Search Results for "path-setting" - Page 5
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Showing 189 open source projects for "path-setting"
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AutoPR
Run AI-powered workflows over your codebase
AutoPR is an AI-driven tool for automating pull request (PR) generation and review processes. It streamlines code contributions by suggesting fixes, generating pull requests, and reviewing code using AI models, reducing manual overhead for developers. -
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DeepKE
An Open Toolkit for Knowledge Graph Extraction and Construction
Supporting cnSchema, standard supervised setting, low-resource setting, document-level setting and multi-modal setting for knowledge base population. DeepKE is a knowledge extraction toolkit supporting cnSchema, standard supervised, low-resource, and document-level scenarios for entity, relation, and attribution extraction. It allows developers and researchers to customize datasets and models to extract information from unstructured texts. -
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VALL-E X
Open source implementation of Microsoft's VALL-E X zero-shot TTS model
...VALL-E-X supports zero-shot cross-lingual synthesis, meaning a monolingual speaker’s voice can be used to speak other languages without additional training. It also preserves aspects of the acoustic environment, such as background noise or reverb, making the generated audio feel more like it came from the same setting as the prompt. The repository includes Python APIs, sample scripts, ready-to-use voice presets, and demos hosted on Hugging Face Spaces and Google Colab so users can try it. -
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ChatGPT UI
A ChatGPT web client that supports multiple users, and databases
...This feature is off by default, you can turn it on in `Chat->Settings` in the admin panel, there is a record `open_web_search` in Settings, set its value to True. Add "open_registration" setting option in the admin panel to control whether user registration is enabled. You can log in to the admin panel and find this setting option under Chat->Setting. The default value of this setting is True (allow user registration). If you do not need it, please change it to False. -
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5
PyTorch Implementation of SDE Solvers
Differentiable SDE solvers with GPU support and efficient sensitivity
...The example fits an SDE to data, whilst regularizing it to be like an Ornstein-Uhlenbeck prior process. The model can be loosely viewed as a variational autoencoder with its prior and approximate posterior being SDEs. The program outputs figures to the path specified by <TRAIN_DIR>. Training should stabilize after 500 iterations with the default hyperparameters. examples/sde_gan.py learns an SDE as a GAN, as in [2], [3]. The example trains an SDE as the generator of a GAN, whilst using a neural CDE [4] as the discriminator. -
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HttpRunner
Testing framework that began with API and performance testing
...Beyond HTTP(S) APIs, the ecosystem spans UI automation (via a companion UI extension), load testing, and integrations that turn the framework into a one-stop solution for functional and performance needs. The repo includes examples, a migration path from older versions, and badges for CI health and coverage to encourage production adoption. Its community presence and long history (since 2017) make it a common choice in Chinese and global QA teams looking for an all-in-one runner. -
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D2L.ai
Interactive deep learning book with multi-framework code
...The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code. Offers sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist. -
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LLM Cookbook
LLM Introduction Tutorial for Developers, Chinese version
LLM Cookbook is an open-source learning repository designed to help developers understand how to build applications powered by large language models through practical examples and translated course material. The project adapts and reproduces content from widely known LLM developer courses and reorganizes it into a structured learning path tailored for developers who want to build real AI applications. It covers the essential topics required to start working with LLM APIs and frameworks, including prompt engineering, application architecture, retrieval-augmented generation, and system evaluation. The repository includes practical coding examples that demonstrate how to integrate language models with tools such as LangChain and other common AI development frameworks. ... -
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finetuner
Task-oriented finetuning for better embeddings on neural search
Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive. Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. ... -
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Repo of Tree of Thoughts (ToT)
Implementation of "Tree of Thoughts
Language models are increasingly being deployed for general problem-solving across a wide range of tasks, but are still confined to token-level, left-to-right decision-making processes during inference. This means they can fall short in tasks that require exploration, strategic lookahead, or where initial decisions play a pivotal role. To surmount these challenges, we introduce a new framework for language model inference, Tree of Thoughts (ToT), which generalizes over the popular Chain of... -
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Audio Webui
A webui for different audio related Neural Networks
...For more advanced users, it exposes a rich set of command-line flags to control behavior such as skipping installation, disabling venv, changing model cache directories, sharing Gradio links, setting passwords, and specifying themes or ports. -
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Chameleon LLM
Codes for "Chameleon: Plug-and-Play Compositional Reasoning
...By integrating various tools such as vision models, web search engines, Python functions, and rule-based modules, Chameleon delivers more accurate, up-to-date, and precise responses, making it a game-changer in the natural language processing landscape. With GPT-4 at its core, Chameleon has showcased exceptional improvements in accuracy on benchmark tasks, outperforming competitors and setting new industry standards. -
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wechat-chatgpt
Use ChatGPT On Wechat via wechaty
Use ChatGPT On Wechat via wechaty Interact with WeChat and ChatGPT: Use ChatGPT on WeChat with wechaty and Official API Add conversation support Support command setting Deployment and configuration options: Add Dockerfile, deployable with docker Support deployment using docker compose Support Railway and Fly.io deployment Other features: Support Dall·E Support whisper Support setting prompt Support proxy (in development) -
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chatgpt-web
Privatized web program based on ChatGPT3.5 API
