Search Results for "artificial" - Page 6
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Showing 415 open source projects for "artificial"
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FlagEmbedding
Retrieval and Retrieval-augmented LLMs
FlagEmbedding is an open-source toolkit for building and deploying high-performance text embedding models used in information retrieval and retrieval-augmented generation systems. The project is part of the BAAI FlagOpen ecosystem and focuses on creating embedding models that transform text into dense vector representations suitable for semantic search and large language model pipelines. FlagEmbedding includes a family of models known as BGE (BAAI General Embedding), which are designed to... -
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FinGPT
Open-Source Financial Large Language Models
FinGPT is an open-source, finance-specialized large language model framework that blends the capabilities of general LLMs with real-time financial data feeds, domain-specific knowledge bases, and task-oriented agents to support market analysis, research automation, and decision support. It extends traditional GPT-style models by connecting them to live or historical financial datasets, news APIs, and economic indicators so that outputs are grounded in relevant and recent market conditions... -
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Ring
Ring is a reasoning MoE LLM provided and open-sourced by InclusionAI
Ring is a reasoning Mixture-of-Experts (MoE) large language model (LLM) developed by inclusionAI. It is built from or derived from Ling. Its design emphasizes reasoning, efficiency, and modular expert activation. In its “flash” variant (Ring-flash-2.0), it optimizes inference by activating only a subset of experts. It applies reinforcement learning/reasoning optimization techniques. Its architectures and training approaches are tuned to enable efficient and capable reasoning performance.... -
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Infinity
Low-latency REST API for serving text-embeddings
Infinity is a high-throughput, low-latency REST API for serving vector embeddings, supporting all sentence-transformer models and frameworks. Infinity is developed under MIT License. Infinity powers inference behind Gradient.ai and other Embedding API providers. -
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Anything to NotebookLM
Multi-source content processor for NotebookLM
Qiaomu Anything to NotebookLM is a Claude Code skill that turns many types of source material into structured NotebookLM-ready outputs. It is built for users who want to convert articles, web pages, videos, PDFs, office files, podcasts, images, and search results into more usable study or presentation formats. The project uses natural-language commands, so the user can ask for a podcast, slide deck, mind map, report, quiz, flashcards, or infographic without manually building the workflow. It... -
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Hephaestus
Semi-Structured Agentic Framework. Workflows build themselves
Hephaestus is an open-source semi-structured agentic framework designed to orchestrate multiple AI agents working together on complex tasks. Instead of relying entirely on predefined workflows, the framework allows agents to dynamically create tasks as they explore a problem space. Developers define high-level phases such as analysis, implementation, and testing, while agents generate specific subtasks within those phases. The system continuously monitors agent behavior and task progression,... -
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MiniOneRec
Minimal reproduction of OneRec
MiniOneRec is an open-source framework designed to explore generative approaches to recommendation systems using large language model architectures. Traditional recommender systems typically rely on large embedding tables and ranking models, but MiniOneRec adopts a generative paradigm in which items are represented as sequences of semantic identifiers generated by autoregressive models. The framework provides an end-to-end pipeline for building generative recommender systems, including... -
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how-to-optim-algorithm-in-cuda
How to optimize some algorithm in cuda
how-to-optim-algorithm-in-cuda is an open educational repository focused on teaching developers how to optimize algorithms for high-performance execution on GPUs using CUDA. The project combines technical notes, code examples, and practical experiments that demonstrate how common computational kernels can be optimized to improve speed and memory efficiency. Instead of presenting only theoretical explanations, the repository includes hand-written CUDA implementations of fundamental operations... -
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SageAttention
NeurIPS2025 Spotlight] Quantized Attention
SageAttention is an open-source optimization library designed to accelerate the attention mechanism used in transformer-based neural networks. Since attention operations are often the most computationally expensive component of modern AI models, SageAttention introduces quantization techniques that significantly reduce computational overhead while preserving model accuracy. The system achieves this by using low-precision numerical formats such as INT4, FP8, or INT8 to represent key matrices... -
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Cybersecurity AI
Cybersecurity AI (CAI), the framework for AI Security
CAI (Cybersecurity AI) is a lightweight open-source framework intended to help security practitioners build and deploy AI-assisted automation for defensive and offensive security workflows. The project frames itself as a practical foundation for “AI security,” focusing on turning security tasks into agentic workflows that can be composed, executed, and iterated on by practitioners. Rather than being a single-purpose tool, CAI is positioned as a framework that supports building multiple... -
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Happy-LLM
Large Language Model Principles and Practice Tutorial from Scratch
Happy-LLM is an open-source educational project created by the Datawhale AI community that provides a structured and comprehensive tutorial for understanding and building large language models from scratch. The project guides learners through the entire conceptual and practical pipeline of modern LLM development, starting with foundational natural language processing concepts and gradually progressing to advanced architectures and training techniques. It explains the Transformer... -
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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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OpenChronicle
Open-source, local-first memory for any tool-capable LLM agent
