4 مستودعات
Techniques for handling and concatenating real-time token streams from language models to optimize latency.
Distinct from Stream Processing: Candidates focus on general data engineering or network buffers, not the specific application of streaming LLM outputs.
Explore 4 awesome GitHub repositories matching artificial intelligence & ml · LLM Stream Processing. Refine with filters or upvote what's useful.
Eino is an AI agent development kit and LLM application framework designed for building autonomous agents and orchestrating complex language model workflows. It serves as a multi-agent orchestration engine and workflow orchestrator, providing a graph-based execution model to route data between models, tools, and retrievers. The framework distinguishes itself through a robust set of multi-agent coordination patterns, including supervisor-led management, sequential flows, and autonomous reasoning loops like ReAct. It features advanced agent execution controls such as active turn preemption, che
Handles real-time model outputs through stream processing and concatenation to minimize response latency.
ollama-python is a Python client for interacting with large language models. It provides an interface for sending prompts to receive text and chat completions, as well as a dedicated client for generating numerical vector embeddings from text. The project includes a wrapper that emulates the OpenAI API, allowing applications built for that standard to interact with local models. It also provides a non-blocking asynchronous client for executing concurrent requests. The library covers the full model lifecycle, including the ability to pull, create, list, and delete models within a local enviro
Processes model responses piece by piece using Python generators for real-time text streaming.
This project is a long context inference engine and optimizer designed to process infinite text streams using large language models without memory growth or performance degradation. It serves as a system for maintaining constant memory usage during the generation of text from arbitrarily long input sequences. The implementation utilizes a rolling key-value cache manager and attention sink mechanisms to stabilize the attention process during continuous stream processing. By retaining initial tokens and employing a sliding window of key-value pairs, the system enables constant-time inference an
Handles and concatenates real-time token streams from language models to optimize latency and throughput.
Iggy هي منصة بث رسائل موزعة ووسيط رسائل متعدد البروتوكولات يعمل كمخزن سجلات موزع ومستمر. يوفر بنية تحتية لنشر واستهلاك الرسائل الثنائية باستخدام سجل إلحاق فقط (Append-only log)، مما يضمن التوافر العالي واتساق البيانات عبر العقد من خلال Viewstamped Replication. تتميز المنصة ببنية تحتية متخصصة لبث نماذج اللغة الكبيرة (LLM)، والتي تستخدم بروتوكول خادم لربط نماذج اللغة الكبيرة ببيانات البث وعناصر تحكم النظام. يتضمن ذلك بروتوكولات موحدة لإدارة السياق وربط البيانات عبر HTTP أو الإدخال والإخراج القياسي. يغطي النظام مجموعة واسعة من القدرات بما في ذلك تنسيق خط أنابيب البيانات مع إضافات المصدر والمصب النمطية، وتنسيق مجموعة المستهلكين للتوسع الأفقي، ودعم النقل متعدد البروتوكولات عبر TCP و QUIC و HTTP و WebSocket. كما يدمج بدائيات أمان مثل تشفير AES-256-GCM للبيانات في حالة السكون وأثناء النقل، ويوفر إمكانية المراقبة عبر مقاييس Prometheus، وتتبع OpenTelemetry، ولوحة تحكم ويب تشغيلية. يمكن نشر الخادم باستخدام صور الحاويات وتنسيقه من خلال Kubernetes.
Exposes a specialized server protocol that enables large language models to control message streaming operations.