This project can deploy your own customized ChatGPT web program (compatible with gpt3.5) with one click, just download the project file of the corresponding platform in the release, modify the configuration and execute it, open http://127.0.0.1:8080, you can Get your own ChatGPT website. Note that each parameter may affect you to get a different chat effect, and you may get another answer if you change a parameter, so please try to debug it yourself, and don't complain about the artificial... -
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Learn Prompting
This website is a free, open-source guide on prompt engineering
This website is a free, open-source guide on prompt engineering. Contributions are welcome! Harsh criticism is welcome too. We launched the first ever prompt hacking competition designed to enhance AI safety and education by challenging participants to outsmart large language models from May 5th to June 3rd! The competition featured 10 increasingly difficult levels of prompt hacking defenses and the chance to win over $35,000 in prizes. Coding is a great skill to learn alongside prompt... -
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NOW
No-code tool for creating a neural search solution in minutes
...You may either choose a demo dataset hosted by NOW, or use your own custom dataset, to build an application. NOW can support your custom data in the form of a DocumentArray, as a path to a local folder, or S3 bucket. You can choose a demo dataset to get started quickly. The demo datasets are hosted by NOW which can be easily used to build a search application. There is a large variety of datasets, including images, text, and audio. Perhaps your data is stored in an S3 bucket, which is an option NOW also supports. ... -
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Self-learning-Computer-Science
Resources to learn computer science in your spare time
...The author (an undergraduate CS student) assembled links to courses from institutions like MIT, UC Berkeley, Stanford, etc., covering mathematics, programming, data structures/algorithms, computer architecture, machine learning, software engineering and more. It’s aimed at learners who find traditional course structures restrictive and want a flexible, self-paced path through CS, with a focus on building depth and breadth rather than shortcut exam skills. The repository provides a roadmap, references, teaching materials, and sometimes the author’s own project examples, offering both guidance and community support. Because the CS field is broad, the structure helps learners allocate study time, avoid duplication, and benefit from “best in class” resources instead of randomly browsing. -
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WhisperBatchRun
This batch file will run OpenAi's whisper to transcribe (or translate)
Currently, Version #1 of the batch file does the following: (1) checks of whisper is installed, and if so, starts to run; (2) Asks if you want to process sub-folders, and if an answer is not provided in 10 seconds, defaults to "N"; (3) Applies the following command to each mp3 or wav file in the folder/sub-folders: "whisper "FILENAME" --model large-v2 --output_format vtt" (4) Creates a log file in the active directory, but only if there were any errors; (5) ends. To use, simply place it in your system's "PATH." Search online how to do this if need be - i.e. to alter your environment variables. Once in the system PATH you simply open a Command Prompt (CMD) and type the batch file name (without the extension if desired). Whisper must be installed in your computer and in the system PATH as well, which will necessitate install other items as well such as Python in order for Whisper to run. ... -
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"Note that by default, three languages are selected. If you only need to recognize English, please only select English." -No installation required. It's ready to use as soon as you open it.- I have made a major upgrade to CSTT this time, including support for all Tesseract-supported languages, improved OCR accuracy, added multiple recognition modes, added keyboard shortcuts for canvas movement and zooming, and enabled users to adjust OCR settings. If you like it, please support...
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Compose
A machine learning tool for automated prediction engineering
...Prediction problems are structured by using a label maker and a labeling function. The label maker automatically extracts data along the time index to generate labels. The process starts by setting the first cutoff time after the minimum amount of data. Then subsequent cutoff times are spaced apart using gaps. Starting from each cutoff time, a window determines the amount of data, also referred to as a data slice, to pass into a labeling function. -
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Minimal text diffusion
A minimal implementation of diffusion models for text generation
...Note that you may have to increase the sequence length (--seq_len) if your corpus is longer than the simple corpus. The other default arguments are set to match the best setting I found for the simple corpus. -
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Dynacover
Dynamic Twitter images and banners
...This enables you to build cool little tricks, like showing your latest followers or GitHub sponsors, your latest content created, a qrcode to something, a progress bar for a goal, and whatever you can think of. You can run Dynacover in three different ways. As a GitHub action: the easiest way to run Dynacover is by setting it up in a public repository with GitHub Actions, using repository secrets for credentials. Follow this step-by-step guide to set this up - no coding is required. With Docker: you can use the public erikaheidi/dynacover Docker image to run Dynacover with a single command, no PHP is required. To further customize your cover, you can clone the dynacover repo to customize banner resources (JSON template and header images, both located at app/Resources), then build a local copy of the Dynacover Docker image to use your custom changes. -
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tf2_course
Notebooks for my "Deep Learning with TensorFlow 2 and Keras" course
...Rather than being book-based, it is course-based, meaning the flow, examples and structure lean toward interactive teaching and incremental builds. It’s well-suited for those who want a focused, deep-learning path rather than a broad ML textbook. -
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AcoPath for Java
Ant Colony Optimization algorithm for the shortest path problem.
The shortest path problem is solved by many methods. Heuristics offer lower complexity in expense of accuracy. There are many use cases where the lower accuracy is acceptable in return of lower consumption of computing resources. The basic idea of the Ant System is that virtual ants are exploited for finding paths with a specific property, e.g., short distance between physical nodes, in the same way nature guides physical ants. -
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ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
...You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. If you want more control, you can provide a config file.