Search Results for "sandbox:/mnt/data/project_plan.pod" - Page 22
Sort By:
Showing 884 open source projects for "sandbox:/mnt/data/project_plan.pod"
View related business solutions-
No-Nonsense Code-to-Cloud Security for Devs | AikidoAikido provides a unified security platform for developers, combining 12 powerful scans like SAST, DAST, and CSPM. AI-driven AutoFix and AutoTriage streamline vulnerability management, while runtime protection blocks attacks.
-
Budgyt Is The Highest Rated Business Budgeting Software In The Market.Budgyt is an easy to use, intuitive platform with a clean simple interface that makes budgeting multiple P&L’s easy to do without needing Excel.
-
1
MiroFlow
Agent framework that enables tool-use agent tasks
MiroFlow is a high-performance open-source framework designed for building intelligent AI agents capable of solving complex reasoning and research tasks. The system introduces a hierarchical architecture that organizes components into control, agent, and foundation layers, allowing developers to manage agent orchestration and tool interactions in a structured manner. One of the core innovations of MiroFlow is its use of agent graphs, which enable flexible orchestration of multiple sub-agents... -
2
Prompt Poet
Streamlines and simplifies prompt design for both developers
...By separating prompt structure from program logic, Prompt Poet encourages iterative prompt design and experimentation without requiring constant changes to application code. The framework supports dynamic prompts that adapt to runtime data, allowing developers to inject variables, context, and examples directly into templates. This approach is particularly useful in production environments where prompt consistency, maintainability, and versioning are important. -
3
AgentEvolver
Towards Efficient Self-Evolving Agent System
...These mechanisms enable agents to continuously improve their capabilities while interacting with complex environments and tools. AgentEvolver also integrates environment sandboxes, experience management systems, and modular data pipelines to support large-scale experimentation. -
4
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. -
Point of Sale. Powerful and Simple.Vibe Retail is an all-in-one retail point-of-sale and operations platform built for single-store and multi-location retailers seeking to unify inventory, sales, staff and customer data from one mobile-friendly interface. The system lets you track inventory across locations and warehouses, handle item variations (size, color, material), manage purchase orders and supplier deliveries, print custom barcodes, and transfer stock between stores in real time. On the sales side, Vibe supports multiple payment types (cards, cash, checks, gift cards, EBT), layaway workflows, serial number tracking, delivery management, loyalty programs and branded receipts. Retailers can integrate with online platforms (such as Shopify and WooCommerce), sync in-store and online sales, access 40+ real-time reports on sales, inventory and performance, set up promotions and discounts, and print receipts from mobile devices.
-
5
PKU Beaver
Constrained Value Alignment via Safe Reinforcement Learning
PKU Beaver is an open-source research project focused on improving the safety alignment of large language models through reinforcement learning from human feedback under explicit safety constraints. The framework introduces techniques that separate helpfulness and harmlessness signals during training, allowing models to optimize for useful responses while minimizing harmful behavior. To support this process, the project provides datasets containing human-labeled examples that encode both... -
6
WebGLM
An Efficient Web-enhanced Question Answering System
...The system is based on the General Language Model architecture and was designed to enable language models to interact directly with web information during the question-answering process. Instead of relying solely on knowledge stored in the model’s training data, the system retrieves relevant web content and integrates it into the reasoning process. WebGLM introduces several components that coordinate this process, including a retrieval module that selects relevant web documents, a generator that produces answers, and a scoring system that evaluates the quality of generated responses. The architecture aims to improve the reliability and usefulness of AI systems that answer questions about current or external knowledge sources. -
7
code-act
Official Repo for ICML 2024 paper
...This approach helps unify reasoning and action planning within large language model agents by using code as the primary interface between the model and the external world. The framework also includes training data, models, and evaluation tools designed to study how language models can become more capable autonomous agents. -
8
LongWriter
Unleashing 10,000+ Word Generation from Long Context LLMs
LongWriter is an open-source framework and set of large language models designed to enable ultra-long text generation that can exceed 10,000 words while maintaining coherence and structure. Traditional large language models can process large inputs but often struggle to generate long outputs due to limitations in training data and alignment strategies. LongWriter addresses this challenge by introducing a specialized dataset and training approach that encourages models to produce longer responses. The system uses an agent-based pipeline called AgentWrite that decomposes large writing tasks into smaller subtasks, allowing the model to produce long documents section by section. ... -
