Search Results for "parallel language" - Page 3
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Showing 116 open source projects for "parallel language"
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Desktop and Mobile Device Management SoftwareDesktop Central is a unified endpoint management (UEM) solution that helps in managing servers, laptops, desktops, smartphones, and tablets from a central location.
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DEVS-Suite Simulator
Component, CA, and CCA models; superdense time, DB repo, testing, etc.
Integrated component-based and cellular automata (CA) Parallel DEVS simulator: https://acims.asu.edu/devs-suite/ OFFERS: 1) synchronized execution & animation, 2) run-time linear/superdense I/O & state trajectories, 3) Action Level Real-Time modeling & simulation, 4) model checking, 5) ABM, 6) CA & composable CA playback, 7) KIB interaction modeling, 8) hierarchical model libraries, 9) Black-Box testing & debugging, 10) PostgreSQL repository, 11) FMU (OpenModelica), 12) OSATE (AADL)... -
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Petals
Run 100B+ language models at home, BitTorrent-style
Parallel inference reaches hundreds of tokens/sec. 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. -
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Medusa
Framework for Accelerating LLM Generation with Multiple Decoding Heads
Medusa is a framework aimed at accelerating the generation capabilities of Large Language Models (LLMs) by employing multiple decoding heads. This approach allows for parallel processing during text generation, significantly enhancing throughput and reducing response times. Medusa is designed to be simple to implement and integrates with existing LLM infrastructures, making it a practical solution for scaling LLM applications. -
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text-dedup
All-in-one text de-duplication
...This is especially useful for NLP tasks where duplicated training data can skew model performance. text-dedup scales to billions of documents and offers tools for chunking, hashing, and comparing text efficiently with low memory usage. It supports Jaccard similarity thresholding, parallel execution, and flexible deduplication strategies, making it ideal for cleaning web-scraped data, language model training datasets, or document archives. -
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Transducers.jl
Efficient transducers for Julia
Transducers are transformations of "sequence" of input that can be composed very efficiently. The interface used by transducers naturally describes a wide range of processes that is expressible as a succession of steps. Furthermore, transducers can be defined without specifying the details of the input and output (collections, streams, channels, etc.) and therefore achieves a full reusability. Transducers are introduced by Rich Hickey, the creator of the Clojure language. His Strange Loop... -
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Nunjucks
A rich and powerful templating language for JavaScript
A powerful templating engine with inheritance, asynchronous control, and more (jinja2 inspired). You've been looking for a more sophisticated templating engine for JavaScript. Here it is. Rich Powerful language with block inheritance, autoescaping, macros, asynchronous control, and more. Heavily inspired by jinja2. Fast & Lean High-performant. Small 8K gzipped runtime with precompiled templates in the browser. Crazy extensible with custom filters and extensions. Everywhere available in node... -
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GPT-NeoX
Implementation of model parallel autoregressive transformers on GPUs
This repository records EleutherAI's library for training large-scale language models on GPUs. Our current framework is based on NVIDIA's Megatron Language Model and has been augmented with techniques from DeepSpeed as well as some novel optimizations. We aim to make this repo a centralized and accessible place to gather techniques for training large-scale autoregressive language models, and accelerate research into large-scale training. For those looking for a TPU-centric codebase, we... -
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TextBox
A text generation library with pre-trained language models github.com
TextBox 2.0 is an up-to-date text generation library based on Python and PyTorch focusing on building a unified and standardized pipeline for applying pre-trained language models to text generation. From a task perspective, we consider 13 common text generation tasks such as translation, story generation, and style transfer, and their corresponding 83 widely-used datasets. From a model perspective, we incorporate 47 pre-trained language models/modules covering the categories of general, translation, Chinese, dialogue, controllable, distilled, prompting, and lightweight models (modules). ... -
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OK
Welcome to the future of programming languages
...The language emphasises readability and pushing logic out into functions so cases remain simple. It includes concurrency via a map function that executes callbacks in parallel. The project is illustrative of Duffield’s vision: code should feel “magical to write” by removing what is unnecessary. -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. ... -
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Mycat2
MySQL Proxy using Java NIO based on Sharding SQL, Calcite
...Support parallel extraction of result sets, support the automatic transfer of back-end result sets, support multiple routing notes, and optimizer notes. Parameterization of the requested SQL, cache physics execution plan, and request of the same parameterization SQL, will avoid some analysis and optimization processes. -
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GPT Neo
An implementation of model parallel GPT-2 and GPT-3-style models
An implementation of model & data parallel GPT3-like models using the mesh-tensorflow library. If you're just here to play with our pre-trained models, we strongly recommend you try out the HuggingFace Transformer integration. Training and inference is officially supported on TPU and should work on GPU as well. This repository will be (mostly) archived as we move focus to our GPU-specific repo, GPT-NeoX. NB, while neo can technically run a training step at 200B+ parameters, it is very... -
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FARM
Fast & easy transfer learning for NLP
