Search Results for "sarvesh-project" - Page 19
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Showing 796 open source projects for "sarvesh-project"
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Go From AI Idea to AI App FastAccess Gemini 3 and 200+ models. Build chatbots, agents, or custom models with built-in monitoring and scaling.
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WorldGen
Generate Any 3D Scene in Seconds
WorldGen is an AI model and library that can generate full 3D scenes in a matter of seconds from either text prompts or reference images. It is designed to create interactive environments suitable for games, simulations, robotics research, and virtual reality, rather than just static 3D assets. The core idea is that you describe a world in natural language and WorldGen produces a navigable 3D scene that you can freely explore in 360 degrees, with loop closure so that the space remains... -
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Agently 4
Build GenAI application quick and easy
...With Agently, one can define agents that call different models, chain tasks, trigger workflows based on events, and switch models with minimal code changes. It abstracts away boilerplate around model API calls, tool usage, prompt management, and workflow state. The project aims at production-grade GenAI application development rather than just one-off scripts — you’ll find examples of news gathering, agentic workflows, control systems, etc. It is licensed under Apache-2.0, allowing commercial use and modification. Because it's built in Python, it integrates easily with existing data pipelines, databases, and agent architectures. -
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Omnilingual ASR
Omnilingual ASR Open-Source Multilingual SpeechRecognition
Omnilingual-ASR is a research codebase exploring automatic speech recognition that generalizes across a very large number of languages using shared modeling and training recipes. It focuses on leveraging self-supervised audio pretraining and scalable fine-tuning so low-resource languages can benefit from high-resource data. The project provides data preparation pipelines, training scripts, decoding utilities, and evaluation tools so researchers can reproduce results and extend to new language sets. It emphasizes modularity: acoustic modeling, language modeling, tokenization, and decoding are separable pieces you can swap or ablate. The repo is aimed at pushing practical multilingual ASR—robust to accents, code-switching, and domain shifts—rather than language-by-language systems. ... -
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HunyuanWorld-Mirror
Fast and Universal 3D reconstruction model for versatile tasks
...The pipeline emphasizes both speed and flexibility so creators can go from casual captures to assets without elaborate capture rigs. Outputs can include point clouds, estimated camera parameters, and other 3D representations that plug into typical graphics workflows. The project sits within a broader family of Hunyuan models that explore world generation and 3D-consistent understanding, and this mirror variant makes the reconstruction stack easier to test. It’s attractive for rapid prototyping of scenes, environment scans, or reference assets when you need repeatable 3D results from ordinary media. -
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Vision Transformer Pytorch
Implementation of Vision Transformer, a simple way to achieve SOTA
...Because it stays close to vanilla PyTorch, you can integrate custom datasets and training loops without framework lock-in. It’s widely used as an educational reference for people learning transformers in vision and as a lightweight baseline for research prototypes. The project encourages experimentation—swap optimizers, change augmentations, or plug the transformer backbone into downstream tasks. -
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LitterBox
A secure sandbox environment for malware developers and red teamers
...Repo metadata and author pages highlight an active security-tools ecosystem around the maintainer, with CI and pull-request activity suggesting ongoing development. The project positions itself as a safe proving ground to reduce surprises in the field while minimizing operational risk. For teams exploring MCP integrations, notes mention pairing with LLM agents for assisted analysis. -
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ArXiv MCP Server
A Model Context Protocol server for searching and analyzing arXiv
arxiv-mcp-server bridges AI assistants and the arXiv repository through a clean MCP interface, enabling search, metadata retrieval, and content access without bespoke scraping. With simple tools like “search” and “fetch,” an agent can find papers, pull abstracts, and download PDFs for downstream summarization or analysis. The project includes packaging and CI to publish to PyPI, plus tests and linting for reliability. Issue threads show feature requests such as extracting embedded LaTeX and improving markdown conversion, reflecting active community use in research flows. It’s designed to be drop-in for MCP clients, giving them typed inputs/outputs and predictable errors around a well-known academic corpus. ... -
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Purple Llama
Set of tools to assess and improve LLM security
