Hugging Face Jobs

I’m building an NLP-driven, multimodal assistant that accepts text, image, and audio inputs, but its replies still drift into hallucination. The goal is straightforward: sharpen response accuracy so the system stays firmly grounded in fact. Right now the core pipeline is a Hugging Face Transformer model wrapped in a Retrieval-Augmented Generation (RAG) layer. I need you to audit the entire flow, diagnose where and why hallucinations appear, and then apply proven mitigation techniques. That could involve prompt engineering, better retrieval logic, truth-focused data augmentation, fine-tuning, or introducing guard-rail frameworks—whatever combination delivers measurably higher factual precision. Deliverables • A revised model or inference pipeline that demonstrably impro...

deploy a dual-model pipeline on AWS. Scope of Work Dual-Model Deployment: Deploy the Pillar-0 (Atlas architecture) for multi-finding classification across Chest, Abdomen, and Brain. Integrate Sybil-1.5 for specialized future lung cancer risk prediction (1–6 year horizon). Inference Pipeline & Report Generation: deploy pipeline that takes zipped DICOM files, performs 3D volumetric reconstruction, and runs concurrent inference. script (attached). This script shows how to read the (Zip file), stack of the medical pictures (DICOMs) into a 3D block, and run both the Pillar-0 model (for findings) and Sybil-1.5 (for cancer risk) -This tells AWS which specialized tools to install to read 3D medical images AWS Cloud Architecture: Host models on AWS SageMaker (GPU instances like m...