Search Results for "zero" - Page 7
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Showing 238 open source projects for "zero"
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eCxx
A C++ library for AVR and NodeMCU
...Java and Python based applications/tools are also included to ease the development and debugging process using the host PC. On one side, eCxx supports the original megaAVR series (ATmega328P, ATmega1284P, ATmega2560, ATmega32U4, AT90USB1286, etc.) as well as the new megaAVR 0-Series (ATmega4808, ATmega4809, etc.). On the other side, while eCxx is primarily designed to work in NodeMCU, it should also support other ESP-12 based design as well as ESP-01 based design. The Makefile build system and some of the tools would only run on Linux. However, the library source code should be portable enough to be built using other build systems for AVR and NodeMC -
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Detic
Code release for "Detecting Twenty-thousand Classes
...A shared region proposal backbone feeds a flexible classification head that can expand to tens of thousands of categories without exhaustive box annotations. The system supports zero- or few-shot extension to novel categories via semantic embeddings and class name supervision, making “open-world” detection practical. Built on Detectron2, the repo includes configs, pretrained weights, and conversion tools to mix fully and weakly supervised sources. Detic is especially useful for applications where label space is vast and long-tailed, but dense bounding-box annotation is infeasible. -
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Petals
Run 100B+ language models at home, BitTorrent-style
...Beyond classic language model APIs — you can employ any fine-tuning and sampling methods, execute custom paths through the model, or see its hidden states. You get the comforts of an API with the flexibility of PyTorch. You can also host BLOOMZ, a version of BLOOM fine-tuned to follow human instructions in the zero-shot regime — just replace bloom-petals with bloomz-petals. Petals runs large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. -
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..."Modelling the geometry of textile reinforcements for composites: TexGen", Chapter 8 in "Composite reinforcements for optimum performance (Second Edition)", ed. P Boisse, Woodhead Publishing Ltd, 2021, ISBN: 978-0-12-819005-0. https://doi.org/10.1016/B978-0-12-819005-0.00008-3 Lin, H., Brown, L. P. & Long, A. C. 2011. Modelling and Simulating Textile Structures using TexGen. Advanced Materials Research, 331, 44-47. To reference version 3.13.0 please use: Louise Brown, mike-matveev, & georgespackman. (2023). louisepb/TexGen: TexGen v3.13.1 (v3.13.1). ...
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PRM800K
800,000 step-level correctness labels on LLM solutions to MATH problem
...Data are stored as newline-delimited JSONL files tracked with Git LFS, where each line is a full solution sample that can contain many step-level labels and rich metadata such as labeler UUIDs, timestamps, generation identifiers, and quality-control flags. Each labeled step can include multiple candidate completions with ratings of -1, 0, or +1, optional human-written corrections (phase 1), and a chosen completion index, along with a final finish reason such as found_error, solution, bad_problem, or give_up. -
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Swiple
Swiple enables you to easily observe, understand, validate data
...With automated data analysis and profiling, scheduling and alerting, teams can resolve data quality issues before they impact mission critical resources. Experience hassle-free integration with Swiple's zero-infrastructure and zero-code setup. Seamlessly incorporate data quality checks into your existing workflows without any coding or infrastructure changes, allowing you to focus on what matters most - your data. Save engineers weeks of time generating data quality checks. Swiple analyzes your dataset and builds data quality checks based on what is observed in your data. ... -
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VALL-E
PyTorch implementation of VALL-E (Zero-Shot Text-To-Speech)
...VALL-E emerges in-context learning capabilities and can be used to synthesize high-quality personalized speech with only a 3-second enrolled recording of an unseen speaker as an acoustic prompt. Experiment results show that VALL-E significantly outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity. In addition, we find VALL-E could preserve the speaker's emotion and acoustic environment of the acoustic prompt in synthesis. -
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nanogpt-lecture
Code created in the Neural Networks: Zero To Hero video lecture
