Search Results for "multi-system" - Page 51
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Showing 2682 open source projects for "multi-system"
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LoLLMs WEBUI
Local AI WebUI for running and managing large language models offlineA
lollms-webui is a locally hosted web interface designed to run and manage large language models without relying on external services. It provides users with a centralized environment to interact with multiple AI models, making it suitable for experimentation, development, and personal use. lollms-webui emphasizes offline capability, allowing users to maintain privacy and control over their data while still accessing advanced AI features. It integrates model management tools that help users... -
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Everywhere
Context-aware desktop AI assistant that understands screen content
Everywhere is a context-aware desktop AI assistant designed to interact directly with the content displayed on a user’s screen. It distinguishes itself from traditional AI tools by eliminating the need for manual input methods such as copying text or taking screenshots, instead allowing users to invoke assistance instantly through a shortcut. It can analyze on-screen information in real time and provide contextual responses, making it useful for tasks like troubleshooting errors, summarizing... -
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Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
...When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. Technologies such as sentence embeddings and named entity recognition are used to interpret user instructions and construct appropriate code outputs. -
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Astron Agent
Enterprise platform for building and orchestrating AI agent workflows
Astron Agent is an enterprise-grade platform designed for building and managing intelligent AI agent workflows in production environments. It provides a development environment that combines workflow orchestration, model management, and integration with various AI tools and services. Astron Agent enables organizations to design complex agent-driven processes that coordinate models, automation tools, and enterprise systems. It also integrates robotic process automation capabilities so agents... -
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LEANN
Local RAG engine for private multimodal knowledge search on devices
LEANN is an open source system designed to enable retrieval-augmented generation (RAG) and semantic search across personal data while running entirely on local devices. It focuses on dramatically reducing the storage overhead typically required for vector search and embedding indexes, enabling efficient large-scale knowledge retrieval on consumer hardware. LEANN introduces a storage-efficient approximate nearest neighbor index combined with on-the-fly embedding recomputation to avoid storing large embedding vectors. ... -
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Ploomber
The fastest way to build data pipelines
Ploomber is an open-source framework designed to simplify the development and deployment of data science and machine learning pipelines. It allows developers to transform exploratory data analysis workflows into production-ready pipelines without rewriting large portions of code. The system integrates with common development environments such as Jupyter Notebook, VS Code, and PyCharm, enabling data scientists to continue working with familiar tools while building scalable workflows. Ploomber automatically manages task dependencies and execution order, allowing complex pipelines with multiple stages to run reliably. ... -
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AI Engineer Headquarters
A collection of scientific methods, processes, algorithms
AI-Engineer-Headquarters is a comprehensive educational repository designed to help developers become advanced AI engineers through a structured learning path and practical system-building exercises. The project serves as a curated collection of resources, methodologies, and tools covering topics across the entire artificial intelligence development lifecycle. Rather than focusing only on theoretical knowledge, the repository emphasizes applied learning and encourages engineers to build real systems that incorporate machine learning, large language models, data pipelines, and AI infrastructure. ... -
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AutoTrain Advanced
Faster and easier training and deployments
...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. The system integrates closely with the Hugging Face ecosystem and allows developers to train models using datasets hosted on the Hugging Face Hub. AutoTrain Advanced can run locally or in cloud environments, making it adaptable to different computational setups. By automating tasks such as model configuration, hyperparameter selection, and training pipelines, the project significantly reduces the technical barrier to building AI systems. -
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seq2seq-couplet
Play couplet with seq2seq model
...The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score tracking. It also supports serving the trained model through a web service, allowing users to interact with the system after training is complete. In addition to local execution, the project includes Docker files, which make it easier to package and deploy the application in a more reproducible way. The repository also points users to an external dataset source and documents vocabulary formatting requirements for custom datasets, showing that it is meant for both experimentation and extension. -
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Instant Neural Graphics Primitives
Instant neural graphics primitives: lightning fast NeRF and more
Instant Neural Graphics Primitives, is an open-source research project developed by NVIDIA that enables extremely fast training and rendering of neural graphics representations. The system implements several neural graphics primitives including neural radiance fields, signed distance functions, neural images, and neural volumes. These representations are trained using a compact neural network combined with a multiresolution hash encoding that dramatically accelerates both training and rendering processes. The framework is capable of reconstructing detailed 3D scenes from images and generating realistic views of those scenes in real time. ... -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. ... -
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MyScaleDB
A @ClickHouse fork that supports high-performance vector search
MyScaleDB is an open-source SQL vector database designed for building large-scale AI and machine learning applications that require both analytical queries and semantic vector search. The system is built on top of the ClickHouse database engine and extends it with specialized indexing and search capabilities optimized for vector embeddings. This design allows developers to store structured data, unstructured text, and high-dimensional vector embeddings within a single database platform. MyScaleDB enables developers to perform vector similarity searches using standard SQL syntax, eliminating the need to learn specialized vector database query languages. ... -
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Hollama
A minimal LLM chat app that runs entirely in your browser
Hollama is a lightweight open-source chat application designed to run entirely within the browser while interacting with large language model servers. The project provides a minimal but powerful user interface for communicating with local or remote LLMs, including servers powered by Ollama or OpenAI-compatible APIs. Because the application runs as a static web interface, it does not require complex backend infrastructure and can be easily deployed or self-hosted. Hollama supports both... -
