Search Results for "waiting"
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Showing 13 open source projects for "waiting"
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1
ChatALL
Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vicuna, etc.
Large Language Models (LLMs) based AI bots are amazing. However, their behavior can be random, and different bots excel at different tasks. If you want the best experience, don't try them one by one. ChatALL (Chinese name: 齐叨) can send prompts to several AI bots concurrently, helping you to discover the best results. All you need to do is download, install, and ask. -
2
ChatALL
Concurrently chat with ChatGPT, Bing Chat, Bard, Alpaca, Vincuna, etc.
Concurrently chat with ChatGPT, Bing Chat, bard, Alpaca, Vincuna, Claude, ChatGLM, MOSS, iFlytek Spark, ERNIE and more, discover the best answers. Large Language Models (LLMs) based AI bots are amazing. However, their behavior can be random and different bots excel at different tasks. If you want the best experience, don't try them one by one. ChatALL (Chinese name: 齐叨) can send prompt to several AI bots concurrently, help you to discover the best results. -
3
Agentspan
Durable, Distributed runtime for ALL of your agents
...It enables agents to persist beyond process crashes, allowing workflows to continue even after interruptions or restarts. The system is built for durability, meaning tasks can pause for extended periods, including waiting for human approval, and then resume seamlessly. It supports scaling across multiple environments, making it suitable for production-grade agent orchestration. Agentspan includes a local server and visual interface that allow developers to inspect execution flows and debug agent behavior. It also integrates with multiple model providers, enabling flexibility in selecting underlying AI systems. ... -
4
Anthropic SDK Python
Provides convenient access to the Anthropic REST API from any Python 3
...The SDK supports both synchronous and asynchronous usage (via async/await) depending on context. Importantly, it also supports streaming responses via Server-Sent Events (SSE) so that large outputs can be consumed incrementally rather than waiting for the full response. The client offers helper abstractions for tools (function-style “tools”) and streaming utilities for building interactive agents. -
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5
Ollama JavaScript Library
Ollama JavaScript library
...It works in Node.js and also supports browser usage through a dedicated browser import, which broadens where it can be deployed. Streaming responses are built in, returning an async generator so applications can render output progressively instead of waiting for a full response. It also supports cloud-hosted usage by pointing the client at Ollama’s cloud endpoint with an API key, while preserving a familiar local-first workflow for developers who want to move between local and remote execution. -
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Semantic Router
Superfast AI decision making and processing of multi-modal data
Semantic Router is a superfast decision-making layer for your LLMs and agents. Rather than waiting for slow, unreliable LLM generations to make tool-use or safety decisions, we use the magic of semantic vector space — routing our requests using semantic meaning. Combining LLMs with deterministic rules means we can be confident that our AI systems behave as intended. Cramming agent tools into the limited context window is expensive, slow, and fundamentally limited. -
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TPOT
A Python Automated Machine Learning tool that optimizes ML
Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. -
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MLJAR Studio
Python package for AutoML on Tabular Data with Feature Engineering
...We developed a desktop application called MLJAR Studio. It is a notebook-based development environment with interactive code recipes and a managed Python environment. All running locally on your machine. We are waiting for your feedback. The mljar-supervised is an Automated Machine Learning Python package that works with tabular data. It is designed to save time for a data scientist. It abstracts the common way to preprocess the data, construct the machine learning models, and perform hyper-parameter tuning to find the best model. It is no black box, as you can see exactly how the ML pipeline is constructed (with a detailed Markdown report for each ML model). -
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EvaDB
Database system for building simpler and faster AI-powered application
...For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. Besides the money spent on hardware, these models also increase the time that you spend waiting for the model inference to finish. -
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10
abstract2paper
Auto-generate an entire paper from a prompt or abstract using NLP
Enter your abstract into the little doohicky here, and quicker'n you can blink your eyes1, a shiny new paper'll come right out for ya! What are you waiting for? Click the "doohicky" link above to get started, and then click the link to open the demo notebook in Google Colaboratory. To run the demo as a Jupyter notebook (e.g., locally), use this version instead. Note: to compile a PDF of your auto-generated paper (when you run the demo locally), you'll need to have a working LaTeX installation on your machine (e.g., so that pdflatex is a recognized system command). ... -
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Open Speech Corpora
A list of accessible speech corpora for ASR, TTS
...The catalog covers well-known resources such as Mozilla Common Voice, Yesno, LJ Speech and numerous Nordic and parliamentary speech corpora, along with their license variants like CC-0 and CC-BY. It is actively maintained as a community resource: users are encouraged to propose new corpora via issues, and there is a backlog of datasets waiting to be integrated. -
12
kcws
Deep Learning Chinese Word Segment
...Install the bazel code construction tool and install tensorflow (currently this project requires tf 1.0.0alpha version or above) Switch to the code directory of this project and run ./configure. Compile background service. Pay attention to the public account of waiting for words and reply to kcws to get the corpus download address. Extract the corpus to a directory. Change to the code directory.After installing tensorflow, switch to the kcws code directory. Currently, the custom dictionary is supported in the decoding stage. Please refer to kcws/cc/test_seg.cc for specific usage. The dictionary is in text format. -
13
EVLib
Management and simulation of Electric Vehicles activities
...EVLib provides a simple, yet efficient interface for the management of all major EV related activities such as the charging and dis-charging of batteries, as well as battery swapping. Moreover, a large number of parameters, such as the number of chargers, the waiting queues, and the available energy can be easily con figured. On top of this, the library supports the simultaneous operation of many EV activities through the efficient use of threads.
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