FORGE BOOSTS 8B MODEL PERFORMANCE TO 99% ON AGENT TASKS

Forge adds guardrails to self-hosted 8B models, lifting performance from 53% to 99% on multi-step agentic tasks. The tool runs on consumer hardware and works independently of the underlying model through system-level improvements. Key features include retry nudges that encourage models to self-correct, step enforcement for workflow adherence, error recovery mechanisms, and VRAM-aware context management for resource-constrained environments. The open-source project ships with an evaluation harness for testing and an interactive dashboard for monitoring. By implementing guardrails around the model rather than modifying the model itself, Forge enables reliable local inference without expensive retraining or larger models. The approach addresses a core challenge for edge AI: achieving production-grade reliability with smaller models suitable for on-device deployment.