Search Results for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files" - Page 10
Sort By:
Showing 270 open source projects for "/storage/emulated/0/android/data/net.sourceforge.uiq3.fx603p/files"
View related business solutions-
Feroot AI automates website security with 24/7 monitoringFeroot unifies JavaScript behavior analysis, web compliance scanning, third-party script monitoring, consent enforcement, and data privacy posture management to stop Magecart, formjacking, and unauthorized tracking.
-
Smart IT MonitoringNetCrunch is a smart, agentless network monitoring and management software system capable of monitoring every device in a network. Developed by AdRem Software, NetCrunch helps businesses of all sizes remotely monitor network services, switches, routers, bandwidth utilization, and traffic flow and visualize their system performance.
-
1
RunAnywhere
Production ready toolkit to run AI locally
...The toolkit allows developers to integrate language models, speech recognition, and voice synthesis capabilities into mobile or desktop applications while keeping all computation local. By running models entirely on device, the platform eliminates network latency and protects user data because information does not leave the device. The SDK supports popular open-source models such as Llama, Mistral, and Qwen, enabling developers to build AI-powered features such as chat interfaces and voice assistants with minimal external dependencies. It also includes integrated pipelines that combine speech-to-text, large language models, and text-to-speech into a complete conversational system. -
2
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... -
3
Canopy
Retrieval Augmented Generation (RAG) framework
Canopy is an open-source retrieval-augmented generation (RAG) framework developed by Pinecone to simplify the process of building applications that combine large language models with external knowledge sources. The system provides a complete pipeline for transforming raw text data into searchable embeddings, storing them in a vector database, and retrieving relevant context for language model responses. It is designed to handle many of the complex components required for a RAG workflow, including document chunking, embedding generation, prompt construction, and chat history management. Developers can use Canopy to quickly build chat systems that answer questions using their own data instead of relying solely on the pretrained knowledge of the language model. ... -
4
local-llm
Run LLMs locally on Cloud Workstations
...The repository includes tools, Docker configurations, and command-line utilities that simplify the process of downloading, running, and interacting with language models directly on local or cloud-based workstations. This approach improves data privacy and control, as all inference can be performed locally without sending sensitive information to external APIs. It also integrates seamlessly with Google Cloud services, allowing developers to build and test AI-powered applications within the broader cloud ecosystem. -
Ditto Edge Server is a lightweight standalone server for resource-constrained edge environments, based on the core Ditto Edge SDK.Ditto's Edge SDK is the only thing your edge devices need to ensure your application is operational in any environment, regardless of network conditions.
-
5
AIConfig
AIConfig is a config-based framework to build generative AI apps
AIConfig is an open-source framework designed to simplify the development and management of generative AI applications by separating AI logic from application code. The framework allows prompts, model configurations, and parameters to be stored as structured configuration files that can be version controlled and managed independently from the rest of the software system. This approach improves collaboration between developers, prompt engineers, and machine learning practitioners by turning prompt logic into a reusable and editable artifact. AIConfig supports multiple model providers and modalities, enabling developers to experiment with different models without rewriting application logic. ... -
6
LIDA
Automatic Generation of Visualizations and Infographics using LLMs
LIDA is an open-source library developed to automate the process of creating data visualizations and infographics using large language models. The system treats visualizations as executable code and uses AI to generate, modify, and interpret that code in order to transform raw datasets into meaningful charts and graphical explanations. Instead of requiring users to manually explore datasets and write plotting scripts, LIDA analyzes the data and automatically proposes visualization goals and design ideas that highlight patterns and relationships. ... -
7
Streamline Analyst
AI agent that streamlines the entire process of data analysis
Streamline Analyst is a cutting-edge, open-source application powered by Large Language Models (LLMs) designed to revolutionize data analysis. This Data Analysis Agent effortlessly automates all the tasks such as data cleaning, preprocessing, and even complex operations like identifying target objects, partitioning test sets, and selecting the best-fit models based on your data. With Streamline Analyst, results visualization and evaluation become seamless. -
8
autollm
Ship RAG based LLM web apps in seconds
...The framework also includes built-in readers for multiple content sources such as PDFs, DOCX files, notebooks, websites, and other document types, which helps shorten the time between raw data and a working knowledge application. -
9
DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT) and tool-assisted reasoning modes. ... -
Safety Compliance Made EasyIdeally designed for business that deals with Construction, Oil & Gas, Mining, Manufacturing, Mechanical, Electrical, Plumbing, Heating, and Excavating, SiteDocs is a perfect solution for any size business looking to modernize the way Safety Compliance is organized.
