Search Results for "task" - Page 8
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Showing 245 open source projects for "task"
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TaskWeaver
A code-first agent framework for seamlessly planning analytics tasks
TaskWeaver is a multi-agent AI framework designed for orchestrating autonomous agents that collaborate to complete complex tasks. -
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Eva AI
Eva is an A.I. assistant that helps users multi-task.
Eva is an A.I. assistant that has the purpose of helping users multi-task. It also has the purpose of helping people with disabilities use the computer with a greater ease. Eva can open and close system related and non-system related applications, search content on web applications, set timers, and take screenshots. Tell Eva "Listen" or "Hey listen" followed by a command. For more instructions, check the instruction manual included in the application. -
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GLM-4-32B-0414
Open Multilingual Multimodal Chat LMs
GLM-4-32B-0414 is a powerful open-source large language model featuring 32 billion parameters, designed to deliver performance comparable to leading models like OpenAI’s GPT series. It supports multilingual and multimodal chat capabilities with an extensive 32K token context length, making it ideal for dialogue, reasoning, and complex task completion. The model is pre-trained on 15 trillion tokens of high-quality data, including substantial synthetic reasoning datasets, and further enhanced with reinforcement learning and human preference alignment for improved instruction-following and function calling. Variants like GLM-Z1-32B-0414 offer deep reasoning and advanced mathematical problem-solving, while GLM-Z1-Rumination-32B-0414 specializes in long-form, complex research-style writing using scaled reinforcement learning and external search tools. ... -
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Email to Event - ETE
The python App/Skrypt automaticly add important events into calendar.
...Install LM studio - recomended for GPU compute 3. Run run_setings.bat and set your authentificators for email***/calendar and etg. 4. Push button SAVE 5. Push button PLAN for add task to Time scheduler 6. Check by run run_ETE.bat **Model must understand your language, test before use! ***In emal seting(usualy on web) create a new folder and set auto COPY! More information and complete instalation guidein in READ ME file. USE THEM! If you find some bug or something else, please write on: jan_pisa<a>email.cz Or Github isue -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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Shinkai: Local AI Agents
Shinkai allows you to create advanced AI (local) agents effortlessly
...Key Features: - No-Code Agent Creation - Build specialized agents (trading bots, sentiment trackers, etc.) with simple descriptions - Multi-Agent Collaboration - Agents work together to solve complex problems - Crypto Integration - Built-in support for decentralized payments and transactions - Flexible AI Models - Choose from cloud models (GPT-4, Claude) or run locally - Universal Compatibility - Works with Model Context Protocol (MCP) for cross-platform integration - Local Security - Crypto keys and computations stay on your device Shinkai transforms AI from single-task tools into collaborative, autonomous systems that can operate in decentralized networks while maintaining privacy and security. -
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PoseidonQ - AI/ML Based QSAR Modeling
ML based QSAR Modelling And Translation of Model to Deployable WebApps
- This Software was made with an intention to make QSAR/QSPR development more efficient and reproducible. - Published in ACS, Journal of Chemical Information and Modeling . Link : https://pubs.acs.org/doi/10.1021/acs.jcim.4c02372 - Simple to use and no compromise on essential features necessary to make reliable QSAR models. - From Generating Reliable ML Based QSAR Models to Developing Your Own QSAR WebApp. For any feedback or queries, contact kabeermuzammil614@gmail.com - Available on... -
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snorkel
A system for quickly generating training data with weak supervision
...Snorkel Flow, an end-to-end machine learning platform for developing and deploying AI applications. Snorkel Flow incorporates many of the concepts of the Snorkel project with a range of newer techniques around weak supervision modeling, data augmentation, multi-task learning, data slicing and structuring. -
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MuJoCo MPC
Real-time behaviour synthesis with MuJoCo, using Predictive Control
MuJoCo MPC (MJPC) is an advanced interactive framework for real-time model predictive control (MPC) built on top of the MuJoCo physics engine, developed by Google DeepMind. It allows researchers and roboticists to design, visualize, and execute complex control tasks for simulated or real robotic systems. MJPC integrates a high-performance GUI and multiple predictive control algorithms, including iLQG, gradient descent, and Predictive Sampling — a competitive, derivative-free method that... -
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BIG-bench
Beyond the Imitation Game collaborative benchmark for measuring
...Tasks are intentionally heterogeneous: some are multiple-choice with exact scoring, others are free-form generation judged by model-based or human evaluation. The suite provides a common JSON task format and an evaluation harness so research groups can contribute new tasks and reproduce results consistently. It emphasizes robustness analysis—looking at scale trends, calibration, and areas where models systematically fail—to guide model development beyond raw accuracy. BIG-bench is as much a community process as a dataset, encouraging open sharing of tasks and findings to keep evaluations fresh and comprehensive. -
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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TensorHouse
A collection of reference Jupyter notebooks and demo AI/ML application
TensorHouse is a scalable reinforcement learning (RL) platform that focuses on high-throughput experience generation and distributed training. It is designed to efficiently train agents across multiple environments and compute resources. TensorHouse enables flexible experiment management, making it suitable for large-scale RL experiments in both research and applied settings. -
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Ailice
AIlice is a fully autonomous, general-purpose AI agent
AIlice is an open-source autonomous AI agent framework built to function as a general-purpose assistant that can plan, decompose, and execute complex tasks through a structured multi-agent architecture. The project presents itself as a standalone assistant powered by open-source language models, with an internal design that treats user requests almost like executable programs rather than simple chat prompts. Its core IACT architecture allows the system to break large goals into smaller... -
