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Source channel @githubtrending · Post #15249 · Oct 26

#html#ech#fail2ban#http#mixed#ml_dsa_65#ml_kem_768#post_quantum#reality#shadowsocks#shadowsocks2022#tls#trojan#tunnel#vless#vmess#wireguard#x25519#xtls_rprx_vision#xtls_rprx_vision_udp443 3X-UI is an easy-to-use, open-source web control panel for managing Xray-core VPN servers. It supports many VPN protocols like VMess, VLESS, Shadowsocks, Trojan, and WireGuard, letting you configure, monitor traffic, manage users, and set limits through a simple web interface. It includes features like automatic SSL certificates, traffic statistics, multi-user support, and security options such as firewall rules and IP bans. This makes managing VPN servers faster, more secure, and accessible even if you are not an expert, helping you control your VPN setup efficiently and safely. https://github.com/MHSanaei/3x-ui

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Илья AGI TV 🤖

@ilia_plasma · Post #148 · 10/08/2023, 12:16 PM

Пока весь мир ждет доступа к новой модели со зрением GPT-4V(ision), опенсорс команда (пара азитов со степенью PhD из американских вузов) уже выпустили свой аналог и бесплатную версию #LLaVA (Large Language and Vision Assistant), которая выдает результат (не) хуже GPT4V и может работать локально. Вот такая скорость развития и конкуренции в этом новом #AI рынке. 🧠LLava - вебсайт 📄WhitePaper 🧬Github code 🔋Demo для потестить на своих дикпиках 🦒Colab (для запуска у себя на серваке)

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@githubtrending · Post #15600 · 04/04/2026, 11:30 AM

#python#apple_silicon#florence2#idefics#llava#llm#local_ai#mlx#molmo#paligemma#pixtral#vision_framework#vision_language_model#vision_transformer MLX-VLM lets you run, chat with, and fine-tune Vision Language Models (VLMs) plus audio/video models on your Mac using MLX—install easily with `pip install -U mlx-vlm`. Use CLI for quick text/image/audio generation (e.g., `mlx_vlm.generate --model ... --image photo.jpg`), Gradio UI for chats, Python scripts, or a FastAPI server with OpenAI-compatible endpoints supporting multi-images/videos. Features like TurboQuant cut KV cache memory by 76%, and LoRA/QLoRA fine-tuning works on consumer hardware. You benefit by experimenting with powerful multimodal AI locally—fast, memory-efficient, no cloud costs, perfect for Mac users tweaking models affordably. https://github.com/Blaizzy/mlx-vlm