral is an open-weight large language model developed by Mistral AI and specifically designed as a code agent for the Lean 4 proof assistant, enabling advanced interaction with formal mathematics and program verification systems. The model is built to understand and generate Lean 4 code, which is used to express complex mathematical constructs as well as formal software specifications. By focusing on theorem proving and formal reasoning, Leanstral represents a specialized direction within large language models, targeting domains that require strict correctness and logical rigor rather than general conversational tasks. It leverages modern large-scale architectures, likely incorporating mixture-of-experts techniques, to balance efficiency and capability while handling structured symbolic reasoning tasks. ...