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Model Benchmark

The Benchmark section of the Modal Gallery enables users to thoroughly evaluate and compare AI model performance across a wide range of tasks, datasets, and metrics. This powerful feature allows users to identify the best-performing models for their specific needs by analyzing performance indicators and visualizing comparisons between models.

Key Features:​

  • Comprehensive Filtering:

    • The Benchmark section allows users to apply multiple filters for a granular evaluation:
      • Tasks: Select tasks such as text generation, question-answering, or specific use cases like TruthfulQA generation.
      • Collections: Filter models by collections from various AI providers such as Azure OpenAI, Meta, Microsoft, Cohere, Mistral, or Databricks.
      • Datasets: Evaluate model performance based on different datasets like TruthfulQA, mmlu_humanities, hellaswag, or mmlu_stem, which measure various language generation benchmarks.
      • Metrics: Focus on specific performance metrics such as:
        • Coherence: Measures how well the model produces logically connected and natural text.
        • Accuracy: Assesses the correctness of the model's output in specific tasks.
        • Fluency: Evaluates the smoothness and readability of the generated text.
        • Groundedness: Measures how much the model's output stays connected to real-world information.
        • Relevance: Ensures that the generated content remains pertinent to the input prompt or question.
        • GPT Similarity: Compares the model's performance with baseline GPT models.

  • Performance Graphs:

    • The benchmark page provides visual representations of the model’s performance metrics. Users can see how a model's output compares across various datasets and test cases.
    • Graphs display the performance of multiple models side-by-side, highlighting their strengths and weaknesses based on the chosen filters.

  • Model Comparison:

    • Users can compare models in-depth by selecting different combinations of tasks, collections, datasets, and metrics. This feature provides a comprehensive view of each model’s relative performance, helping users determine the best model for their specific project requirements.
    • For example, a user can compare the performance of Gemma 3 with other models like Llama 3.1 or Phi-3.5-mini based on fluency in a particular dataset.

How to Use:​

  1. Select Filters:

    • Use the side panel to select the desired filters such as task type (e.g., text generation), collections (e.g., Meta, Azure OpenAI), and metrics (e.g., coherence or fluency). You can also choose specific datasets to see how well models perform on various types of data.

  2. Examine Metrics:

    • Once the filters are applied, the performance graphs will update to reflect the selected models' results. Key metrics such as coherence, accuracy, and fluency are displayed visually, making it easy to compare how different models handle the same tasks or datasets.

  3. Model Comparison:

    • To compare models, apply multiple filters for tasks, models, and datasets. The performance graphs will help visualize the strengths and weaknesses of each model, providing a side-by-side analysis to facilitate decision-making.
    • For example, you can compare Llama 3.1 and Gemma 3 to evaluate which model performs better in terms of coherence and groundedness when answering specific types of questions.

Key Benefits:​

  • Granular Analysis: The filtering options allow for in-depth analysis of model performance across a variety of metrics, making it easier to find the perfect model for your specific use case.
  • Visual Representation: Performance graphs provide an intuitive way to compare models, making it simpler to assess differences and make informed decisions based on data.
  • Informed Decision-Making: By comparing models across different benchmarks, users can make confident decisions on which model best fits their project’s needs, ensuring that their chosen AI model performs optimally for the specific task at hand.

Screenshot Example:​

This screenshot shows the benchmark comparison for the Gemma 3 model. The filters, such as Tasks, Models, Datasets, and Metrics, allow users to explore and assess the model’s performance across various benchmarks. The performance graph illustrates how Gemma 3 compares to other models, helping users choose the most appropriate model for their task.

Conclusion:​

The Benchmark section is a powerful tool in the Modal Gallery, providing users with in-depth performance insights into each AI model. By allowing comparisons across different tasks, datasets, and metrics, users can confidently select the best AI models for their projects, ensuring that their use cases are met with optimal performance and accuracy.