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Instruction Following

Eval Recipe for model migration

This Eval Recipe demonstrates how to compare performance of an Instruction Following prompt with Gemini 1.5 Flash and Gemini 2.0 Flash using Vertex AI Evaluation Service.

Instruction-Following Eval (IFEval) is a straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set of "verifiable instructions" such as "write in more than 400 words", "write in bullet points", etc.

  • Use case: Instruction Following

  • Evaluation Dataset is based on Instruction Following Evaluation Dataset. It includes 10 randomly sampled prompts in a JSONL file dataset.jsonl with the follwing structure:

    • prompt: The task with specific instructions provided

  • Prompt Template is a zero-shot prompt located in prompt_template.txt with one prompt variable (prompt) that is automatically populated from our dataset.

  • Python script eval.py configures the evaluation:

    • run_eval: configures the evaluation task, runs it on the 2 models and prints the results.
    • load_dataset: loads the dataset including the contents of all documents.

  • Shell script run.sh installs the required Python libraries and runs eval.py

  • Google Cloud Shell is the easiest option as it automatically clones our Github repo:

    Open in Cloud Shell

  • Alternatively, you can use the following command to clone this repo to any Linux environment with configured Google Cloud Environment:

    git clone --filter=blob:none --sparse https://github.com/GoogleCloudPlatform/applied-ai-engineering-samples.git && \
cd applied-ai-engineering-samples && \
git sparse-checkout init && \
git sparse-checkout set genai-on-vertex-ai/gemini/model_upgrades && \
git pull origin main
cd genai-on-vertex-ai/gemini/model_upgrades

  • Navigate to the Eval Recipe directory in terminal, set your Google Cloud Project ID and run the shell script run.sh.

    cd instruction_following/vertex_script
export PROJECT_ID="[your-project-id]"
./run.sh

  • The resulting metrics will be displayed in the script output.

  • You can use Vertex AI Experiments to view the history of evaluations for each experiment, including the final metrics scores.

How to customize this Eval Recipe:

  1. Edit the Python script eval.py:
    • set the project parameter of vertexai.init to your Google Cloud Project ID.
    • set the parameter baseline_model to the model that is currently used by your application
    • set the parameter candidate_model to the model that you want to compare with your current model
    • configure a unique experiment_name for tracking purposes
  2. Replace the contents of dataset.jsonl with your custom data in the same format.
  3. Replace the contents of prompt_template.txt with your custom prompt template. Make sure that prompt template variables map to the dataset attributes.
  4. Please refer to our documentation if you want to further customize your evaluation. Vertex AI Evaluation Service has a lot of features that are not included in this recipe.
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