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Source channel @githubtrending · Post #15521 · Feb 25

#rust#ai_gateway#ai_gateway_support#envoy#envoyproxy#gateway#generative_ai#llm_gateway#llm_inference#llm_proxy#llm_routing#llmops#llms#openai#prompt#proxy#proxy_server#routing Plano is an AI-native proxy server that handles key tasks for agentic apps like routing between agents, smart LLM model selection, safety guardrails, and automatic traces for observability. Define agents in simple YAML, write basic HTTP code in any language, and start Plano to run multi-agent systems without custom plumbing or framework lock-in. You benefit by building and shipping reliable agents to production much faster, focusing on core logic while gaining safety, low latency, and easy scaling. https://github.com/katanemo/plano

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Machinelearning

@ai_machinelearning_big_data · Post #9091 · 11/27/2025, 10:16 AM

⚡️Qwen3-VL: выпустили технический отчёт по новой линейке VLM Опубликован tech report по Qwen3-VL - мультимодальным моделям, работающим с изображениями и текстом. Кратко : - Три модели собрали 1M+ загрузок за месяц. - Qwen3-VL-8B - более 2M скачиваний. - Линейка развивает идеи Qwen2.5-VL (2800+ цитирований). Что описано в отчёте: - Архитектура vision–language модели. - Процесс обучения: pretraining + post-training. - Источники данных и методы фильтрации. - Сравнения с другими VLM и ключевые метрики. 🔗 PDF: https://arxiv.org/pdf/2511.21631 🔗Видео: https://www.youtube.com/watch?v=clwFmuJX_wQ @ai_machinelearning_big_data #Qwen#Qwen3#QwenVL#Qwen3VL#LLM#AIModel

AI & Law

@ai_and_law · Post #108 · 09/10/2023, 08:33 AM

🌟 AI Sunday Wonders: Meet TinyLlama, the 550MB AI Model Trained on 3 Trillion Tokens Hello, everyone! In the world of AI, smaller models are gaining immense popularity due to their efficiency on edge devices with limited memory and processing power. Enter TinyLlama, a groundbreaking project led by a research assistant at Singapore University of Technology and Design. Despite its tiny 550MB size, TinyLlama is pre-trained on a massive three trillion tokens. This compact model holds great promise for various applications, including real-time machine translation without the need for an internet connection. The project aims to complete the training of this 1.1 billion Llama model in just 90 days, utilizing 16 A100-40G GPUs. You can track its progress and loss metrics in real-time. TinyLlama shares the same architecture and tokenizer as Meta's Llama 2, making it compatible with open-source projects built on Llama. TinyLlama joins the league of smaller language models like Pythia-1b and MPT-1b, offering developers efficient options for creating cutting-edge AI applications. #TinyLlama#AIModel#AIResearch#MachineLearning#AIInnovation#TinyButMighty