📦 Hash-sum → 40974327a3c9f929258db2b68749ec4d | 📌 Updated on 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: modern architecture (Ada Lovelace / Ampere minimum) Unveiling the Qwen3.6-35B-A3B-GGUF: A Game-Changing Large Language Model The …
Category: Functions
Functions
Jul 22
Zero-Click Run dots.mocr on Copilot+ PC Complete Walkthrough Windows
📊 File Hash: 383f0b9576f2045687585c3ba3e70a16 — Last update: 2026-07-16 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention The dots.mocr Advantage The dots.mocr model offers unparalleled efficiency and …
Jul 20
gemma-4-26B-A4B-it-GGUF Locally via LM Studio Step-by-Step
🛡️ Checksum: 621ecc8be041c2bac3b6650c9ae566cb — ⏰ Updated on: 2026-07-15 Verify Processor: high single-core performance needed for token latency RAM: enough space for background apps and OS overhead Storage: extra room for future model updates and datasets Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Gemma-4-26B-A4B-it-GGUF Model: A State-of-the-Art Addition to the …
Jul 19
Launch MiniCPM-V-4.6 via WebGPU (Browser) Fully Jailbroken
🗂 Hash: 78fd9c5fa5854ee6906826e6fded5b9f • Last Updated: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space GPU: modern architecture (Ada Lovelace / Ampere minimum) Digital Visionary: Empowering Real-Time Multimodal Understanding The MiniCPM-V-4.6 represents a groundbreaking achievement …
Jul 19
Full Deployment embeddinggemma-300M-GGUF Windows 10 For Low VRAM (6GB/8GB) Offline Setup
🛡️ Checksum: fb2e907effe3478a0368b07c28e97801 — ⏰ Updated on: 2026-07-13 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or higher for smooth 32k context lengths Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Power of Compact Embeddings for NLP Tasks The …
