Patrick Hill PRO

PBH Applied Systems publishes evaluated open-weight GGUF models for practical AI deployment, with an emphasis on quantized inference, agentic workflows, structured outputs, tool use, and production reliability. Every model published under this organization is converted, evaluated, and documented by PBH Applied Systems using its proprietary `quant_eval` framework. The evaluation process compares full-precision and quantized variants across agent-adjacent task families including structured JSON output, tool dispatch, multi-turn state retention, mixed natural language plus JSON responses, multiple-choice extraction, fuzz-style constraint adherence, and multi-step planning. These model cards are designed to support deployment decisions, not just model discovery. Each card documents practical behavior, quantization trade-offs, failure modes, recommended use cases, hardware requirements, and guardrails for production use. Try the live PBH Applied Systems AI Agent Demo: https://pbhappliedsystems.com/assistant.html The demo lets visitors interact with evaluated quantized open-weight models across reasoning, document intelligence, and code automation workflows running on private GPU infrastructure.