Evaluator Fine-tuning

Build your own evaluation metric by fine-tuning alBERTa evaluator models

The AutoEval fine-tuning service enables you to develop models that represent your custom evaluation criteria. The flow is roughly as follows:

1. Start with a Dataset of your application data.
2. (Assuming you don't have labels) Specify your evaluation criteria and generate labels with LLM Judge++.
3. Use the AutoEval fine-tuning service to train a fine-tuned evaluator model using the labeled data to learn the evaluation criteria.

The fine-tuned model will then produce a numeric 0->1 probability score for every request.

Some example evaluators that AutoEval customers have trained:

• A custom response quality metric that includes succinctness, clarity, and accuracy.
• A custom correctness metric for tool use (function calling) in a multi-agent system.
• A custom brand tone metric to measure LLM response adherence to company's brand tone rubric.

Why fine-tune?

Fine-tuning your own alBERTa evaluator model is much more effective than LLM Judge style eval approaches for a number of reasons. Unlike an LLM Judge, this model can be small, fast and include human-in-the-loop refinement:

• better metric quality -- the quality of evaluations is bound by quality of the labels (which can be refined with human feedback), not bound by an LLM's ability to understand your application context.
• small & fast -- 100x faster than LLM Judge (can run inference in 10-300ms), which allows these evaluators to run online as guardrails.
• customizable -- the model is simply learning the label distribution, making it easy to teach it any kind of classification task.

You can fine-tune as many evaluators as you want – one for each evaluation criteria you need for your application.

Usage Guide

Upload Datasets

Create a Dataset containing the application trace data to use for fine-tuning.

tip

We recommend at least a few hundred examples (256 minimum). A few thousand examples is ideal.

During the fine-tuning flow you can choose which of these columns to include in the training. Make sure that the data contains at least one of the following columns:

• input
• output
• ground_truth

tip

Don't have app data handy? No problem - check out our Example Datasets to get synthetic datasets to try out the platform.

Label data

Use the LLM Judge++ flow to define your evaluation criteria and generate labels.

Training and test data split (optional)

Split the dataset into a training dataset and a test (holdout) dataset.

tip

We recommend an 80/20 or 90/10 split.

You can do this via API:

python

split_dataset

from lastmile.lib.auto_eval import AutoEval, Metric
from sklearn.model_selection import train_test_split
import pandas as pd

client = AutoEval(api_token="api_token_if_LASTMILE_API_TOKEN_not_set")
dataset_df = client.download_dataset(dataset_id=dataset_id) # Labeled dataset from previous step

# Split the data into training and test sets
train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)

train_df.to_csv('train.csv', index=False)
test_df.to_csv('test.csv', index=False)

test_dataset_id = client.upload_dataset(
  file_path="test.csv",  # Your test dataset file
  name="My Test Dataset for Fine-tuning",
  description="Test dataset for evaluating the fine-tuned model"
)

train_dataset_id = client.upload_dataset(
  file_path="train.csv",
  name="My Training Dataset for Fine-tuning",
  description="Training dataset for the fine-tuned model"
)

Fine-tune model

At this point we have labeled application data to train, and test the model. Time to fine-tune!

API

python

fine_tune

# Continuation from previous step
fine_tune_job_id = client.fine_tune_model(
  train_dataset_id=train_dataset_id,  
  test_dataset_id=test_dataset_id,
  model_name="My Custom Evaluation Metric",
  selected_columns=["input", "output", "ground_truth"], # You can decide which of these columns to include in the training data
  wait_for_completion=False # Set to true for blocking
)

print(f"Fine-tuning job initiated with ID: {fine_tune_job_id}. Waiting for completion...")
client.wait_for_fine_tune_job(fine_tune_job_id)
print(f"Fine-tuning job completed with ID: {fine_tune_job_id}")

UI

1. Navigate to Model Console and click Fine-Tune a Model.

2. Fill out the Fine-tuning form and click Submit to start the training job.

3. Track progress in the Model Console, including training metrics such as loss/accuracy.

   info

   Set up your Weights & Biases API key to have the training data logged to your own W&B account if you prefer.

   

Use fine-tuned model

Once the model is trained and deployed on the inference server, it will be listed as 🟢 Online in the dashboard.

You can try it out directly in the playground, and see its training metrics in the Fine-Tune Info tab.

API

Use the API to run evals using your new model

python

run_inference

fine_tuned_metric = Metric(name="My Custom Evaluation Metric") # Reference the fine-tuned model by name or id

# Run evals on your test/holdout dataset to see how the model is performing
test_results_df = client.evaluate_dataset(test_dataset_id, fine_tuned_metric)

# Run evals on any application data
eval_results_df = client.evaluate_data(
  # data should include the columns that the model expects
  data=pd.DataFrame({
      "input": ["What is the meaning of life?"],
      "output": ["42"],
      "ground_truth": ["Life, universe and everything"]
  }),
  metrics=[fine_tuned_metric],
)

Weights & Biases integration

AutoEval allows you to track detailed training runs in your own Weights & Biases account. To do so, navigate to the API Keys console, and save your W&B API key. Subsequent fine-tuning runs will be tracked in your account.