...With FARM you can build fast proofs-of-concept for tasks like text classification, NER or question answering and transfer them easily into production. Easy fine-tuning of language models to your task and domain language. AMP optimizers (~35% faster) and parallel preprocessing (16 CPU cores => ~16x faster). Modular design of language models and prediction heads. Switch between heads or combine them for multitask learning. Full Compatibility with HuggingFace Transformers' models and model hub. Smooth upgrading to newer language models. ... -
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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. -
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gluon
A static, type inferred and embeddable language written in Rust
...Marshalling values to and from gluon requires next to no boilerplate, allowing functions defined in Rust to be directly passed to gluon. Gluon supports Unicode out of the box with utf-8 encoded strings and Unicode codepoints as characters. Gluon is a garbage-collected language but uses a separate heap for each executing gluon thread. This keeps each heap small, reducing the overhead of the garbage collector. Gluon is written in Rust, which guarantees thread safety. Gluon keeps the same guarantees, allowing multiple gluon programs to run in parallel. -
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Accelerate
Embedded language for high-performance array computations
...Embedded in the advanced functional programming language Haskell, Accelerate programs are declarative, statically-typed, pure, functional, and ready to exploit all of the performance of modern parallel hardware. The combination of a strong type system, high-level code, and interactive development environment, allows you to develop code quickly with the confidence that it is correct. -
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Saturn
The vip.com's distributed job scheduling platform
Saturn is a platform created by VIP.com to provide a distributed, fault-tolerant and high available job scheduling service. -
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Idris-dev
A Dependently Typed Functional Programming Language
Idris‑dev is the development version of Idris 1, a general-purpose functional programming language featuring full dependent types, designed for writing type-safe programs and proofs within the language itself. It compiles to C and JavaScript (for Node.js and browsers), and supports code generation via substitute backends. This repository represents the latest development version of the language, and may contain bugs that are being actively worked on. For those who wish to use a more stable... -
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Cubature.jl
One- and multi-dimensional adaptive integration routines for Julia
This module provides one- and multi-dimensional adaptive integration routines for the Julia language, including support for vector-valued integrands and facilitation of parallel evaluation of integrands, based on the Cubature Package by Steven G. Johnson. Adaptive integration works by evaluating the integrand at more and more points until the integrand converges to a specified tolerance (with the error estimated by comparing integral estimates with different numbers of points). ... -
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YouTubeCrawler
Go-based automation utility that downloads YouTube videos
This tool is a Go-based automation utility that downloads YouTube videos and permanently embeds or “hard-codes” their subtitles (typically English) into MP4 output files. The workflow involves specifying one or more URLs (via a simple “url” text file in each folder) and the program uses youtube-dl to fetch video and subtitle, then ffmpeg to overlay the subtitles onto the video track. The architecture follows a command-pattern setup: tasks implement a common interface and are scheduled and... -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
...The project also provides scripts to fetch and preprocess monolingual data, learn BPE codes, and train cross-lingual embeddings that bootstrap unsupervised alignment between languages. Beyond the core EMNLP 2018 setup, the codebase exposes additional, optional capabilities such as multi-language training, language model pretraining with shared parameters, and adversarial training. -
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Pipelines
An experimental programming language for data flow
Pipelines is a language and runtime for crafting massively parallel pipelines. Unlike other languages for defining data flow, the Pipeline language requires the implementation of components to be defined separately in the Python scripting language. This allows the details of implementations to be separated from the structure of the pipeline while providing access to thousands of active libraries for machine learning, data analysis, and processing. -
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Skylark
Skylark in Go: the Skylark configuration language
Skylark, now known as Starlark, is an interpreter for a Python-like language implemented in Go. It is designed as a lightweight, deterministic, and embeddable configuration and scripting language ideal for use within larger applications. Skylark maintains Python’s familiar syntax and high-level data types while omitting features that could cause nondeterminism, such as concurrency and dynamic module imports. -
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OpenSeq2Seq
Toolkit for efficient experimentation with Speech Recognition
...Its core goal is to give researchers a flexible, modular framework for building and training encoder–decoder architectures while fully leveraging distributed and mixed-precision training. The toolkit includes ready-made models for neural machine translation, automatic speech recognition, speech synthesis, language modeling, and additional NLP tasks such as sentiment analysis. It supports multi-GPU and multi-node data-parallel training, and integrates with Horovod to scale out across large GPU clusters. Mixed-precision support (float16) is optimized for NVIDIA Volta and Turing GPUs, allowing significant speedups and memory savings without sacrificing model quality. ... -
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TensorFlow-ZH
Chinese version of the official document of TensorFlow
The tensorflow-zh repository is a Chinese translation of the official TensorFlow documentation, organized to make the core guides, tutorials, and reference material accessible to Chinese speakers. It was initiated shortly after TensorFlow’s open-sourcing, with translation and proofreading contributions from a community of volunteers who aimed to bridge the language barrier for learners in China and other Mandarin communities. The repo mirrors the structure of the original English docs:...