nanogpt-lecture contains the code developed during the nanoGPT installment of Andrej Karpathy's Neural Networks: Zero to Hero lecture series. It is designed to accompany the video so students can inspect, modify, and rerun the implementation themselves. The repository begins with a simple character-level bigram language model and develops toward a GPT-style transformer. Its compact Python code exposes core ideas such as tokenization, embeddings, attention, transformer blocks, training, and text generation. ... -
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Flame Auto Clicker
Fastest Auto Clicker on the market . Over 1000 CPS (For Real)
...This Auto Clicker can be used with ANY game, Minecraft, Roblox and more! Source Code: GitHub: https://github.com/PETEROLO291/Flame-Auto-Clicker TIP: Set delay to 0 to get super fast clicks. Clicks are only limited by your PC hardwere! Installer: 9,22 MB Program: 27,3 MB Version: 1.6.1 New in Version 1.6.1: -Fixed the bug that caused the Settings window to appear under the main window -Now you can start and stop with the same hotkey -Other minor bugs have been fixed -Fixed a bug that didn't allow the program work with normality if the "Click until stopped" box was checked If you liked this project consider buying me a coffee ☕ Buy me a coffee ☕: https://www.paypal.me/PeterJSM Consider trying Ozyr: https://sourceforge.net/projects/ozyr/ -
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PromptSource
Toolkit for creating, sharing and using natural language prompts
PromptSource is a toolkit for creating, sharing and using natural language prompts. Recent work has shown that large language models exhibit the ability to perform reasonable zero-shot generalization to new tasks. For instance, GPT-3 demonstrated that large language models have strong zero- and few-shot abilities. FLAN and T0 then demonstrated that pre-trained language models fine-tuned in a massively multitask fashion yield even stronger zero-shot performance. A common denominator in these works is the use of prompts which has gained interest among NLP researchers and engineers. ... -
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m2cgen
Transform ML models into a native code
m2cgen (Model 2 Code Generator) - is a lightweight library that provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#, Rust, Elixir). Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies. Some models force input data to be particular type during prediction phase in their native Python libraries. Currently, m2cgen works only with float64 (double) data type. You can try to cast your input data to another type manually and check results again. Also, some small differences can happen due to specific implementation of floating-point arithmetic in a target language. -
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bert4keras
Keras implement of transformers for humans
...The original intention of this project is for the convenience of modification and customization, so it may be updated frequently. Load the pre-trained weights of bert/roberta/albert for fine-tune. Implement the attention mask required by the language model and seq2seq. Pre-training code from zero (supports TPU, multi-GPU, please see pertaining). Compatible with keras, tf.keras. -
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EasyNLP
EasyNLP: A Comprehensive and Easy-to-use NLP Toolkit
...It is seamlessly integrated to Platform of AI (PAI) products, including PAI-DSW for development, PAI-DLC for cloud-native training, PAI-EAS for serving, and PAI-Designer for zero-code model training. -
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Voided-Git AutoTyper
A simple autotyper that can be used to send multiple messages
A simple autotyper that can be used to send multiple messages at the same time or with a slight delay. Run, enter the text you want repeatedly typed, enter the amount of time you want the text repeated (or sent) and enter the delay (can be 0). Please use this software responsibly as some applications can terminate your account by spamming messages (as per their individual ToS). No responsibility is held should your account be suspended, rate-limited or terminated. Most applications, however will allow you to send several hundred messages by ratelimiting you (such as Discord, you can send upwards of 1000 messages, but it'll take a few minutes for them all to send). -
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Mocking Bird
Clone a voice in 5 seconds to generate arbitrary speech in real-time
MockingBird is an open-source voice cloning and real-time speech generation toolkit that lets you clone a speaker’s voice from a short audio sample (reportedly as little as 5 seconds) and then synthesize arbitrary speech in that voice. It builds on deep-learning based TTS / voice-cloning technology (in the lineage of projects such as Real-Time-Voice-Cloning), but extends it with support for Mandarin Chinese and multiple Chinese speech datasets — broadening its applicability beyond English.... -
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VCPlayerBot
Telegram bot to stream videos in telegram voicechat for both groups
Telegram bot to stream videos in telegram voice chat for both groups and channels. Supports live streams, YouTube videos and telegram media. With record stream support, Schedule streams, and many more. -
