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When specifying AI/ML library dependencies, ensure version compatibility between related packages and verify that prebuilt binaries are available for the target environment. Many AI libraries like auto_gptq, autoawq, and flash_attn are compiled against specific versions of PyTorch and CUDA, and version mismatches can force compilation from source or cause runtime failures.

Before finalizing requirements, check:

• Library compatibility matrices (e.g., auto_gptq 0.7.1 requires torch 2.2.1, not 2.3.1)
• Availability of prebuilt wheels for your CUDA version
• Whether dependencies require CUDA compilers if building from source

Consider separating incompatible libraries into different requirements files when necessary.

Example of problematic dependencies:

torch==2.3.1
auto_gptq==0.7.1  # Compiled against torch 2.2.1, will cause issues

Better approach:

# requirements-gptq.txt
torch==2.2.1
auto_gptq==0.7.1

# requirements-awq.txt  
torch==2.4.1
autoawq==0.2.6