🧮 Hash-code: f830f26e1de9029afbf4be2f7994cca7 • 📆 2026-07-22 Verify Processor: next-gen chip for heavy context processing RAM: at least 32 GB in dual-channel mode for bandwidth Storage: extra room for future model updates and datasets GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference The gemma-4-E4B-it-MLX-4bit model: A breakthrough in open-source language models The gemma-4-E4B-it-MLX-4bit […]
Category Archives: Checkpoints
Checkpoints
🛠 Hash code: ac555119379381018a7178b355a19c29 — Last modification: 2026-07-18 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: enough space for background apps and OS overhead Disk Space: free: 80 GB on system drive for scratch space GPU: high memory bandwidth GPU for next-gen local AI pipeline The medgemma-27b-it model: A medical language […]
🗂 Hash: c1d2ee41c9a14dbc72c4cd63c7bad0f1 • Last Updated: 2026-07-21 Verify Processor: next-gen chip for heavy context processing RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space: at least 100 GB for multiple local LLM variants GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Unlocking Real-Time Multimodal Understanding with MiniCPM-V-4.6 The MiniCPM-V-4.6 vision-language model […]
🔐 Hash sum: 4de66717b9a1cb2dd3cf785d82a5d595 | 📅 Last update: 2026-07-16 Verify CPU: multi-threading optimized for fast prompt processing RAM: 48 GB needed to prevent memory swapping to disk Storage:100 GB free space for HuggingFace cache folder GPU: high memory bandwidth GPU for next-gen local AI pipeline Chandra OCR-2: Revolutionizing Document Recognition The Chandra OCR-2 model is […]
📦 Hash-sum → 564ff9720fa5b9c0b51d6c074c832580 | 📌 Updated on 2026-07-20 Verify Processor: next-gen chip for heavy context processing RAM: 64 GB to avoid OOM crashes on large contexts Disk Space:70 GB free space for full FP16 weights storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip Qwen3.5-0.8B is an ultra-compact, state-of-the-art multimodal foundation model engineered for […]
📎 HASH: 7af1590c1994169c020a3c731da12666 | Updated: 2026-07-15 Verify CPU: modern architecture (Zen 3 / Alder Lake minimum) RAM: enough space for background apps and OS overhead Disk Space: at least 100 GB for multiple local LLM variants Graphics: CUDA Compute Capability 8.0+ required for flash-attention Advancements in Large Language Capabilities The **Qwen3.6-35B-A3B-NVFP4** model represents a significant […]
🔍 Hash-sum: 93070f0590bd304a79524cbf4f84189e | 🕓 Last update: 2026-07-15 Verify Processor: 6-core 3.5 GHz minimum required RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk Space:70 GB free space for full FP16 weights storage Graphics: stable 30+ tk/s at 4-bit quantization on medium setup Unlocking High-Accuracy Transcription with Parakeet-TDT-0.6B-V3 The Parakeet-TDT-0.6B-V3 model is designed to tackle […]
📤 Release Hash: 6f9808d004352614c858065a8903cd58 • 📅 Date: 2026-07-12 Verify CPU: multi-threading optimized for fast prompt processing RAM: 32 GB highly recommended for 26B+ GGUF models Disk: high-speed SSD 120 GB to cache model layers Graphics: TensorRT-LLM / vLLM inference engine compatible chip Unlocking the Power of Open-Source Language Models The Gemma-4-31B-it model represents a significant […]