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Nextpy
Self-Modifying Framework from the Future
NextPy is a Python-based framework for building AI-powered automation agents, allowing developers to create intelligent, rule-based workflows. -
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Punica
Serving multiple LoRA finetuned LLM as one
...The system includes specialized CUDA kernels that enable batched GPU operations across different LoRA models simultaneously. This design allows a single GPU cluster to host many task-specific models while maintaining high throughput and minimal latency. The architecture also includes scheduling mechanisms that coordinate requests from multiple tenants and distribute workloads efficiently across available resources. -
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Transformers4Rec
Transformers4Rec is a flexible and efficient library
...In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task. -
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XAgent
An Autonomous LLM Agent for Complex Task Solving
XAgent is an AI-driven autonomous agent framework capable of handling multi-step tasks across different domains. It enables AI agents to perform decision-making, task planning, and self-learning based on user-defined objectives, making it ideal for automation and research applications. -
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YiVal
Your Automatic Prompt Engineering Assistant for GenAI Applications
...YiVal supports integration with various LLM providers and can orchestrate experiments across different models, making it adaptable to evolving AI ecosystems. It also includes evaluation pipelines that help quantify output quality based on criteria such as accuracy, coherence, or task-specific benchmarks. -
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EvaDB
Database system for building simpler and faster AI-powered application
...To use an AI model, the user needs to program against multiple low-level libraries, like PyTorch, Hugging Face, Open AI, etc. This tedious process often leads to a complex AI app that glues together these libraries to accomplish the given task. This programming complexity prevents people who are experts in other domains from benefiting from these models. Running these deep learning models on large document or video datasets is costly and time-consuming. For example, the state-of-the-art object detection model takes multiple GPU years to process just a week’s videos from a single traffic monitoring camera. ... -
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LangChain Apps on Production with Jina
Langchain Apps on Production with Jina & FastAPI
Jina is an open-source framework for building scalable multi-modal AI apps on Production. LangChain is another open-source framework for building applications powered by LLMs. long-chain-serve helps you deploy your LangChain apps on Jina AI Cloud in a matter of seconds. You can benefit from the scalability and serverless architecture of the cloud without sacrificing the ease and convenience of local development. And if you prefer, you can also deploy your LangChain apps on your own... -
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ChatGLM Efficient Tuning
Fine-tuning ChatGLM-6B with PEFT
...The project exposes practical switches for quantization and mixed precision, allowing bigger models to fit into limited VRAM. It includes examples for instruction tuning and dialogue datasets, making it straightforward to stand up a task-specific assistant. Because the code leans on widely used libraries, you can bring your own datasets and monitoring tools with minimal glue. For builders who want results fast, it’s a pragmatic way to specialize ChatGLM while controlling costs and turnaround time. -
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OpenNMT-tf
Neural machine translation and sequence learning using TensorFlow
OpenNMT is an open-source ecosystem for neural machine translation and neural sequence learning. OpenNMT-tf is a general-purpose sequence learning toolkit using TensorFlow 2. While neural machine translation is the main target task, it has been designed to more generally support sequence-to-sequence mapping, sequence tagging, sequence classification, language modeling. Models are described with code to allow training custom architectures and overriding default behavior. For example, the following instance defines a sequence-to-sequence model with 2 concatenated input features, a self-attentional encoder, and an attentional RNN decoder sharing its input and output embeddings. ... -
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Promptify
se GPT or other prompt based models to get structured output
...The project provides tools that help developers generate structured prompts for different NLP tasks and apply them across multiple generative AI systems. Instead of manually crafting prompts for each task, Promptify introduces a unified architecture that combines prompt templates, language model interfaces, and processing pipelines into a single framework. This approach allows developers to perform tasks such as text classification, named entity recognition, question answering, and information extraction using consistent prompt templates. ... -
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ReAct Prompting
Synergizing Reasoning and Acting in Language Models
...This alternating sequence of reasoning, acting, and observing results allows the model to gather additional information and refine its decision-making process during task execution. The framework has been tested on several benchmarks including question answering, fact verification, and interactive decision-making tasks, demonstrating improved performance compared to methods that rely only on reasoning. -
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finetuner
Task-oriented finetuning for better embeddings on neural search
Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive. Jina AI’s Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware. Create... -
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TaskMatrix
Enable sending and receiving images during chatting
...Originally introduced alongside the Visual ChatGPT concept, TaskMatrix acts as an orchestration framework where a central language model delegates subtasks to domain-specific AI systems such as image generators, segmentation tools, or recognition models. The architecture focuses on modularity, allowing new APIs and foundation models to be integrated as interchangeable task-solving components. The project also explores low-code human-AI interaction workflows that improve controllability and transparency during complex task execution. -
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Gorilla CLI
LLMs for your CLI
Gorilla CLI powers your command-line interactions with a user-centric tool. Simply state your objective, and Gorilla CLI will generate potential commands for execution. Gorilla today supports ~1500 APIs, including Kubernetes, AWS, GCP, Azure, GitHub, Conda, Curl, Sed, and many more. No more recalling intricate CLI arguments.
