Search Results for "parallel language"
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25 projects for "parallel language" with 2 filters applied:
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The Most Powerful Software Platform for EHSQ and ESG ManagementChoose from a complete set of software solutions across EHSQ that address all aspects of top performing Environmental, Health and Safety, and Quality management programs.
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Skillfully - The future of skills based hiringSkillfully transforms hiring through AI-powered skill simulations that show you how candidates actually perform before you hire them. Our platform helps companies cut through AI-generated resumes and rehearsed interviews by validating real capabilities in action. Through dynamic job specific simulations and skill-based assessments, companies like Bloomberg and McKinsey have cut screening time by 50% while dramatically improving hire quality.
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multi-agent-shogun
Samurai-inspired multi-agent system for Claude Code
multi-agent-shogun is a multi-agent orchestration system designed to coordinate multiple AI coding agents working in parallel. Inspired by the hierarchy of a feudal Japanese military structure, the system organizes agents into roles such as Shogun, Karo, and Ashigaru, which correspond to strategist, coordinator, and worker agents. A user interacts primarily with the Shogun agent by issuing natural language instructions that describe the desired tasks. -
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LLMCompiler
An LLM Compiler for Parallel Function Calling
LLMCompiler is an open-source framework designed to optimize how large language models orchestrate multiple external tool or function calls during complex reasoning tasks. Traditional LLM agent systems typically execute tool calls sequentially, which can create latency, higher costs, and reduced reliability when solving multi-step problems. LLMCompiler addresses this limitation by applying principles from classical compilers to analyze a task and construct an execution plan that allows multiple functions to run in parallel whenever possible. ... -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
MiroFish is a next-generation artificial intelligence prediction engine that leverages multi-agent technology and swarm-intelligence simulation to model, simulate, and forecast complex real-world scenarios. The system extracts “seed” information from sources such as breaking news, policy documents, and market signals to construct a high-fidelity digital parallel world populated by thousands of virtual agents with independent memory and behavior rules. Users can inject variables or conditions... -
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ChainForge
An open-source visual programming environment
ChainForge is an open-source visual programming environment designed to help developers systematically test, compare, and evaluate prompts and outputs across multiple large language models in a structured and scalable way. Instead of relying on isolated prompt experimentation, it introduces a dataflow-based interface that allows users to create complex prompt pipelines and evaluate them across different models, parameters, and datasets simultaneously. The platform enables rapid experimentation by generating permutations of prompts and inputs, making it possible to test hundreds of variations in parallel and analyze performance trends more effectively. ... -
AestheticsPro Medical Spa SoftwareAestheticsPro is the most complete Aesthetics Software on the market today. HIPAA Cloud Compliant with electronic charting, integrated POS, targeted marketing and results driven reporting; AestheticsPro delivers the tools you need to manage your medical spa business. It is our mission To Provide an All-in-One Cutting Edge Software to the Aesthetics Industry.
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Megatron
Ongoing research training transformer models at scale
Megatron is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This repository is for ongoing research on training large transformer language models at scale. We developed efficient, model-parallel (tensor, sequence, and pipeline), and multi-node pre-training of transformer based models such as GPT, BERT, and T5 using mixed precision. Megatron is also used in NeMo Megatron, a framework to help enterprises overcome the challenges of building and training sophisticated natural language processing models with billions and trillions of parameters. ... -
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wllama
WebAssembly binding for llama.cpp - Enabling on-browser LLM inference
wllama is a WebAssembly-based library that enables large language model inference directly inside a web browser. Built as a binding for the llama.cpp inference engine, the project allows developers to run LLM models locally without requiring a server backend or dedicated GPU hardware. The library leverages WebAssembly SIMD capabilities to achieve efficient execution within modern browsers while maintaining compatibility across platforms. By running models locally on the user’s device, wllama... -
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Xtuner
A Next-Generation Training Engine Built for Ultra-Large MoE Models
Xtuner is a large-scale training engine designed for efficient training and fine-tuning of modern large language models, particularly mixture-of-experts architectures. The framework focuses on enabling scalable training for extremely large models while maintaining efficiency across distributed computing environments. Unlike traditional 3D parallel training strategies, XTuner introduces optimized parallelism techniques that simplify scaling and reduce system complexity when training massive models. ... -
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Parallax
Parallax is a distributed model serving framework
Parallax is a decentralized inference framework designed to run large language models across distributed computing resources. Instead of relying on centralized GPU clusters in data centers, the system allows multiple heterogeneous machines to collaborate in serving AI inference workloads. Parallax divides model layers across different nodes and dynamically coordinates them to form a complete inference pipeline. A two-stage scheduling architecture determines how model layers are allocated to... -
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DFlash
Block Diffusion for Ultra-Fast Speculative Decoding
DFlash is an open-source framework for ultra-fast speculative decoding using a lightweight block diffusion model to draft text in parallel with a target large language model, dramatically improving inference speed without sacrificing generation quality. It acts as a “drafter” that proposes likely continuations which the main model then verifies, enabling significant throughput gains compared to traditional autoregressive decoding methods that generate token by token. -
Award-Winning Medical Office Software Designed for Your SpecialtyRXNT is an ambulatory healthcare technology pioneer that empowers medical practices and healthcare organizations to succeed and scale through innovative, data-backed, AI-powered software.