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gpt-j-api
API for the GPT-J language mode. Including a FastAPI backend
An API to interact with the GPT-J language model and variants! You can use and test the model in two different ways. These are the endpoints of the public API and require no authentication. Just SSH into a TPU VM. This code was tested on both the v2-8 and v3-8 variants. -
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EduData
Datasets in Education and convenient interface for dataset
...The first element of the item is the exercise id (in some works, the exercise id is not one-to-one mapped to one knowledge unit(ku)/concept, but in junyi, one exercise contains one ku) and the second one indicates whether the learner correctly answers the exercise, 0 for wrongly while 1 for correctly. -
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Texthero
Text preprocessing, representation and visualization from zero to hero
Texthero is a python package to work with text data efficiently. It empowers NLP developers with a tool to quickly understand any text-based dataset and it provides a solid pipeline to clean and represent text data, from zero to hero. -
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cryptos
Pure Python from-scratch zero-dependency implementation of Bitcoin
Cryptos is a pure Python implementation of major Bitcoin concepts built from scratch for educational purposes. It deliberately avoids external dependencies so readers can inspect how the underlying cryptography and protocol mechanics work. The project implements SHA-256, finite-field elliptic-curve mathematics, Bitcoin key generation, addresses, and ECDSA digital signatures. It can parse and inspect legacy and SegWit transaction structures, although its transaction validation is... -
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Zero Install is a decentralised cross-distribution software installation system. Create one package that works everywhere! With dependency handling and automatic updates, full support for shared libraries, and integration with native package managers
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
XLM (Cross-lingual Language Model) is a family of multilingual pretraining methods that align representations across languages to enable strong zero-shot transfer. It popularized objectives like Masked Language Modeling (MLM) across many languages and Translation Language Modeling (TLM) that jointly trains on parallel sentence pairs to tighten cross-lingual alignment. Using a shared subword vocabulary, XLM learns language-agnostic features that work well for classification and sequence labeling tasks such as XNLI, NER, and POS without target-language supervision. ... -
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Big List of Naughty Strings
List of strings which have a high probability of causing issues
The Big List of Naughty Strings is a community-maintained catalog of “gotcha” inputs that commonly break software, from unusual Unicode to SQL and script injection payloads. It exists so developers and QA engineers can easily test edge cases that normal test data would miss, such as zero-width characters, right-to-left marks, emojis, foreign alphabets, and long or malformed strings. By throwing these strings at forms, APIs, databases, and UIs, teams can discover encoding bugs, sanitizer gaps, rendering issues, and security oversights early. The list is language-agnostic and repository-friendly, meaning you can consume it from CI pipelines or local scripts with minimal setup. ... -
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Semantic Segmentation in PyTorch
Semantic segmentation models, datasets & losses implemented in PyTorch
...PyTorch v1.1 is supported (using the new supported tensoboard); can work with earlier versions, but instead of using tensoboard, use tensoboardX. Poly learning rate, where the learning rate is scaled down linearly from the starting value down to zero during training. Considered as the go-to scheduler for semantic segmentation. One Cycle learning rate, for a learning rate LR, we start from LR / 10 up to LR for 30% of the training time, and we scale down to LR / 25 for remaining time, the scaling is done in a cos annealing fashion (see Figure bellow), the momentum is also modified but in the opposite manner starting from 0.95 down to 0.85 and up to 0.95. -
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TensorFlow 2.0 Tutorials
TensorFlow 2.x version's Tutorials and Examples
TensorFlow 2.0 Tutorials is an open-source educational repository that provides practical examples and walkthroughs for learning deep learning using the TensorFlow 2.x framework. The repository contains a large set of hands-on tutorials that demonstrate how to build neural networks and machine learning systems with modern TensorFlow APIs. These examples cover a wide range of topics including convolutional neural networks, recurrent neural networks, generative adversarial networks,...
