Weights

📡 Hash Check: 85758c1b090e7ebd12abf64f2fecdb8e | 📅 Last Update: 2026-07-23 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: […]

🛡️ Checksum: 229c517ff2fd4554177c7e930f370e83 — ⏰ Updated on: 2026-07-22 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or

📡 Hash Check: 7868c2391bdc6010603a7c1cd9a6ad15 | 📅 Last Update: 2026-07-20 Verify Processor: high single-core performance needed for token latency RAM: 48

🗂 Hash: 7827f58cdb06e1bac8cdcd0cc6c80d85 • Last Updated: 2026-07-21 Verify Processor: high single-core performance needed for token latency RAM: minimum 16 GB

📘 Build Hash: 765c2b4c1f72ff42aacdee1a56e43a6b • 🗓 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB

🔒 Hash checksum: d3ee59599d6f6aaead22a1006f27350f • 📆 Last updated: 2026-07-20 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM:

📦 Hash-sum → 14c0b3431e10998201d97ec807506d6b | 📌 Updated on 2026-07-14 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM:

Running this model locally is fastest when deployed through a PowerShell script. Proceed by following the technical instructions below. No

For an instant local deployment, running a pre-configured shell script is ideal. Review and follow the instructions below. The loader

The shortest path to running this model is by activating Hyper-V features. Follow the straightforward walkthrough provided below. The setup