Search Results for "memory usage" - Page 2
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207 projects for "memory usage" with 1 filter applied:
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FlexLLMGen
Running large language models on a single GPU
...The system focuses on high-throughput generation workloads where large batches of text must be processed quickly, such as large-scale data extraction or document analysis tasks. Instead of requiring expensive multi-GPU systems, the framework uses techniques such as memory offloading, compression, and optimized batching to run large models on commodity hardware. The architecture distributes computation and memory usage across the GPU, CPU, and disk in order to maximize the number of tokens processed during inference. This design allows organizations to deploy powerful language models for high-volume tasks without the infrastructure costs typically associated with large-scale AI systems. ... -
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Claude Cognitive
Persistent context and multi-instance coordination
Claude Cognitive is an advanced memory and context-management extension designed to address the stateless limitations of Claude Code by giving the model a form of persistent “working memory” and multi-instance coordination. It introduces an attention-based context router that prioritizes files and content relevant to the current development discussion — tagging them as HOT, WARM, or COLD based on recency and keyword activation — so Claude Code doesn’t waste token budget rereading irrelevant code. ... -
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SAM 2
The repository provides code for running inference with SAM 2
SAM2 is a next-generation version of the Segment Anything Model (SAM), designed to improve performance, generalization, and efficiency in promptable image segmentation tasks. It retains the core promptable interface—accepting points, boxes, or masks—but incorporates architectural and training enhancements to produce higher-fidelity masks, better boundary adherence, and robustness to complex scenes. The updated model is optimized for faster inference and lower memory use, enabling real-time... -
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JetCache
JetCache is a Java cache framework
JetCache is a Java cache abstraction that provides uniform usage for different caching solutions. It provides more powerful annotations than those in Spring Cache. The annotations in JetCache support native TTL, two-level caching, and automatically refresh in distributed environments, also you can manipulate Cache instances by your code. Currently, there are four implementations: RedisCache, TairCache(not open source on github), CaffeineCache (in memory) and a simple LinkedHashMapCache (in memory). -
Your monitoring isn't a stack. It's a pile. Fix that.Replace Datadog, New Relic, and Sentry without adding three more dashboards.
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retro-go
Retro emulation for the ODROID-GO and other ESP32 devices
...Its design makes it ideal for DIY handheld consoles and hobbyist hardware projects, where minimal resource usage is critical. -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language... -
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lru-cache
A fast cache that automatically deletes the least recently used items
node-lru-cache is a high performance in memory caching library for Node.js that automatically evicts the least recently used items when the cache reaches its size limit. It is designed to provide predictable memory usage while maintaining fast lookup and update operations. The library is widely used in server side JavaScript applications where efficient caching can significantly improve performance. -
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Tracy Profiler
Frame profiler
A real-time, nanosecond resolution, remote telemetry, hybrid frame, and sampling profiler for games and other applications. Tracy supports profiling CPU (Direct support is provided for C, C++, Lua and Python integration. At the same time, third-party bindings to many other languages exist on the internet, such as Rust, Zig, C#, OCaml, Odin, etc.), GPU (All major graphic APIs: OpenGL, Vulkan, Direct3D 11/12, OpenCL.), memory allocations, locks, context switches, automatically attribute... -
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JMX Exporter
A process for exposing JMX Beans via HTTP for Prometheus consumption
...It can be also run as a standalone HTTP server and scrape remote JMX targets, but this has various disadvantages, such as being harder to configure and being unable to expose process metrics (e.g., memory and CPU usage). Running the exporter as a Java agent is strongly encouraged. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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LangChainGo
LangChain for Go, the easiest way to write LLM-based programs in Go
...LangChainGo also includes support for embeddings, semantic search, and retrieval-augmented generation, enabling developers to build data-aware applications. With built-in abstractions for agents and tool usage, it enables the creation of systems that can reason, take actions, and interact with their environment. -
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TechCPP
C++ learning and interview guide aimed at back-end systems developers
...The repository organizes topics so you can quickly locate weak spots before interviews or brush up for day-to-day coding. It’s equally useful as a revision handbook and as a roadmap for engineers moving from “effective C++ usage” toward “systems-level competence”. -
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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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Whisper-WebUI
A Web UI for easy subtitle using whisper model
...Built with Gradio, it allows users to upload audio or video files, process them locally, and generate accurate text outputs without relying on command-line tools. The platform integrates optimized implementations such as faster-whisper, significantly improving transcription speed and reducing memory usage compared to standard models. It supports multiple input sources including local files, YouTube content, and microphone input, making it versatile for different workflows. Whisper WebUI also includes advanced preprocessing and postprocessing features such as voice activity detection, background music separation, and speaker diarization, enabling more accurate and structured outputs. -
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Laravel Chunk Upload
The basic implementation for chunk upload with multiple providers
Laravel Chunk Upload simplifies chunked uploads with support for multiple JavaScript libraries atop Laravel's file upload system, designed with a minimal memory footprint. Features include cross-domain request support, automatic cleaning, and intuitive usage. -