Purple Llama is an umbrella safety initiative that aggregates tools, benchmarks, and mitigations to help developers build responsibly with open generative AI. Its scope spans input and output safeguards, cybersecurity-focused evaluations, and reference shields that can be inserted at inference time. The project evolves as a hub for safety research artifacts like Llama Guard and Code Shield, along with dataset specs and how-to guides for integrating checks into applications. CyberSecEval, one of its flagship components, provides repeatable evaluations for security risk, including agent-oriented tasks such as automated patching benchmarks. ... -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
...It supports function-calling style datasets (via "messages" keys) as well as plain text formats, with guidelines on formatting, tokenization, and vocabulary extension (e.g. extending vocab to 32768 for some models) before finetuning. The project also provides tutorial notebooks (e.g. mistral_finetune_7b.ipynb) to walk through the steps. -
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AICodeBot
AI-powered tool for developers, simplifying coding tasks
...We've planned to build out multiple different interfaces for interacting with AICodeBot. To start, it's a command-line tool that you can install and run in your terminal and a GitHub Action for Code Reviews. This project was built before AI Coding Assistants were cool. As such, much of the functionality has been replicated in various IDEs. Where AICodeBot shines is a) it's in the terminal, not GUI, and b) it can be used in processes like GitHub actions. We're using AICodeBot to build AICodeBot, and it's upward spiraling all the time.️ We're looking for contributors to help us build it out. -
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refinery
Open-source choice to scale, assess and maintain natural language data
...Also, the makers of refinery currently work on integrations to other labeling tools, such that you can easily switch between different choices. refinery is a multi-repository project, you can find all integrated services in the architecture below. The app builds on top of Hugging Face and spaCy to leverage pre-built language models for your NLP tasks, as well as qdrant for neural search. -
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OpenVINO Training Extensions
Trainable models and NN optimization tools
OpenVINO™ Training Extensions provide a convenient environment to train Deep Learning models and convert them using the OpenVINO™ toolkit for optimized inference. When ote_cli is installed in the virtual environment, you can use the ote command line interface to perform various actions for templates related to the chosen task type, such as running, training, evaluating, exporting, etc. ote train trains a model (a particular model template) on a dataset and saves results in two files. ote... -
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Hivemind
Decentralized deep learning in PyTorch. Built to train models
...Train neural networks of arbitrary size: parts of their layers are distributed across the participants with the Decentralized Mixture-of-Experts. If you have succesfully trained a model or created a downstream repository with the help of our library, feel free to submit a pull request that adds your project to the list. -
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Build Your Own OpenClaw
A step-by-step guide to build your own AI agent
Build Your Own OpenClaw is a step-by-step educational framework that teaches developers how to construct a fully functional AI agent system from scratch, gradually evolving from a simple chat loop into a multi-agent, production-ready architecture. The project is structured into 18 progressive stages, each introducing a new concept such as tool usage, memory persistence, event-driven design, and multi-agent coordination, with each step including both explanatory documentation and runnable code. It begins with foundational concepts like conversational loops and tool integration, then expands into more advanced capabilities such as dynamic skill loading, web interaction, and context management. ... -
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promptmap2
A security scanner for custom LLM applications
promptmap is an automated security scanner for custom LLM applications that focuses on prompt injection and related attack classes. The project supports both white-box and black-box testing, which means it can either run tests directly against a known model and system prompt configuration or attack an external HTTP endpoint without internal access. Its scanning workflow uses a dual-LLM architecture in which one model acts as the target being tested and another acts as a controller that evaluates whether an attack succeeded. ... -
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autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter changes. The system centers on a simple workflow where the agent modifies a single training file while human researchers guide the process through a program.md instruction file. ... -
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Google Workspace MCP Server
Control Gmail, Google Calendar, Docs, Sheets, Slides, Chat, Forms
Google Workspace MCP is an open-source server that connects AI assistants to Google Workspace services through the Model Context Protocol (MCP), allowing large language models to interact directly with productivity tools. The project exposes a wide set of Google services including Gmail, Google Drive, Docs, Sheets, Slides, Calendar, Chat, and other Workspace components as structured tools that an AI system can call programmatically. By acting as a bridge between AI clients and the Google ecosystem, the server enables automated workflows such as searching emails, creating calendar events, retrieving documents, or editing files without leaving the AI environment. ... -
