Finetunes

📘 Build Hash: 1d22b4fec188634021b8df1f99436bbc • 🗓 2026-07-21 Verify Processor: high single-core performance needed for token latency RAM: 32 GB highly recommended for 26B+ GGUF models Storage: extra room for future model updates and datasets GPU: 16 GB+ video memory highly

📊 File Hash: 7f4604e6dc6ba5a25a8df40d44c5aa71 — Last update: 2026-07-17 Verify CPU: multi-threading optimized for fast prompt processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: RTX

🛠 Hash code: b0b3316b4950c7bb49e39ca9f459ae57 — Last modification: 2026-07-19 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers GPU: 16

🛠 Hash code: 6b54cf029f5474a4d5e7a216ff4d1fa5 — Last modification: 2026-07-18 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 80 GB NVMe SSD required for fast model weights

🔐 Hash sum: 237dffb901425d8cc37cdd196231d50f | 📅 Last update: 2026-07-15 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU:

🛠 Hash code: 3a48d5bae2d9ba29ae4f69ab02655a3f — Last modification: 2026-07-14 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference