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✅ Selection Criteria to Choose Pre-Trained Models

When selecting a pre-trained foundation model for your generative AI use case, it’s important to evaluate various factors such as performance, cost, and capabilities. Below are the key criteria to consider:

💸 Cost​

  • Why it matters: Pre-trained models can be expensive to train and operate, especially large foundation models.
  • Key Consideration: Balance model accuracy vs. cost.
    • Example: Choose between a model with 98% accuracy that costs $100,000 to train vs. one with 97% accuracy at $5,000.
  • Includes:
    • Training cost
    • Inference cost
    • Compute/storage requirements

🧠 Model Size & Complexity​

  • Impacts:
    • Compute and memory requirements
    • Feasibility for edge deployment
  • Trade-off: Larger models offer better accuracy but demand more resources.

🧠 Model Complexity & Architecture​

  • Architecture depends on task:
    • CNN → Image tasks
    • RNN / Transformers → Sequential or NLP tasks
  • Complexity indicators:
    • Number of parameters
    • Number of layers
    • Computational load

⚡ Latency​

  • Why it matters: Some applications require real-time results.
  • Key Consideration: Inference time must match the application's responsiveness needs.
    • Example: A self-driving car cannot use a slow model, even if accurate.
  • Trade-off: High accuracy often means slower performance due to model complexity.

🧩 Modality​

  • Definition: Type of input/output supported by the model:
    • Text
    • Image
    • Audio
    • Video
    • Multimodal (e.g., image + text)
  • Note: Ensemble models combine modalities to enhance performance.

🌍 Multi-Lingual Capabilities​

  • Why it matters: Global applications require support for multiple languages.
  • Check: Was the model trained on the languages relevant to your users?

🎛️ Customization Ability​

  • Can the model be:
    • Prompt-engineered?
    • Fine-tuned?
    • Used with RAG (Retrieval-Augmented Generation)?
  • Importance: Critical for domain-specific use cases.

🔁 Input/Output Length Limits​

  • Each model has limits on token length.
  • Important for:
    • Document summarization
    • Long conversations
  • Action: Always check token limits in the model’s specs.

🧩 Summary: How to Choose?​

Ask these questions:

  • What modality do I need (text, image, etc.)?
  • Do I need multilingual capabilities?
  • What are my budget and latency constraints?
  • What compute resources are available?
  • Do I need to customize or fine-tune the model?
  • What metrics define success in my use case?
tip

Choosing the right pre-trained model involves balancing cost, performance, modality, and scalability to match your application goals.