Forge Framework Boosts 8B Model Performance from 53% to 99%
Original: Show HN: Forge – Guardrails take an 8B model from 53% to 99% on agentic tasks
Why This Matters
Demonstrates significant performance improvements for local AI models without requiring cloud services
Open-source Python framework Forge improves self-hosted LLM tool-calling performance through guardrails and context management. The reliability layer takes local 8B models from 53% to 99% accuracy on agentic tasks using rescue parsing, retry mechanisms, and VRAM-aware budgets.
Antoine Zambelli released Forge, a Python framework that enhances self-hosted LLM performance on multi-step agentic workflows. The system uses guardrails including rescue parsing, retry nudges, and step enforcement, combined with VRAM-aware context management and tiered compaction. Testing shows Ministral-3 8B Instruct Q8 on llama-server achieves 86.5% accuracy across Forge's 26-scenario evaluation suite, with 76% on the hardest tier. The framework offers three usage modes: WorkflowRunner for defining tools and workflows, Agent for autonomous task execution, and direct tool integration. Forge addresses common issues in self-hosted LLM deployments like inconsistent output formatting and context overflow.