-
10
Qwen2.5
Open source large language model by Alibaba
...Trained on a comprehensive dataset of up to 18 trillion tokens, Qwen2.5 models exhibit significant improvements in instruction following, long-text generation (exceeding 8,000 tokens), and structured data comprehension, such as tables and JSON formats. They support context lengths up to 128,000 tokens and offer multilingual capabilities in over 29 languages, including Chinese, English, French, Spanish, and more. The models are open-source under the Apache 2.0 license, with resources and documentation available on platforms like Hugging Face and ModelScope. ... -
11
ToRA
Tool-integrated Reasoning LLM Agents
...This approach allows the model to reason step by step in natural language and then execute precise calculations or code through tool calls, creating a hybrid reasoning workflow. The framework was designed to address known weaknesses of large language models in mathematical problem solving and formal reasoning tasks. Training data includes tool-use trajectories that teach the model when to reason verbally and when to delegate tasks to specialized tools. -
12
Aviary
Ray Aviary - evaluate multiple LLMs easily
...Supporting Transformer models hosted on Hugging Face Hub or present on local disk. Aviary has native support for autoscaling and multi-node deployments thanks to Ray and Ray Serve. Aviary can scale to zero and create new model replicas (each composed of multiple GPU workers) in response to demand. Ray ensures that the orchestration and resource management is handled automatically. Aviary is able to support hundreds of replicas and clusters of hundreds of nodes, deployed either in the cloud or on-prem. -
13
Knowledge + Chat
Knowledge is a tool for saving, searching, accessing, and chatting
Knowledge is an open-source desktop application designed for organizing, exploring, and interacting with information gathered from websites, documents, and other digital sources. The platform allows users to collect knowledge from multiple formats including web pages, PDF files, videos, and other documents and organize them into structured projects and subprojects. These sources can then be visualized and explored using several views such as graph, grid, table, and calendar interfaces, making it easier to understand relationships between pieces of information. The application also includes a conversational chat interface that allows users to ask questions about stored sources and receive AI-generated explanations or summaries. ... -
14
Chinese-LLaMA-Alpaca 2
Chinese LLaMA-2 & Alpaca-2 Large Model Phase II Project
...The Chinese LLaMA-2 base model and the Alpaca-2 instruction fine-tuning large model are open-sourced. These models expand and optimize the Chinese vocabulary on the basis of the original Llama-2, use large-scale Chinese data for incremental pre-training, and further improve the basic semantics and command understanding of Chinese. Performance improvements. The related model supports FlashAttention-2 training, supports 4K context and can be extended up to 18K+ through the NTK method. -
15
axflow
The TypeScript framework for AI development
...Its core SDK enables developers to integrate language model capabilities into web applications while maintaining strong modular design principles. Additional components support data ingestion, evaluation, and model interaction workflows that are commonly required when building production AI systems. For example, the framework includes modules for connecting application data to language models, evaluating the quality of model outputs, and building streaming user interfaces. Because each component can be used independently, developers can adopt Axflow incrementally rather than committing to a monolithic framework. ... -
16
towhee
Framework that is dedicated to making neural data processing
Towhee is an open-source machine-learning pipeline that helps you encode your unstructured data into embeddings. You can use our Python API to build a prototype of your pipeline and use Towhee to automatically optimize it for production-ready environments. From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. ... -
17
LLaMA-MoE
Building Mixture-of-Experts from LLaMA with Continual Pre-training
LLaMA-MoE is an open-source project that builds mixture-of-experts language models from LLaMA through expert partitioning and continual pre-training. The repository is centered on making MoE research more accessible by offering smaller and more affordable models with only about 3.0 to 3.5 billion activated parameters, which helps reduce deployment and experimentation costs. Its architecture works by splitting LLaMA feed-forward networks into sparse experts and adding gating mechanisms so... -
18
DB-GPT-Hub
A repository that contains models, datasets, and fine-tuning
