Weights

🖹 HASH-SUM: 3cae2f2699428f7f91c39019d14cd118 | 📅 Updated on: 2026-07-23 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: 64 GB to avoid OOM crashes on large contexts Storage: extra room for future model updates and datasets Graphics: TensorRT-LLM / vLLM inference engine compatible chip The Qwen 3.5-9B-AWQ Language Model: A Balanced Approach to … Read more

🔗 SHA sum: 61d823357cd646b46ef99540e32e2d88 | Updated: 2026-07-21 Verify Processor: 6-core 3.5 GHz minimum required RAM: enough space for background apps and OS overhead Disk: 150+ GB for high-context vector database storage Graphics: TensorRT-LLM / vLLM inference engine compatible chip Overview of Qwen3-ASR-1.7B Model The Qwen3-ASR-1.7B model is a state-of-the-art automatic speech recognition (ASR) system that … Read more

📎 HASH: 9a79d10b6e6a97d19126edcfc89904e8 | Updated: 2026-07-16 Verify CPU: 8-core / 16-thread recommended for orchestration RAM: 32 GB or higher for smooth 32k context lengths Disk Space: free: 80 GB on system drive for scratch space GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats Unveiling the Power of Qwen3.6-27B Deep within the … Read more

🗂 Hash: 1e5103a95f732c29dbc421e63fc0eab5 • Last Updated: 2026-07-19 Verify Processor: 6-core 3.5 GHz minimum required RAM: required: 16 GB absolute minimum for small models Storage: extra room for future model updates and datasets GPU: RTX 4080 / RTX 4090 recommended for 26B-A4B fast inference Motivations Behind the Qwen3-4B-Instruct-2507-FP8 Model The Qwen3-4B-Instruct-2507-FP8 model represents a compelling solution … Read more

📡 Hash Check: 8972c0a7cdbd018fc6b7bdedf69dee76 | 📅 Last Update: 2026-07-21 Verify CPU: multi-threading optimized for fast prompt processing RAM: required: 16 GB absolute minimum for small models Disk: 150+ GB for high-context vector database storage GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats The Cutting Edge of Document Understanding The DeepSeek-OCR-2 model … Read more