
Worked on the sophgo/LLM-TPU repository to deliver two core features focused on multi-modal AI deployment and inference. Developed support for multi-image dialogues in MiniCPM-V, updating model configurations, export scripts, and Python demos to process and integrate information from several images within a single conversational turn. Enabled deployment of the Megrez-3B-Instruct large language model on BM1684X hardware, providing setup instructions, model compilation scripts, and inference demos. Leveraged Python, C++, and ONNX to align configuration and tooling for scalable, hardware-accelerated inference workflows. The work emphasized production readiness and business value by reinforcing deployment capabilities for advanced multi-modal models.
December 2024 monthly summary for sophgo/LLM-TPU focused on delivering core two features and reinforcing deployment readiness for multi-modal models. The work emphasizes business value by enabling multi-image context processing and hardware-accelerated inference, with production-ready tooling and demos.
December 2024 monthly summary for sophgo/LLM-TPU focused on delivering core two features and reinforcing deployment readiness for multi-modal models. The work emphasizes business value by enabling multi-image context processing and hardware-accelerated inference, with production-ready tooling and demos.

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