9
LlamaGen
Autoregressive Model Beats Diffusion
LlamaGen is an open-source research project that introduces a new approach to image generation by applying the autoregressive next-token prediction paradigm used in large language models to visual generation tasks. Instead of relying on diffusion models, the framework treats images as sequences of tokens that can be generated progressively using transformer architectures similar to those used for text generation. The project explores how scaling autoregressive models and improving image... -
SIEM | API Security | Log Management SoftwareBuilt on the Graylog Platform, Graylog Security is the industry’s best-of-breed threat detection, investigation, and response (TDIR) solution. It simplifies analysts’ day-to-day cybersecurity activities with an unmatched workflow and user experience while simultaneously providing short- and long-term budget flexibility in the form of low total cost of ownership (TCO) that CISOs covet. With Graylog Security, security analysts can:
-
10
Magicoder
Empowering Code Generation with OSS-Instruct
...This technique uses open-source code repositories as a foundation for generating more realistic and diverse instruction datasets for training language models. By grounding training data in real open-source examples, Magicoder aims to reduce bias and improve the reliability of code generation results compared to models trained solely on synthetic instructions. The project includes model implementations, training resources, and evaluation benchmarks that demonstrate how the approach improves instruction-following and code synthesis capabilities. ... -
11
xLSTM
Neural Network architecture based on ideas of the original LSTM
...By introducing innovations such as matrix-based memory and improved normalization techniques, xLSTM improves the ability of recurrent networks to capture long-range dependencies in sequential data. The architecture aims to provide competitive performance with transformer-based models while maintaining advantages such as linear computational scaling and efficient memory usage for long sequences. Researchers have demonstrated that xLSTM models can scale to billions of parameters and large training datasets while maintaining efficient inference speeds. -
12
Lagent
A lightweight framework for building LLM-based agents
...The framework provides tools and abstractions that allow language models to interact with external tools, execute tasks, and perform multi-step reasoning processes. Instead of using LLMs only for text generation, Lagent enables developers to transform models into agents capable of performing actions such as retrieving data, executing code, or interacting with APIs. The system includes modular components that allow developers to connect different models and tools within the same agent architecture. Its design emphasizes simplicity and flexibility so that developers can experiment with different agent workflows without needing a complex infrastructure setup. ... -
13
OmAgent
Build multimodal language agents for fast prototype and production
OmAgent is an open-source Python framework designed to simplify the development of multimodal language agents that can reason, plan, and interact with different types of data sources. The framework provides abstractions and infrastructure for building AI agents that operate on text, images, video, and audio while maintaining a relatively simple interface for developers. Instead of forcing developers to implement complex orchestration logic manually, the system manages task scheduling, worker coordination, and node optimization behind the scenes. ... -
14
ai-cookbook
Examples and tutorials to help developers build AI systems
...The repository contains examples that demonstrate how to build AI workflows using modern tools such as large language models, autonomous agents, and external APIs. Developers can learn how to construct applications like intelligent assistants, automation pipelines, and AI-powered data analysis tools through step-by-step tutorials and ready-to-run scripts. The code examples are designed to emphasize practical architecture patterns that are commonly used in production environments, helping developers understand how to integrate AI services into software products. -
15
InternVL
A Pioneering Open-Source Alternative to GPT-4o
...The project focuses on scaling vision models and aligning them with large language models so that they can perform tasks involving both visual and textual information. InternVL is trained on massive collections of image-text data, enabling it to learn representations that capture both visual patterns and semantic meaning. The model supports a wide variety of tasks, including visual perception, image classification, and cross-modal retrieval between images and text. It can also be connected to language models to enable conversational interfaces that understand images, videos, and other visual content. ... -
16
TimesFM
Pretrained time-series foundation model developed by Google Research
