Empowers small language models with a deterministic tool-calling framework for advanced math and safe code execution.
Sponsored
ReasonForge provides a powerful solution for enhancing the capabilities of small language models (8B–32B parameters) by delegating complex tasks to specialized, deterministic tools. Instead of relying on the LLM for direct computation, it integrates a verified SymPy backend for symbolic mathematics and a sandboxed Python environment for code execution, significantly improving accuracy and reducing latency. This two-phase pipeline allows the LLM to focus on reasoning about task delegation and result presentation, while the robust backend handles the heavy lifting, making small LLMs competitive in math and coding challenges.
Key Features
01Deterministic math computation via SymPy backend
02Sandboxed Python code execution and analysis
03Two-phase LLM response pipeline for compute and presentation
04Significant accuracy and latency improvements for small LLMs
05Compatibility with Ollama and OpenAI-compatible API backends
060 GitHub stars
Use Cases
01Enhancing mathematical problem-solving capabilities of small LLMs
02Benchmarking and evaluating LLM performance on math and coding datasets
03Enabling safe and accurate code generation and execution for LLM applications
