qwenGPU：把 4 通道 SIMT GPU 编译进 Qwen3.5-2B 权重

Parameter-sharing loops let a 9B model punch at 27B weight class, which cuts inference cost for teams running complex agent pipelines on limited hardware. Developers building coding or office-automation agents can download the weights from Hugging Face and benchmark them against Qwen3.5-27B directly. The approach also signals a broader shift in Chinese labs toward compute-depth efficiency over raw parameter scaling.

A sub-1B model reaching near-top OmniDocBench scores means document parsing and invoice extraction can now run on modest hardware rather than large multimodal models. Teams building document pipelines can deploy it locally, pull weights from Hugging Face or GitHub, and use the JSON and Markdown outputs directly in automation workflows. Against rivals like PaddleOCR's larger document models, it trades scale for efficiency while matching them on benchmarks.

Encoding a real instruction set inside model weights points to a future where LLMs handle deterministic computation natively instead of routing to a code interpreter, trading speed and memory for reliability. Developers working on agents or tool-free reasoning can try the open-source weights on a single 24GB GPU, though the multi-token-per-instruction design makes it impractical for latency-sensitive use today.