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Mistral Finetune
Memory-efficient and performant finetuning of Mistral's models
mistral-finetune is an official lightweight codebase designed for memory-efficient and performant finetuning of Mistral’s open models (e.g. 7B, instruct variants). It builds on techniques like LoRA (Low-Rank Adaptation) to allow customizing models without full parameter updates, which reduces GPU memory footprint and training cost. The repo includes utilities for data preprocessing (e.g. reformat_data.py), validation scripts, and example YAML configs for training variants like 7B base or... -
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Swift Guide
Swift Featured Projects in brain Mapping
SwiftGuide is a comprehensive, community-maintained guide to the Swift programming language, designed to serve as both a learning resource and a handy reference. It covers all major language aspects: syntax, control flow, functions, closures, generics, protocols, extensions, memory management, concurrency, and the standard library. Each topic typically includes clear explanations, annotated code snippets, and tips for best practices, helping readers understand both how features work and how... -
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Symfony VarExporter
Allows exporting any serializable PHP data structure to plain PHP code
Symfony VarExporter is a component that provides fast and safe serialization of PHP objects, especially those involving closures, services, or references. Unlike traditional serialize() or var_export(), VarExporter produces PHP code that can be re-evaluated safely and efficiently, which is useful in caching, dependency injection, and configuration scenarios. -
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Claw Compactor
14-stage Fusion Pipeline for LLM token compression
Claw Compactor is a utility designed to optimize and manage the context limitations inherent in AI agent systems, particularly those built on OpenClaw-like architectures. It addresses the challenge of finite context windows in language models by compressing or summarizing historical interactions while preserving essential information. The system works by transforming older conversation data into condensed representations that maintain continuity without exceeding token limits. This approach... -
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verl-agent
Designed for training LLM/VLM agents via RL
verl-agent is an open-source reinforcement learning framework designed to train large language model agents and vision-language model agents for complex interactive environments. Built as an extension of the veRL reinforcement learning infrastructure, the project focuses on enabling scalable training for agents that perform multi-step reasoning and decision-making tasks. The framework supports multi-turn interactions between agents and their environments, allowing the system to receive... -
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Monitoring Plugins
A suite of Monitoring Plugins (formerly known as nagios-plugins)
Monitoring Plugins is a collection of open-source plugins designed to work with Nagios and other monitoring systems. These plugins perform various checks on hosts and services, providing administrators with essential information about system health and performance. The suite includes a wide range of plugins written in C, Perl, and other languages. -
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OpenClaw Opik Observability Plugin
Official plugin for OpenClaw that exports agent traces to Opik
...The project integrates directly with OpenClaw’s plugin architecture so that developers can capture detailed runtime information about how their agents behave while executing tasks. Each time an AI agent performs an action—such as calling a large language model, invoking a tool, accessing memory, or delegating to a sub-agent—the plugin records the full interaction and sends it to Opik for analysis and visualization. This allows developers to inspect inputs, outputs, token usage, latency, and execution flow across complex multi-step agent workflows. The goal of the project is to provide transparency into the internal reasoning and operational pipeline of agent systems so developers can diagnose failures, control costs, and improve reliability. -
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uemacs
Random version of microemacs with my private modificatons
uemacs is Linus Torvalds’ compact take on MicroEMACS, a tiny, fast, terminal-friendly text editor. The codebase prizes simplicity and portability, making it feasible to build and run on a variety of Unix-like systems without heavyweight dependencies. Its feature set is deliberately modest compared to full Emacs, favoring a quick, predictable editing experience for everyday text manipulation. The project serves as both a functional tool and an example of clean, old-school C systems... -
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shimmy
Python-free Rust inference server
...This compatibility enables developers to replace remote AI services with locally hosted models while keeping their existing software architecture intact. Shimmy focuses on performance and simplicity, using efficient runtime components to minimize memory usage and startup time compared to heavier inference frameworks. It supports modern model formats such as GGUF and SafeTensors and can automatically discover models stored locally or in common directories used by other AI tools. Advanced capabilities include CPU offloading for Mixture-of-Experts models and GPU acceleration, enabling large models to run on consumer hardware with limited VRAM. -
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Down
Streaming downloads using Net::HTTP, http.rb or HTTPX
Down is a small, reliable Ruby library for downloading files that favors correctness, streaming, and clear error handling. It follows redirects safely, supports timeouts and retries, and streams responses to disk to keep memory usage low—ideal for large downloads or server environments. The API returns file-like objects (often Tempfile) with helpful metadata such as original filename and content type, which plays nicely with file-attachment libraries and background jobs. Multiple HTTP backends are supported, letting you choose between Net::HTTP or faster/feature-rich clients while keeping a consistent interface. ... -
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LangChain for Java
LangChain4j is an open-source Java library
LangChain for Java is an open-source Java framework designed to simplify the development of applications powered by large language models. The library provides a unified API that allows developers to connect Java applications to multiple AI providers and embedding databases without having to implement separate integrations for each service. Its architecture includes abstractions for prompts, chat interactions, document processing, embeddings, and vector storage, enabling developers to build...