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MiniMax-01
Large-language-model & vision-language-model based on Linear Attention
MiniMax-01 is the official repository for two flagship models: MiniMax-Text-01, a long-context language model, and MiniMax-VL-01, a vision-language model built on top of it. MiniMax-Text-01 uses a hybrid attention architecture that blends Lightning Attention, standard softmax attention, and Mixture-of-Experts (MoE) routing to achieve both high throughput and long-context reasoning. It has 456 billion total parameters with 45.9 billion activated per token and is trained with advanced parallel strategies such as LASP+, varlen ring attention, and Expert Tensor Parallelism, enabling a training context of 1 million tokens and up to 4 million tokens at inference. ... -
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Atropos
Language Model Reinforcement Learning Environments frameworks
...It provides foundational tooling for asynchronous RL loops where environment services communicate with trainers and inference engines, enabling complex workflow orchestration in distributed and parallel setups. This framework facilitates experimentation with RLHF (Reinforcement Learning from Human Feedback), RLAIF, or multi-turn training approaches by abstracting environment logic, scoring, and logging into reusable components. -
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WorkAny
Desktop Agent for Any Task
WorkAny is an open-source desktop AI agent application that executes generic tasks through natural language commands, effectively bringing intelligent automation into everyday workflows without needing to write code manually. It acts as a unified environment where users can ask the AI to generate documents, presentations, websites, spreadsheets, organize files, or write code — all with real-time streaming outputs directly in the app, so you see results as the AI produces them. ... -
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Step3-VL-10B
Multimodal model achieving SOTA performance
Step3-VL-10B is an open-source multimodal foundation model developed by StepFun AI that pushes the boundaries of what compact models can achieve by combining visual and language understanding in a single architecture. Despite having only about 10 billion parameters, it delivers performance that rivals or even surpasses much larger models (10×–20× larger) on a wide range of multimodal benchmarks covering reasoning, perception, and complex tasks, positioning it as one of the most powerful... -
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ERNIE
The official repository for ERNIE 4.5 and ERNIEKit
...The project also emphasizes optimization techniques for large-scale training, including mixed-precision and hybrid-parallel strategies that are commonly needed for multi-node GPU clusters. In addition to training, it includes guidance and example materials intended to help developers adopt ERNIE models for real product scenarios rather than only research demonstrations. -
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BeeAI Framework
Build production-ready AI agents in both Python and Typescript
...The framework supports both Python and TypeScript with full feature parity, making it accessible to a wide range of developers and teams. It includes a unified backend layer that connects seamlessly to multiple large language model providers, allowing flexible deployment across different AI infrastructures without vendor lock-in. BeeAI also provides orchestration tools for designing dynamic workflows, enabling multiple agents to coordinate tasks through structured execution flows, retries, and parallel processing. -
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Functionary
Chat language model that can use tools and interpret the results
Functionary is an open-source large language model specifically designed for interpreting and executing structured functions or external tools within conversational AI systems. The model extends traditional chat-based language models by enabling them to determine when external functions should be called and how to extract the necessary parameters from natural language input. -
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Graph of Thoughts
Official Implementation of "Graph of Thoughts
...The framework executes these operations using a large language model as the reasoning engine while evaluating intermediate results to guide the search process. This approach enables models to explore multiple reasoning strategies in parallel and choose the most promising solutions during problem solving. -
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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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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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Weld
High-performance runtime for data analytics applications
Weld is a programming language and runtime designed to improve the performance of data-intensive applications by optimizing computations across multiple libraries. Instead of optimizing individual functions independently, Weld introduces an intermediate representation that allows different frameworks to share optimization opportunities. This approach reduces data movement between libraries and enables the system to generate highly optimized machine code for parallel execution. ... -
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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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popt4jlib
Parallel Optimization Library for Java
popt4jlib is an open-source parallel optimization library for the Java programming language supporting both shared memory and distributed message passing models. Implements a number of meta-heuristic algorithms for Non-Linear Programming, including Genetic Algorithms, Differential Evolution, Evolutionary Algorithms, Simulated Annealing, Particle Swarm Optimization, Firefly Algorithm, Monte-Carlo Search, Local Search algorithms, Gradient-Descent-based algorithms, as well as some well-known network flow and other graph algorithms. ... -
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CRFSharp
CRFSharp is a .NET(C#) implementation of Conditional Random Field
CRFSharp(aka CRF#) is a .NET(C#) implementation of Conditional Random Fields, an machine learning algorithm for learning from labeled sequences of examples. It is widely used in Natural Language Process (NLP) tasks, for example: word breaker, postagging, named entity recognized, query chunking and so on. CRF#'s mainly algorithm is the same as CRF++ written by Taku Kudo. It encodes model parameters by L-BFGS. Moreover, it has many significant improvement than CRF++, such as totally parallel encoding, optimizing memory usage and so on. ... -
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Sanchay is a collection of tools and APIs for language researchers. It has some implementations of NLP algorithms, some flexible APIs, several user friendly annotation interfaces and Sanchay Query Language for language resources.
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A Java port of LDC's Champollion sentence aligner (http://champollion.sourceforge.net). Intended audience is Natural Language researchers wishing to sentence-align parallel text. In the future, Akerblad will extend Champollion with such features as rule
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