Retrievers

🔗 SHA sum: bea310ae011930c1db5b016e09e828a2 | Updated: 2026-07-18 Verify CPU: multi-threading optimized for fast prompt processing RAM: enough space for background apps and OS overhead Disk: high-speed SSD 120 GB to cache model layers GPU: high memory bandwidth GPU for next-gen…

🖹 HASH-SUM: 61b8fc122dc3155bf47316901ac11dc6 | 📅 Updated on: 2026-07-22 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: high-speed DDR5 memory preferred for CPU offloading Disk Space:70 GB free space for full FP16 weights storage Graphic Processor: hardware…

📄 Hash Value: 1eece162b69513c3cfe601f8c3802a54 | 📆 Update: 2026-07-15 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: fast 5600MHz+ required to avoid memory bottlenecks Disk: 150+ GB for high-context vector database storage Graphics: 12 GB VRAM minimum required…

📎 HASH: b851fde163ddb958e98f184bc812c9ee | Updated: 2026-07-14 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 GPU: modern architecture (Ada Lovelace / Ampere minimum) Unlocking…

Deploying this model locally is quickest when done via a simple curl command. Refer to the instructions below to proceed. 1-click setup: the app automatically fetches the large weight files. The deployment tool scans your environment and chooses the ideal…

For an instant local deployment, running a pre-configured shell script is ideal. Follow the sequence of steps detailed below. The client handles the setup, pulling gigabytes of data automatically. The configuration wizard runs silently to set up the model for…

The fastest way to get this model running locally is via Optional Features. Follow the guidelines below to continue. All large files and heavy weights are downloaded automatically by the script. The installer will automatically analyze your hardware and select…