When documenting AI/ML model deployment and inference procedures, always test and specify compatible library versions to prevent runtime failures. Version incompatibilities in AI libraries can cause critical errors like inference failures, probability tensor issues, or model output corruption. Key practices: - Test all documented version combinations before publication - Specify exact working versions when known incompatibilities exist - Document alternative versions when primary versions fail - Include version-specific notes for critical functionality Example from model deployment documentation: ```bash # Working configuration auto_gptq==0.6.0+cu1210 # Note: >=0.7.0 causes RuntimeError with probability tensors # vLLM version notes vLLM==0.6.2 # Recommended for Qwen2.5 7B/14B/32B with 128k context vLLM==0.6.3 # May cause output issues: !!!!!!!!!!!!!!!!!!!!!!! ``` This prevents users from encountering runtime errors like "probability tensor contains either `inf`, `nan` or element < 0" or model output corruption, ensuring reliable AI model deployment and inference.