#python#deep_learning#inference#llm#nlp#pytorch#transformer
Nano-vLLM is a small, fast, and easy-to-understand tool for running large language models offline. It matches the speed of bigger systems like vLLM but uses only about 1,200 lines of clean Python code, making it simple to read and modify. It includes smart features like prefix caching and tensor parallelism to boost performance. You can install it easily and run models like Qwen3-0.6B on your own GPU. This tool is great if you want fast, efficient AI inference without complex setups, ideal for learning, research, or small deployments on limited hardware.
https://github.com/GeeeekExplorer/nano-vllm
#java
The Model Context Protocol (MCP) Java SDK helps developers connect AI models with tools and data sources using a standardized interface. It supports both synchronous and asynchronous communication, making it flexible for different applications. The SDK includes features like tool management, logging, and multiple transport options, which simplify interactions between AI systems and external tools. This benefits users by providing a consistent way to integrate AI with various data sources, reducing the complexity of managing multiple connectors for different tools.
https://github.com/modelcontextprotocol/java-sdk
#java
BookLore is a self-hosted web app that helps you organize, manage, and read your personal book collection easily. You can sort books into libraries and shelves, automatically get book details from sources like Goodreads, and track your reading progress on PDFs and eBooks with a built-in reader. It supports multiple users with separate accounts and secure login options, so everyone can manage their own books without mixing collections. You can upload many books at once, share books by email (great for Kindle users), and browse books via compatible reading apps. This gives you full control over your digital library with a clean, modern interface and continuous updates[1][2][5].
https://github.com/adityachandelgit/BookLore
JSpecify — стандартизация Java-аннотаций для статического анализа кода и взаимодействия между языками JVM.
Если вы знакомы с Java или изучали исходный код, то одним из решений проблемы null является использование аннотаций nullability. Однако реализаций таких аннотаций много: JetBrains, Android Jetpack, Spring, Uber и другие создали свои версии.
Решений очень много, и возникла проблема выбора и поддержки. Хотелось бы иметь стандарт в Java, но договориться не удалось.
Консорциум компаний и команд из Google, JetBrains, Meta, Kotlin, Android, Spring, PMD, Sonar, EISOP и других объединился и создал единый стандарт, который обязуются поддерживать в своих решениях.
JSpecify 1.0 сосредоточен на nullability и содержит четыре аннотации: @NonNull, @Nullable, @NullMarked, @NullUnmarked.
Интеграция уже началась в библиотеки Jetpack Android и Kotlin.
#java
Java Backend
1 - dars. Kirish
- JVM, JRE, JDK
- Java qanday ishlaydi?
- O‘zgaruvchilar
- Maʼlumot turlari
- Kommentariyalar
- Chiqarish
Mentor : Hasan Po‘latov
#java
👉@ummat_uchun_dasturlash