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hCaptcha Challenger
Gracefully face hCaptcha challenge with multimodal llms
hCaptcha Challenger is an open-source automation framework designed to solve hCaptcha verification challenges using computer vision models and multimodal reasoning techniques. The project integrates machine learning models capable of analyzing visual captcha tasks and identifying the correct responses required to pass the verification process. Instead of relying on third-party captcha-solving services or browser scripts, the system operates independently by using pretrained neural networks that can classify images, detect objects, and interpret spatial relationships. ... -
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Agentic RAG for Dummies
A modular Agentic RAG built with LangGraph
Agentic RAG for Dummies is an educational repository that demonstrates how to build retrieval-augmented generation systems combined with autonomous AI agents. The project explains the principles behind agentic retrieval pipelines where language models can dynamically decide when to retrieve information, analyze results, and plan further actions. Instead of relying on static retrieval pipelines, the system shows how agents can orchestrate retrieval, reasoning, and tool usage in a more flexible decision loop. ... -
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Streamer-Sales
LLM Large Model of Selling Anchor
Streamer-Sales is an open-source large language model system designed specifically for e-commerce live streaming and automated product promotion. The project focuses on generating persuasive product descriptions and live presentation scripts that mimic the style of professional online sales hosts. By analyzing product characteristics and marketing information, the model can produce engaging explanations that emphasize benefits, features, and emotional appeal to encourage viewers to make purchasing decisions. ... -
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NVIDIA Generative AI Examples
Generative AI reference workflows
NVIDIA GenerativeAIExamples is an open-source repository that provides practical reference implementations and example workflows for building generative AI applications using NVIDIA’s software ecosystem. The project is designed to help developers accelerate the development of AI applications by providing ready-to-run pipelines, notebooks, and tools that demonstrate how to integrate large language models into real-world systems. The repository includes examples covering topics such as retrieval-augmented generation pipelines, agent-based workflows, and multimodal AI applications that combine text, vision, and data processing. ... -
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AutoAgent
AutoAgent: Fully-Automated and Zero-Code LLM Agent Framework
...It also describes resource orchestration and iterative self-improvement behaviors, including controlled code generation for building tools and agent capabilities when needed. The project is designed to work with multiple LLM providers and model endpoints, allowing users to choose different backends by setting environment variables and model identifiers. -
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Continuous Claude v3
Context management for Claude Code. Hooks maintain state via ledgers
...Rather than relying on a single session’s context, Continuous Claude uses mechanisms like ledgers, YAML handoffs, and a memory system to preserve and recall state across multiple sessions, ensuring that learned insights and plans are not lost when context compaction occurs. The project orchestrates many specialized agents and skills—109 skills and 32 agents—so that complex coding tasks can be broken down, analyzed, and executed collaboratively by different components. It also includes a layered code analysis pipeline to reduce token usage and maintain relevant context efficiently. This continuous learning environment enables workflows such as bug fixing, refactoring, planning, and exploratory investigation while minimizing the need to re-explain context manually. -
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MAI-UI
Real-World Centric Foundation GUI Agents
MAI-UI is a cutting-edge open-source project that implements a family of foundation GUI (Graphical User Interface) agent models capable of interpreting natural language and performing real-world GUI navigation and control tasks across mobile and desktop environments. Developed by Tongyi-MAI (Alibaba’s research initiative), the MAI-UI models are multimodal agents trained to understand user instructions and corresponding screenshots, grounding those instructions to on-screen elements and generating sequences of GUI actions such as taps, swipes, text input, and system commands. ... -
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rLLM
Democratizing Reinforcement Learning for LLMs
...With rLLM, developers can define custom “agents” and “environments,” and then train those agents via reinforcement learning workflows, possibly surpassing what vanilla fine-tuning or supervised learning might provide. The project is designed to support large-scale language models (including support for big models via integrated training backends), making it relevant for state-of-the-art research and production use. The framework includes tools for defining workflows, specifying objectives or reward functions, and managing training/policy updates across possibly distributed settings.
