Vision-Language OCR + RAG on AMD ROCm
Two GPU-accelerated mini-challenges built on the mandated ROCm PyTorch base, each packaged as a self-contained Docker image with model weights baked in — no network access needed at evaluation time. Mini-Challenge 2 is an OCR service for license plates and road signs. A resident Qwen3-VL-8B daemon loads once at container start and serves thin-client exec calls over a Unix socket, so each image transcribes in under a second — well inside the 30-second budget. The prompt encodes the challenge transcription rules: jurisdiction banners and slogans omitted on US plates, Chinese province prefixes preserved, multi-line sign text in reading order, advisory plaques returning digits only. It scored 10/10 on the official samples and 60/60 on a locally generated degraded set (noise, blur, rotation, low light). Mini-Challenge 3 is a RAG system over a mixed-filetype corpus. An index pass parses PDF, DOCX, XLSX, CSV, logs, source code, and transcribes images via the same resident VLM — with per-file error handling so unreadable, encrypted, or unknown files never abort the walk. Retrieval combines BM25 with identifier-expansion for multi-hop questions, withdrawn documents are excluded from context, and unanswerable questions return a clean refusal (empty answer, empty citations). Retrieval-Augmented Generation over a mixed-filetype corpus, packaged on the
mandated ROCm PyTorch base. A resident **Qwen3-VL-8B-Instruct** daemon loads once
at container start and holds the corpus index in memory;
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