Evaluate A Hugging Face Llm With Mlflow Evaluate Mlflow

This guide will show how to load a pre-trained Hugging Face pipeline, log it to MLflow, and use mlflow.evaluate() to evaluate builtin metrics as well as custom LLM-judged metrics for the model. For detailed information, please read the documentation on using MLflow evaluate. Here we are loading a text generation pipeline, but you can also use either a text summarization or question answering pipeline. We log our pipeline as an MLflow Model, which follows a standard format that lets you save a model in different "flavors" that can be understood by different downstream tools. In this case, the model is of the transformers "flavor". Load in a dataset from Hugging Face Hub to use for evaluation.

A comprehensive guide and implementation for evaluating Large Language Models (LLMs) from Hugging Face using MLflow's evaluation framework. This project demonstrates how to load, log, and systematically evaluate pre-trained language models with both built-in and custom metrics. This project showcases a complete MLOps pipeline for LLM evaluation, featuring: Model Loading: Integration with Hugging Face Transformers Model Logging: MLflow model registry and versioning Comprehensive Evaluation: Built-in metrics and custom LLM-judged metrics

MLflow is a powerful open-source platform for managing the machine learning lifecycle. While it’s traditionally used for tracking model experiments, logging parameters, and managing deployments, MLflow has recently introduced support for evaluating Large Language Models (LLMs). In this tutorial, we explore how to use MLflow to evaluate the performance of an LLM—in our case, Google’s Gemini model—on a set of fact-based prompts. We’ll generate responses to fact-based prompts using Gemini and assess their quality using a variety of metrics supported directly by MLflow. For this tutorial, we’ll be using both the OpenAI and Gemini APIs. MLflow’s built-in generative AI evaluation metrics currently rely on OpenAI models (e.g., GPT-4) to act as judges for metrics like answer similarity or faithfulness, so an OpenAI API key is required.

You can obtain: In this step, we define a small evaluation dataset containing factual prompts along with their correct ground truth answers. These prompts span topics such as science, health, web development, and programming. This structured format allows us to objectively compare the Gemini-generated responses against known correct answers using various evaluation metrics in MLflow. This code block defines a helper function gemini_completion() that sends a prompt to the Gemini 1.5 Flash model using the Google Generative AI SDK and returns the generated response as plain text. We then apply this function to each prompt in our evaluation dataset to generate the model’s predictions, storing them in a new “predictions” column.

These predictions will later be evaluated against the ground truth answers and get access to the augmented documentation experience You can evaluate AI models on the Hub in multiple ways and this page will guide you through the different options: Community leaderboards show how a model performs on a given task or domain. For example, there are leaderboards for question answering, reasoning, classification, vision, and audio. If you’re tackling a new task, you can use a leaderboard to see how a model performs on it.

Here are some examples of community leaderboards: There are many more leaderboards on the Hub. Check out all the leaderboards via this search or use this dedicated Space to find a leaderboard for your task. When you buy through links on our site, we may earn a commission at no extra cost to you. However, this does not influence our evaluations. MLFLOW is a powerful open source platform to manage the life cycle of automatic learning.

Although it is traditionally used for monitoring model experiences, parameter journalization and deployment management, MLFlow recently introduced support to assess large language models (LLMS). In this tutorial, we explore how to use MLFLOW to assess the performance of an LLM – In our case, the Gemini model of Google – on a set of prompts based on facts. We will generate responses to prompts based on facts using Gemini and will assess their quality by using a variety of measures supported directly by MLFLOW. For this tutorial, we will use the OPENAI and Gemini APIs. The assessment metrics generating the integrated AI of MLFLOW are currently based on OPENAI models (for example, GPT-4) to act as judges for metrics such as the similarity of response or loyalty, therefore a... You can get:

In this stage, we define a small set of evaluation data containing factual prompts with their correct -ground truth responses. These invites cover subjects such as science, health, web development and programming. This structured format allows us to objectively compare the responses generated by the gemini-aux known correct responses by using various evaluation measures in MLFLOW. With the emerging of ChatGPT, LLMs have shown its power of text generation in various fields, such as question answering, translating and text summarization. Evaluating LLMs' performance is slightly different from traditional ML models, as very often there is no single ground truth to compare against. MLflow provides an API mlflow.evaluate() to help evaluate your LLMs.

MLflow's LLM evaluation functionality consists of 3 main components: If you're interested in thorough use-case oriented guides that showcase the simplicity and power of MLflow's evaluate functionality for LLMs, please navigate to the notebook collection below: Below is a simple example that gives an quick overview of how MLflow LLM evaluation works. The example builds a simple question-answering model by wrapping "openai/gpt-4" with custom prompt. You can paste it to your IPython or local editor and execute it, and install missing dependencies as prompted. Running the code requires OpenAI API key, if you don't have an OpenAI key, you can set it up by following the OpenAI guide.

There are two types of LLM evaluation metrics in MLflow:

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