Search Results for "language learning" - Page 4
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Showing 527 open source projects for "language learning"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Enterprise-grade ITSM, for every businessFreshservice is an intuitive, AI-powered platform that helps IT, operations, and business teams deliver exceptional service without the usual complexity. Automate repetitive tasks, resolve issues faster, and provide seamless support across the organization. From managing incidents and assets to driving smarter decisions, Freshservice makes it easy to stay efficient and scale with confidence.
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OpenClaw-RL
Train any agents simply by 'talking'
OpenClaw-RL is an open-source reinforcement learning framework designed to train and personalize AI agents built on the OpenClaw ecosystem. The project focuses on enabling agents to improve their behavior through interactive learning rather than relying solely on static prompts or predefined skills. One of its key ideas is allowing users to train an AI agent simply by interacting with it conversationally, using natural language feedback to guide the learning process. ... -
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dLLM
dLLM: Simple Diffusion Language Modeling
...The project provides an integrated pipeline that standardizes how diffusion language models are trained, evaluated, and deployed, helping researchers reproduce experiments and compare results more easily. The framework includes scalable training infrastructure inspired by modern deep learning toolkits and supports integrations with widely used libraries for distributed training. -
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Atropos
Language Model Reinforcement Learning Environments frameworks
Atropos is a comprehensive open-source framework for reinforcement learning (RL) environments tailored specifically to work with large language models (LLMs). Designed as a scalable ecosystem of environment microservices, Atropos allows researchers and developers to collect, evaluate, and manage trajectories (sequences of actions and outcomes) generated by LLMs across a variety of tasks—from static dataset benchmarks to dynamic interactive games and real-world scenario environments. ... -
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AIDE ML
AI-Driven Exploration in the Space of Code
AIDE ML is an open-source research framework designed to explore automated machine learning development through agent-based search and code optimization. The project implements the AIDE algorithm, which uses a tree-search strategy guided by large language models to iteratively generate, evaluate, and refine code. Instead of relying on manual experimentation, the agent autonomously drafts machine learning pipelines, debugs errors, and benchmarks performance against user-defined evaluation metrics. ... -
Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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AgentEvolver
Towards Efficient Self-Evolving Agent System
AgentEvolver is an open-source research framework for building self-evolving AI agents powered by large language models. The system focuses on improving the efficiency and scalability of training autonomous agents by allowing them to generate tasks, explore environments, and refine strategies without heavy reliance on manually curated datasets. Its architecture combines reinforcement learning with LLM-driven reasoning mechanisms to guide exploration and learning. -
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dive-into-llms
"Dive into LLMs" series of practical programming tutorials
The dive-into-llms project is an educational resource designed to provide a comprehensive introduction to large language models and their underlying concepts. It combines theoretical explanations with practical examples, guiding users through topics such as model architecture, training processes, and inference techniques. The repository is structured as a learning pathway, making it accessible to both beginners and intermediate practitioners interested in understanding how LLMs work. ... -
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Jittor
Jittor is a high-performance deep learning framework
...The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. -
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Datasets
Hub of ready-to-use datasets for ML models
Datasets is a library for easily accessing and sharing datasets, and evaluation metrics for Natural Language Processing (NLP), computer vision, and audio tasks. Load a dataset in a single line of code, and use our powerful data processing methods to quickly get your dataset ready for training in a deep learning model. Backed by the Apache Arrow format, process large datasets with zero-copy reads without any memory constraints for optimal speed and efficiency. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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DataDreamer
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models
DataDreamer is a tool designed to assist in the generation and manipulation of synthetic data for various applications, including testing and machine learning. -
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OpenMLSys-ZH
Machine Learning Systems: Design and Implementation
...The repo mirrors the structure of the original OpenMLSys docs: sections on system design, API references, deployment instructions, module overviews, and example workflows. It helps bridge language barriers in open machine learning systems by providing side-by-side translation or localized explanations. The repository includes scripts or tooling to keep translation synchronized with upstream changes, versioning, and possibly translation metadata (contributors, timestamp). Users can browse or clone the translated documentation to follow along with the original content, deploy examples, or understand system internals in their preferred language. -
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Bolt NLP
Bolt is a deep learning library with high performance
Bolt is a high-performance deep learning inference framework developed by Huawei Noah's Ark Lab. It is designed to optimize and accelerate the deployment of deep learning models across various hardware platforms. Bolt is a light-weight library for deep learning. Bolt, as a universal deployment tool for all kinds of neural networks, aims to automate the deployment pipeline and achieve extreme acceleration. Bolt has been widely deployed and used in many departments of HUAWEI company, such as... -
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AlphaTree
DNN && GAN && NLP && BIG DATA
...The repository organizes these architectures into a structured learning path that helps learners understand how deep learning models improved over time through changes in depth, architectural complexity, and training techniques. In addition to neural networks used for image classification, the project also references broader AI fields such as generative adversarial networks, natural language processing, and graph neural networks. -
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Skywork-R1V4
Skywork-R1V is an advanced multimodal AI model series
Skywork-R1V is an open-source multimodal reasoning model designed to extend the capabilities of large language models into vision-language tasks that require complex logical reasoning. The project introduces a model architecture that transfers the reasoning abilities of advanced text-based models into visual domains so the system can interpret images and perform multi-step reasoning about them. Instead of retraining both language and vision models from scratch, the framework uses a... -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
spaCy is designed to help you do real work, to build real products, or gather real insights. The library respects your time, and tries to avoid wasting it. It's easy to install, and its API is simple and productive. spaCy excels at large-scale information extraction tasks. It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry... -
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BitNet
BitNet: Scaling 1-bit Transformers for Large Language Models
BitNet is a machine learning research implementation that explores extremely low-precision neural network architectures designed to dramatically reduce the computational cost of large language models. The project implements the BitNet architecture described in research on scaling transformer models using extremely low-bit quantization techniques. In this approach, neural network weights are quantized to approximately one bit per parameter, allowing models to operate with far lower memory usage than traditional 16-bit or 32-bit neural networks. ... -
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Weaviate
Weaviate is a cloud-native, modular, real-time vector search engine
Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. ... -
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AutoTrain Advanced
Faster and easier training and deployments
AutoTrain Advanced is an open-source machine learning training framework developed by Hugging Face that simplifies the process of training and fine-tuning state-of-the-art AI models. The project provides a no-code and low-code interface that allows users to train models using custom datasets without needing extensive expertise in machine learning engineering. It supports a wide range of tasks including text classification, sequence-to-sequence modeling, token classification, sentence embedding training, and large language model fine-tuning. ... -
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UCCL
UCCL is an efficient communication library for GPUs
UCCL is a high-performance GPU communication library designed to support distributed machine learning workloads and large-scale AI systems. The library focuses on enabling efficient data transfer and collective communication between GPUs during training and inference processes. It supports a variety of communication patterns including collective operations such as all-reduce as well as peer-to-peer transfers that are commonly used in modern machine learning architectures. UCCL is designed to... -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. Since TFP inherits the benefits of TensorFlow, you can build, fit, and deploy a model using a single language throughout the lifecycle of model exploration and production. ... -
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Detoxify
Trained models & code to predict toxic comments
Detoxify is a deep learning-based tool for detecting and filtering toxic language in online conversations, leveraging Transformer models for high accuracy. -
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Reflexion
Reflexion: Language Agents with Verbal Reinforcement Learning
Reflexion is a research-oriented AI framework that focuses on improving the reasoning and problem-solving capabilities of language model agents through iterative self-reflection and feedback loops. Instead of relying solely on a single-pass response, Reflexion enables agents to evaluate their own outputs, identify errors, and refine their reasoning over multiple iterations, leading to more accurate and reliable results. The framework introduces a mechanism where agents maintain a memory of past attempts and use that memory to guide future decisions, effectively simulating a learning process without requiring traditional model retraining. ... -
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All Agentic Architectures
Implementation of 17+ agentic architectures
All Agentic Architectures is an open educational repository that provides hands-on implementations of modern AI agent architectures. The project acts as a practical learning resource that bridges the gap between theoretical research on autonomous agents and real software implementations. It contains more than a dozen agent architectures implemented using frameworks such as LangChain and LangGraph. Each architecture is explained through runnable notebooks that illustrate how the agent works... -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures. Automatic batch size selection, distributed training (DDP, DeepSpeed),... -
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SparseML
Libraries for applying sparsification recipes to neural networks
SparseML is an optimization toolkit for training and deploying deep learning models using sparsification techniques like pruning and quantization to improve efficiency.
