APIs
Inicio 📄 Hash Value: 8e153560de968ff0a1c2f20e1cf0121d | 📆 Update: 2026-07-22 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 GPU: modern architecture (Ada Lovelace…
Inicio 🧾 Hash-sum — f07160cf0b783a8a2adab973269147ba • 🗓 Updated on: 2026-07-22 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder GPU: modern…
Inicio 🧩 Hash sum → bb420d90e649e65f105fc5bff92b19c1 — Update date: 2026-07-16 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 80 GB NVMe SSD required for fast model weights loading…
Inicio 🔧 Digest: 681af8e0d7a88233a9199a0a5c106732 • 🕒 Updated: 2026-07-16 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphics:…
Inicio 🛡️ Checksum: 7d910bb7112fb640cee915e72d374867 — ⏰ Updated on: 2026-07-17 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 48 GB needed to prevent memory swapping to disk Disk: high-speed SSD 120 GB to cache model layers Graphics: stable…
Inicio 🔒 Hash checksum: a626b3aa398831d8e71320780374ef59 • 📆 Last updated: 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers GPU: 16 GB+…
Inicio 📘 Build Hash: 88c9166c0d86d2f74cb6c94b3059a593 • 🗓 2026-07-15 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: minimum 16 GB for stable 8B model loading Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute…
Inicio If you need a near-instant local setup, just fetch files via a basic curl request. Follow the straightforward walkthrough provided below. The script takes care of fetching the multi-gigabyte model weights. The installer will automatically analyze your hardware and…
Inicio If you want the fastest local installation for this model, use standard pip packages. Simply follow the directions outlined below. The engine will automatically fetch large dependencies in the background. During setup, the script automatically determines and applies the…