OpenChronicle is a knowledge management and storytelling platform designed to organize information into structured timelines and interconnected narratives. It allows users to create chronological records that link events, ideas, and entities in a cohesive format. The system emphasizes visualization and organization of complex information over time. It can be used for research, writing, or personal knowledge tracking. OpenChronicle supports extensibility, enabling customization of how data is... -
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Unstructured.IO
Open source libraries and APIs to build custom preprocessing pipelines
The unstructured library provides open-source components for ingesting and pre-processing images and text documents, such as PDFs, HTML, Word docs, and many more. The use cases of unstructured revolve around streamlining and optimizing the data processing workflow for LLMs. unstructured modular bricks and connectors form a cohesive system that simplifies data ingestion and pre-processing, making it adaptable to different platforms and is efficient in transforming unstructured data into... -
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Claude Code Tools
Practical productivity tools for Claude Code, Codex-CLI
Claude Code Tools is an open-source collection of command-line utilities and productivity plugins designed to enhance developer workflows when using AI coding agents such as Claude Code and Codex-CLI. The project focuses on solving common problems encountered in AI-assisted development environments, including managing session history, automating terminal interactions, and maintaining context across multiple coding sessions. It includes tools that allow developers to search conversation logs... -
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Gemma
Gemma open-weight LLM library, from Google DeepMind
Gemma, developed by Google DeepMind, is a family of open-weights large language models (LLMs) built upon the research and technology behind Gemini. This repository provides the official implementation of the Gemma PyPI package, a JAX-based library that enables users to load, interact with, and fine-tune Gemma models. The framework supports both text and multi-modal input, allowing natural language conversations that incorporate visual content such as images. It includes APIs for... -
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Swirl
Swirl queries any number of data sources with APIs
Swirl queries any number of data sources with APIs and uses spaCy and NLTK to re-rank the unified results without extracting and indexing anything! Includes zero-code configs for Apache Solr, ChatGPT, Elastic Search, OpenSearch, PostgreSQL, Google BigQuery, RequestsGet, Google PSE, NLResearch.com, Miro & more! SWIRL adapts and distributes queries to anything with a search API - search engines, databases, noSQL engines, cloud/SaaS services etc - and uses AI (Large Language Models) to re-rank... -
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Qwen3-Omni
Qwen3-omni is a natively end-to-end, omni-modal LLM
Qwen3-Omni is a natively end-to-end multilingual omni-modal foundation model that processes text, images, audio, and video and delivers real-time streaming responses in text and natural speech. It uses a Thinker-Talker architecture with a Mixture-of-Experts (MoE) design, early text-first pretraining, and mixed multimodal training to support strong performance across all modalities without sacrificing text or image quality. The model supports 119 text languages, 19 speech input languages, and... -
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kg-gen
Knowledge Graph Generation from Any Text
kg-gen is an open-source framework developed by the STAIR Lab that automatically generates knowledge graphs from unstructured text using large language models. The system is designed to transform plain text sources such as documents, articles, or conversation transcripts into structured graphs composed of entities and relationships. Instead of relying on traditional rule-based extraction techniques, KG-Gen uses language models to identify entities and their relationships, producing... -
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CogVLM2
GPT4V-level open-source multi-modal model based on Llama3-8B
CogVLM2 is the second generation of the CogVLM vision-language model series, developed by ZhipuAI and released in 2024. Built on Meta-Llama-3-8B-Instruct, CogVLM2 significantly improves over its predecessor by providing stronger performance across multimodal benchmarks such as TextVQA, DocVQA, and ChartQA, while introducing extended context length support of up to 8K tokens and high-resolution image input up to 1344×1344. The series includes models for both image understanding and video... -
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GLM-V
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning
GLM-V is an open-source vision-language model (VLM) series from ZhipuAI that extends the GLM foundation models into multimodal reasoning and perception. The repository provides both GLM-4.5V and GLM-4.1V models, designed to advance beyond basic perception toward higher-level reasoning, long-context understanding, and agent-based applications. GLM-4.5V builds on the flagship GLM-4.5-Air foundation (106B parameters, 12B active), achieving state-of-the-art results on 42 benchmarks across image,... -
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LLM Foundry
LLM training code for MosaicML foundation models
Introducing MPT-7B, the first entry in our MosaicML Foundation Series. MPT-7B is a transformer trained from scratch on 1T tokens of text and code. It is open source, available for commercial use, and matches the quality of LLaMA-7B. MPT-7B was trained on the MosaicML platform in 9.5 days with zero human intervention at a cost of ~$200k. Large language models (LLMs) are changing the world, but for those outside well-resourced industry labs, it can be extremely difficult to train and deploy... -
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BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics
BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports guided, supervised, semi-supervised, manual, long-document, hierarchical, class-based, dynamic, and online topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found here, here and here. For a more detailed overview, you can... -
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Deep Lake
Data Lake for Deep Learning. Build, manage, and query datasets
Deep Lake (or Deeplake, formerly known as Activeloop Hub) is a data lake for deep learning applications. Our open-source dataset format is optimized for rapid streaming and querying of data while training models at scale, and it includes a simple API for creating, storing, and collaborating on AI datasets of any size. It can be deployed locally or in the cloud, and it enables you to store all of your data in one place, ranging from simple annotations to large videos. Deep Lake is used by... -
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spacy-llm
Integrating LLMs into structured NLP pipelines
Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various...
