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Replace dual 8B with single 30B-A3B FP8 vision model
Simplify pipeline architecture: - Vision: Qwen3-VL-30B-A3B-Thinking-FP8 (~30-35GB) replaces dual 8B - Embedding: 2B model (2048-dim) replaces 8B - Reranker: 2B model replaces 8B - Total VRAM: ~38-43GB (was ~68GB), 45GB+ headroom on 4xL4 Key changes: - vLLM with FP8 quantization (built-in, no autoawq needed) - Proper Qwen3-VL chat template formatting via processor - Removed dual-model Thinking→Instruct pipeline - Single model handles analysis + structured JSON output Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>706520f -
Replace 30B MoE with dual 8B models (Thinking + Instruct)
Architecture change: - Vision: Qwen3-VL-30B-A3B-Instruct → dual Qwen3-VL-8B-Thinking + 8B-Instruct - Two-stage pipeline: Thinking (deep analysis) → Instruct (JSON formatting) - VRAM: 90GB → 68GB (~22GB savings, 20GB headroom on 4xL4) Key changes: - models/real.py: New DualVisionModel with token-based </think> parsing - config/settings.py: Dual model paths (vision_model_thinking, vision_model_instruct) - config/inference.py: ThinkingInferenceConfig (temp=0.6, max_tokens=32768) - Removed all lazy loading code (load_vision/unload_vision/load_rag) - All 4 models now load simultaneously at startup Per Qwen3-VL GitHub recommended hyperparameters for thinking models. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>333c083
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Initial commit: FDAM AI Pipeline v4.0.1
- Gradio-based fire damage assessment application - Qwen3-VL vision model integration (mock + real) - RAG-based knowledge retrieval with ChromaDB - FDAM-compliant calculations (ACH, sample density) - PDF generation with WeasyPrint - Session persistence via localStorage - 151 passing tests Ready for HuggingFace Spaces deployment on 4xL4 (96GB VRAM) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>88bdcff