Ensure all necessary AI model parameters are properly configured and passed through the system. AI models require comprehensive configuration including hardware settings (CPU/GPU allocation, thread counts), model-specific parameters (context length, n_gpu_layers), and proper path resolution. Missing configuration can lead to runtime failures, suboptimal performance, or initialization errors. Key requirements: - Include hardware settings: CPU threads, GPU layers (ngl), memory allocation - Specify model parameters: context length (ctx_len), prompt templates, embedding settings - Use proper path resolution instead of hardcoded paths - Validate configuration completeness before model initialization Example of complete model configuration: ```typescript const modelSettings = { llama_model_path: await joinPath([modelsDir, model.id]), ctx_len: model.settings.ctx_len || 2048, ngl: model.settings.ngl || 100, cpu_threads: nitroResourceProbe.numCpuPhysicalCore, prompt_template: model.settings.prompt_template, embedding: true }; // Validate before initialization if (!modelSettings.llama_model_path || !modelSettings.cpu_threads) { throw new Error('Incomplete model configuration'); } ``` This prevents critical issues like models failing to load due to missing parameters or processes spawning before proper configuration is established.