APIs

🔍 Hash-sum: b5f133b3b555561130e698122ba9bedb | 🕓 Last update: 2026-07-19 Verify Processor: high single-core performance needed for token latency RAM: 48 GB […]

🧩 Hash sum → a730623187f4a1f5d8c735b92c9fa7d1 — Update date: 2026-07-14 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM:

📡 Hash Check: e1453f87f2d9eaebd0bced8e3a2ce5cd | 📅 Last Update: 2026-07-17 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16

📘 Build Hash: f122170fbfaf499b4a2cdc79ee2a8f17 • 🗓 2026-07-17 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM:

📤 Release Hash: bc94113e4384d70dafa62149bd7b3049 • 📅 Date: 2026-07-14 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB or

🗂 Hash: 3ac923ffde1da07012656952b08545ba • Last Updated: 2026-07-17 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 64

🧾 Hash-sum — 906a1e4d95fa4992374ac291731880e5 • 🗓 Updated on: 2026-07-14 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM:

🧾 Hash-sum — 2621327f34a64a24f0841137f61867a0 • 🗓 Updated on: 2026-07-11 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM:

For an instant local deployment, running a pre-configured shell script is ideal. Follow the guidelines below to continue. No manual

If you want the fastest local installation for this model, use standard pip packages. Go through the configuration rules shown