EXL2

💾 File hash: 20f22777b588a892168e474e1beb9c59 (Update date: 2026-07-19)VerifyProcessor: next-gen chip for heavy context processing RAM: 48 GB needed to prevent memory swapping to disk Disk: 150+ GB for high-context vector database storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Breaking New Grounds in Open-Source Language ModelsThe gemma-4-E4B-it model represents a significant milestone in the evolution of open-source language models, marking a substantial leap forward in terms of scale and efficiency. By harnessing massive computational resources, this model has achieved

📡 Hash Check: 7766a5c2c47677aaed891fee37da000d | 📅 Last Update: 2026-07-17VerifyProcessor: 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 Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Power of Qwen3.6-27B-MLX-8bit ModelThe Qwen3.6-27B-MLX-8bit model is a cutting-edge language understanding solution that delivers exceptional performance for a wide range of natural language tasks. With its 27B parameters and optimized 8-bit quantization, it strikes a perfect balance between accuracy and memory footprint.

📤 Release Hash: 85ae97875a3a64029bcaad8c099e88cd • 📅 Date: 2026-07-18VerifyProcessor: 4.0 GHz+ boost clock recommended for CPU inference RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference The Kimi-K2.6-NVFP4 Model: A Breakthrough in Enterprise Language Understanding and GenerationThe Kimi-K2.6-NVFP4 model represents a significant advancement in language understanding and generation for enterprise applications, leveraging a trillion-parameter architecture combined with advanced quantization to deliver

📎 HASH: b75fd246b2702367dcf3a2905aa97465 | Updated: 2026-07-15VerifyCPU: multi-threading optimized for fast prompt processing RAM: required: 16 GB absolute minimum for small models Disk Space: at least 100 GB for multiple local LLM variants GPU: high memory bandwidth GPU for next-gen local AI pipeline Trellis.2-4B Model OverviewThe TRELLIS.2-4B model represents a significant advancement in open-source language models, delivering state-of-the-art performance while maintaining a manageable parameter count of 2.4 billion. Built on a transformer-based architecture with enhanced attention mechanisms, it achieves superior comprehension of both textual

🔧 Digest: ea98d1a3ab4d254a66d997574053a7d2 • 🕒 Updated: 2026-07-13VerifyCPU: multi-threading optimized for fast prompt processing RAM: 64 GB to avoid OOM crashes on large contexts Storage:100 GB free space for HuggingFace cache folder GPU: high memory bandwidth GPU for next-gen local AI pipeline Tailored Performance for Diverse ApplicationsThe Qwen3.6-35B-A3B-MLX-8bit model boasts exceptional performance, making it an ideal choice for various applications. Its ability to deliver high accuracy on a wide range of NLP tasks, coupled with its compact footprint and optimized architecture, sets it apart

📤 Release Hash: e9000a7a166f916c931e03bdb49b44df • 📅 Date: 2026-07-12VerifyProcessor: 6-core 3.5 GHz minimum required RAM: 48 GB needed to prevent memory swapping to disk Storage:100 GB free space for HuggingFace cache folder GPU: modern architecture (Ada Lovelace / Ampere minimum) Unveiling the Rio-3.0-Open-Mini: A Revolution in Edge DeploymentThe Rio-3.0-Open-Mini model is a game-changer in edge deployment, offering a compact yet powerful architecture that redefines performance on resource-constrained devices. By striking the perfect balance between parameter count and inference speed, it delivers state-of-the-art results that

💾 File hash: 34533e19af196d57f56a72a8a4dfbead (Update date: 2026-07-16)VerifyProcessor: high single-core performance needed for token latency RAM: 32 GB or higher for smooth 32k context lengths Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip Advancements in Large Language CapabilitiesThe **Qwen3.6-35B-A3B-NVFP4** model represents a significant breakthrough in large language capabilities, seamlessly integrating 35B parameters with the innovative A3B architecture. Built on the cutting-edge NVFP4 precision format, it achieves unprecedented inference efficiency while maintaining high fidelity in generated

🧩 Hash sum → 3d5265108f5b4b35747293bfb23a484e — Update date: 2026-07-12VerifyCPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space: free: 80 GB on system drive for scratch space Graphics: 12 GB VRAM minimum required for basic quantization A Revolutionary Language Model for Multilingual Understanding and EfficiencyGemma-4-26B-A4B-it-QAT-MLX-4bit is a cutting-edge large language model built on the Gemma architecture, boasting an impressive 26 billion parameters. This model's design principles, rooted in A4B, enable it to strike a balance

The fastest method for installing this model locally is by using Docker. Refer to the action plan below to initialize the model. The client handles the setup, pulling gigabytes of data automatically. During setup, the script automatically determines and applies the best settings. 🔍 Hash-sum: d50497978c65aa7ccf289b44a4e20c2f | 🕓 Last update: 2026-07-12VerifyProcessor: 4.0 GHz+ boost clock recommended for CPU inference RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: RTX 3060 or RX 6600

The most efficient approach for a local installation is leveraging Docker containers. Refer to the action plan below to initialize the model. The installer automatically pulls the model (could be multiple GBs). The deployment tool scans your environment and chooses the ideal parameters. 📤 Release Hash: a135261694ddea0a76255f866bbd37dc • 📅 Date: 2026-07-13VerifyCPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: CUDA Compute Capability 8.0+ required for