Archivos de categorías: Retrievers

🔗 SHA sum: 976a5ccce38e62bf1b05ebed54ce5233 | Updated: 2026-07-23 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: 100 GB for multi-modal model vision components Graphics: CUDA Compute Capability 8.0+ required for flash-attention The Cutting Edge of Document Understanding The DeepSeek-OCR-2 model revolutionizes the field of document understanding by […]

🛠 Hash code: 8bcd18de31721146651e4af114b81407 — Last modification: 2026-07-22 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: enough space for background apps and OS overhead Disk Space: required: fast PCIe 4.0 drive for instant boots GPU: high memory bandwidth GPU for next-gen local AI pipeline Harnessing the Power of Multimodal Language Models Qwen3-VL-30B-A3B-Instruct is a […]

📎 HASH: 0566c9b07b656190685f837f342b0a14 | Updated: 2026-07-19 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: 64 GB to avoid OOM crashes on large contexts Disk Space: 100 GB for multi-modal model vision components GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking the Power of Multimodal AI with Qwen3-VL-4B-Instruct The Qwen3-VL-4B-Instruct model is […]

📤 Release Hash: 2e8f1476eb2e0bb8aaa54d37ce6b0fe7 • 📅 Date: 2026-07-18 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: minimum 16 GB for stable 8B model loading Disk Space: free: 80 GB on system drive for scratch space GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking the Power of Vision-Language Re-Ranking […]

📦 Hash-sum → 878d91dd1e1615ad1539cc9939206b0c | 📌 Updated on 2026-07-16 Verify Processor: Intel i5 or AMD Ryzen 5 for basic 7B models RAM: 32 GB highly recommended for 26B+ GGUF models Disk: 150+ GB for high-context vector database storage GPU: modern architecture (Ada Lovelace / Ampere minimum) Revolutionizing Large Language Model Efficiency The Qwen3.6-35B-A3B-NVFP4 model marks […]

🔗 SHA sum: 64c0ad3781580c41e0156eb1ab677039 | Updated: 2026-07-20 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: minimum 16 GB for stable 8B model loading Disk: high-speed SSD 120 GB to cache model layers Graphics: CUDA Compute Capability 8.0+ required for flash-attention Unveiling the Power of Moss-TTS: Revolutionizing Text-to-Speech Synthesis Moss-TTS, a cutting-edge text-to-speech […]

🔐 Hash sum: a545953c38480a2e4633bc1a0dfb311e | 📅 Last update: 2026-07-18 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB or higher for smooth 32k context lengths Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration Fusion of Cutting-Edge Technologies for Enhanced Model Performance […]

🛡️ Checksum: 312a2ecfab6fa5cc6a985136383e1be3 — ⏰ Updated on: 2026-07-18 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 48 GB needed to prevent memory swapping to disk Storage: extra room for future model updates and datasets Graphics: 12 GB VRAM minimum required for basic quantization Advancements in Large Language Capabilities The **Qwen3.6-35B-A3B-NVFP4** model […]

📦 Hash-sum → ffa8499dd867a087e5f79a1a0994a594 | 📌 Updated on 2026-07-13 Verify CPU: AVX2/AVX-512 instruction set required for llama.cpp RAM: at least 32 GB in dual-channel mode for bandwidth Disk Space: free: 80 GB on system drive for scratch space GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats The Power of Qwen3-Coder-30B-A3B-Instruct: Unlocking […]

🔧 Digest: f80bf12dc3fcb94d34a53ec3d7f05b40 • 🕒 Updated: 2026-07-16 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Storage: extra room for future model updates and datasets Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration A Revolutionary Language Model for Multilingual Understanding and Efficiency Gemma-4-26B-A4B-it-QAT-MLX-4bit […]