TimesFM is a pretrained time-series foundation model from Google Research built for forecasting tasks, designed to generalize across many domains without requiring extensive per-dataset retraining. It provides a decoder-only model approach to forecasting, aiming for strong performance even in zero-shot or low-data settings where traditional models often struggle. The project includes code and an inference API intended to make it practical to run forecasts programmatically, with options to use different backends such as Torch or Flax depending on your environment and performance needs. Newer releases emphasize expanded context handling and more flexible forecasting outputs, including quantile forecasting so users can get uncertainty estimates rather than only point predictions. ... -
17
Ling-V2
Ling-V2 is a MoE LLM provided and open-sourced by InclusionAI
...It introduces highly sparse architectures where only a fraction of the model’s parameters are activated per input token, enabling models like Ling-mini-2.0 to achieve reasoning and instruction-following capabilities on par with much larger dense models while remaining significantly more computationally efficient. Trained on more than 20 trillion tokens of high-quality data and enhanced through multi-stage supervised fine-tuning and reinforcement learning, Ling-V2’s models demonstrate strong general reasoning, mathematical problem-solving, coding understanding, and knowledge-intensive task performance. -
18
Agent Lightning
The absolute trainer to light up AI agents
...It’s designed to be compatible with a wide range of agent architectures and frameworks — from LangChain and OpenAI Agent SDKs to AutoGen and custom Python agents — making it broadly applicable across different agent tooling ecosystems. Agent-Lightning introduces a lightweight training pipeline that observes agents’ execution traces, converts them into structured data, and feeds them into training algorithms, enabling users to improve agent behaviors systematically. The project emphasizes minimalist integration, so you can drop this into existing systems without extensive rewrites, focusing instead on iterative performance improvement. -
19
JEPA
PyTorch code and models for V-JEPA self-supervised learning from video
...This makes learning focus on semantics and structure, yielding features that transfer well with simple linear probes and minimal fine-tuning. The repository provides training recipes, data pipelines, and evaluation utilities for image JEPA variants and often includes ablations that illuminate which masking and architectural choices matter. Because the objective is non-autoregressive and operates in embedding space, JEPA tends to be compute-efficient and stable at scale. The approach has become a strong alternative to contrastive or pixel-reconstruction methods for representation learning. -
20
DLRM
An implementation of a deep learning recommendation model (DLRM)
...The implementation is optimized for performance at scale, supporting multi-GPU and multi-node execution, quantization, embedding partitioning, and pipelined I/O to feed huge embeddings efficiently. It includes data loaders for standard benchmarks (like Criteo), training scripts, evaluation tools, and capabilities like mixed precision, gradient compression, and memory fusion to maximize throughput. -
21
MoCo (Momentum Contrast)
Self-supervised visual learning using momentum contrast in PyTorch
MoCo is an open source PyTorch implementation developed by Facebook AI Research (FAIR) for the papers “Momentum Contrast for Unsupervised Visual Representation Learning” (He et al., 2019) and “Improved Baselines with Momentum Contrastive Learning” (Chen et al., 2020). It introduces Momentum Contrast (MoCo), a scalable approach to self-supervised learning that enables visual representation learning without labeled data. The core idea of MoCo is to maintain a dynamic dictionary with a momentum-updated encoder, allowing efficient contrastive learning across large batches. The repository includes implementations for both MoCo v1 and MoCo v2, the latter improving training stability and performance through architectural and augmentation enhancements. ... -
22
Animated Drawings
Code to accompany "A Method for Animating Children's Drawings"
...Users can provide rough keyframes or control constraints (pose anchors), and the system fills intermediate frames with fluid animation. The repository includes demonstration apps and notebooks where you can upload or draw shapes and watch animations play. Because the approach is data-driven, it generalizes to new drawings even with varying proportions or stylizations. -
23
DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical features and some dense numerical features. Low-order Extractor learns feature interaction through product between vectors. Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
24
IVY
The Unified Machine Learning Framework
...For example, an existing TensorFlow model, and some useful functions from both PyTorch and NumPy libraries. Choose any framework for writing your higher-level pipeline, including data loading, distributed training, analytics, logging, visualization etc. Choose any backend framework which should be used under the hood, for running this entire pipeline. Choose the most appropriate device or combination of devices for your needs. DeepMind releases an awesome model on GitHub, written in JAX. We'll use PerceiverIO as an example. ... -
25
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...