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Prompt Poet
Streamlines and simplifies prompt design for both developers
Prompt Poet is an open-source framework designed to simplify the creation, organization, and maintenance of prompts for large language model applications. The project focuses on transforming prompt engineering into a structured design process rather than ad-hoc string manipulation within application code. It allows developers and non-technical users to build prompts using templated configurations based on YAML and Jinja2, which makes prompts easier to compose, reuse, and modify across... -
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OpenOutreach
Linkedin Automation Tool
...Instead of requiring a prebuilt contact list, it starts from a product description and target market definition, then uses AI to discover and prioritize likely leads on LinkedIn. The system generates search queries, evaluates candidate profiles, and learns over time which contacts best match the ideal customer profile. According to the repository, it combines large language model classification with a Bayesian machine learning layer based on profile embeddings, which helps it shift from broad exploration to more confident qualification as it gathers more decisions. ... -
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Text-to-LoRA (T2L)
Hypernetworks that adapt LLMs for specific benchmark tasks
Text-to-LoRA is a research project that introduces a method for dynamically adapting large language models using hypernetworks that generate LoRA parameters directly from textual descriptions. Instead of training a new LoRA adapter for every task or dataset, the system can produce task-specific adaptations based solely on a text description of the desired capability. This approach enables models to rapidly internalize new contextual knowledge without performing traditional fine-tuning steps. The project provides a reference implementation of the Doc-to-LoRA method, which allows language models to quickly encode factual information or contextual constraints into lightweight LoRA modules. ... -
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PicoLM
Run a 1-billion parameter LLM on a $10 board with 256MB RAM
PicoLM is an open-source inference framework designed to run large language models on extremely constrained hardware environments such as inexpensive single-board computers and embedded systems. The project focuses on enabling efficient local inference by optimizing memory usage, computation, and system dependencies so that relatively large models can operate on devices with minimal RAM. It is written primarily in C and designed with a minimalist architecture that removes unnecessary dependencies and external libraries. The runtime is capable of running language models with billions of parameters on devices with only a few hundred megabytes of memory, which is significantly lower than typical LLM infrastructure requirements. ... -
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RAG from Scratch
Demystify RAG by building it from scratch
RAG From Scratch is an educational open-source project designed to teach developers how retrieval-augmented generation systems work by building them step by step. Instead of relying on complex frameworks or cloud services, the repository demonstrates the entire RAG pipeline using transparent and minimal implementations. The project walks through key concepts such as generating embeddings, building vector databases, retrieving relevant documents, and integrating the retrieved context into... -
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UCCL
UCCL is an efficient communication library for GPUs
...UCCL is designed to work with heterogeneous hardware environments, allowing GPUs from different vendors and network interfaces to communicate efficiently without vendor lock-in. The system also supports specialized workloads such as reinforcement learning weight transfers, key-value cache sharing, and expert parallelism for mixture-of-experts models. Its architecture emphasizes flexibility and extensibility so that developers can implement custom communication protocols tailored to specific machine learning workloads. -
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SmythOS
Cloud-native runtime for agentic AI
SmythOS SRE (Smyth Runtime Environment) is an open-source runtime and development platform designed for building and operating production-grade AI agents. It provides a foundational infrastructure layer that functions similarly to an operating system for agentic AI systems, managing resources such as language models, storage, vector databases, and caching through a unified interface. Developers can use the runtime to create, deploy, and orchestrate intelligent agents across local machines, cloud environments, or hybrid infrastructures without rewriting their application logic. ... -
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DriveLM
Driving with Graph Visual Question Answering
...Instead of treating autonomous driving as a purely sensor-driven pipeline, DriveLM frames it as a reasoning problem where models answer structured questions about the environment to guide decision making. The system includes DriveLM-Data, a dataset built on driving environments such as nuScenes and CARLA, where human-written reasoning steps connect different layers of driving tasks. This design allows models to learn relationships between objects, behaviors, and navigation decisions through graph-structured logic. -
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Korvus
Korvus is a search SDK that unifies the entire RAG pipeline
...By leveraging PostgresML and vector extensions such as pgvector, Korvus eliminates the need for external microservices typically used for AI search architectures, reducing both system complexity and latency. The architecture enables machine learning operations to occur directly in the database, minimizing data transfer between services and improving overall performance for large datasets. -
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CAG
Cache-Augmented Generation: A Simple, Efficient Alternative to RAG
...This strategy allows the model to generate responses using the cached context directly, eliminating the need for repeated retrieval operations during runtime. As a result, the approach can significantly reduce latency and simplify system architecture compared with traditional RAG pipelines. The framework is particularly effective when the knowledge base is limited enough to fit within the extended context window of modern language models. -
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dataline
AI data analysis and visualization on CSV, Postgres, MySQL, Snowflake
dataline is an open-source AI data analysis and visualization platform that allows users to interact with datasets using natural language. The system enables both technical and non-technical users to explore data by asking questions conversationally, which the platform translates into database queries and analytical operations. It supports connections to multiple structured data sources such as PostgreSQL, MySQL, Snowflake, SQLite, Excel files, CSV datasets, and other database systems. ... -
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WFGY 3.0
A tension reasoning engine over 131 S-class problems
...Different versions of the framework, including WFGY 1.0, 2.0, and 3.0, represent stages of development where early conceptual ideas evolved into more structured reasoning engines and diagnostic tools. The system maps reasoning tension across a large set of complex problems spanning domains such as mathematics, science, climate, finance, and artificial intelligence behavior.