...The project serves as a specialized extension of the broader DB-GPT ecosystem, focusing on the preparation and evaluation of models capable of translating natural language questions into structured database queries. It offers a modular framework that supports data preparation, model fine-tuning, benchmarking, and inference for Text-to-SQL systems. The repository includes datasets and experiment configurations that allow researchers to train models on real database schemas and evaluate them using standardized benchmarks. Its design encourages experimentation with different large language models and fine-tuning techniques, including parameter-efficient training approaches. -
19
Alpaca-CoT
We unified the interfaces of instruction-tuning data
...The repository includes datasets, training scripts, and examples demonstrating how chain-of-thought data can be used to fine-tune language models. It also explores how reasoning traces generated by larger models can be distilled into smaller models. -
20
RAGs
Build ChatGPT over your data, all with natural language
...Users can also inspect and adjust parameters such as the number of retrieved documents, summarization strategies, and query settings through a configuration interface. Once the pipeline is created, the system enables conversational queries over the connected data sources, effectively creating a personalized knowledge assistant. -
21
LLMDataHub
Quick guide (especially) for trending instruction finetuning dataset
...The project aims to solve the challenge of discovering high-quality datasets by collecting resources that are otherwise scattered across multiple research communities and repositories. Each dataset entry typically includes information such as size, language coverage, intended use cases, and links to the original data sources. The repository focuses particularly on datasets useful for chatbot training, instruction-following tasks, and alignment training scenarios. By organizing these resources into a curated hub, the project helps researchers and developers identify the most relevant training corpora for building conversational AI systems. The repository also highlights datasets suitable for reinforcement learning from human feedback and other alignment strategies used in modern language model training. -
22
Autolabel
Label, clean and enrich text datasets with LLMs
Autolabel is a Python library to label, clean and enrich datasets with Large Language Models (LLMs). Autolabel data for NLP tasks such as classification, question-answering and named entity recognition, entity matching and more. Seamlessly use commercial and open-source LLMs from providers such as OpenAI, Anthropic, HuggingFace, Google and more. -
23
AutoGPT.js
Auto-GPT on the browser
AutoGPT.js is an open-source project that brings autonomous AI agent capabilities similar to AutoGPT directly into the browser environment. The system allows users to run an AI agent capable of performing tasks such as generating code, searching the web, and interacting with files on the local computer. Unlike traditional AutoGPT implementations that require server infrastructure, AutoGPT.js is designed to run primarily in the browser, making it easier to deploy and experiment with autonomous agents. The platform uses web APIs and language model integrations to give the agent the ability to plan tasks, execute commands, and store short-term memory during operations. ... -
24
Chinese Llama 2 7B
The first Chinese LLaMA2 model in the open source community
Chinese Llama 2 7B is an open-source large language model adapted from the LLaMA-2 architecture and optimized for Chinese and bilingual Chinese-English applications. The project provides a version of LLaMA-2 that has been further trained on Chinese data so it can better understand and generate text in Chinese while maintaining compatibility with the original model ecosystem. In addition to the model weights, the repository also includes supervised fine-tuning datasets and training resources that help developers build chat-optimized versions of the model. The project follows the input format used by the LLaMA-2 chat architecture, ensuring compatibility with existing optimization techniques and tools built for the LLaMA-2 ecosystem. ... -
25
ReplitLM
Inference code and configs for the ReplitLM model family
ReplitLM is a family of open-source language models developed by Replit for assisting with programming tasks such as code generation and completion. The project includes model checkpoints, configuration files, and example code that enable developers to run and experiment with the models locally or within machine learning frameworks. These models are designed specifically for coding workflows and are trained on large datasets of source code covering many programming languages and development environments. The repository also includes documentation and tutorials for integrating the models into development tools, APIs, or research pipelines. ...
