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🛡️ Checksum: 017b733675422f31d5e802369345d557 — ⏰ Updated on: 2026-07-18 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking Advanced Document Understanding with GLM-OCR The GLM-OCR…
🧩 Hash sum → b30cbd9eab6758e0933d13b8995ddee0 — Update date: 2026-07-20 Verify Processor: high single-core performance needed for token latency RAM: enough space for background apps and OS overhead Disk Space:70 GB free space for full FP16 weights storage GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Power of Molmo2-8B: A Compact Vision-Language Model The…
🔗 SHA sum: 7c9d67db6971a0cf20fc05c33c79d3d1 | Updated: 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: at least 32 GB in dual-channel mode for bandwidth Storage: extra room for future model updates and datasets Graphics: 12 GB VRAM minimum required for basic quantization Unveiling the Qwen3.5-9B-GGUF Model: A Breakthrough in Open-Source Language Models The…
💾 File hash: 46dc105428197ee3c8e4651e4b041d5f (Update date: 2026-07-17) Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 100 GB for multi-modal model vision components Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unveiling the Power of Qwen3.5-2B: A Compact Language Model for Efficiency…
📘 Build Hash: 696daf515817e823c869817ae84599d4 • 🗓 2026-07-20 Verify Processor: high single-core performance needed for token latency RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Power of Sulphur-2-base: Revolutionizing Scientific Reasoning and Code…