Model inference quality and stability, specifically 'hallucinated tool call end tokens' and potential 'parser state corruption' when running DS4 on 2-bit quantization.

This issue exposes a critical reliability concern within DS4, specifically regarding model output integrity under 2-bit quantization. 'Hallucinated tool call end tokens' directly impact the trustworthiness and usability of the inference engine, suggesting either model instability or parser vulnerabilities. For B2B applications, unpredictable output or internal state corruption is unacceptable, hindering adoption in production environments. Aggressive quantization like 2-bit is crucial for resource-constrained deployments, but not at the expense of accuracy or stability. Addressing this requires deep debugging into token generation and parser logic, ensuring that performance optimizations do not compromise fundamental model reliability. This directly impacts developer confidence and the perceived maturity of the DS4 engine.