wehub-resource-sync
42 lines
2.5 KiB
Markdown
42 lines
2.5 KiB
Markdown
# MLflow examples for LLM use cases
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This directory includes several examples for tracking, evaluating, and scoring models with LLMs.
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## Summarization
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The `summarization/summarization.py` script uses prompt engineering to build two summarization models for news articles with LangChain. It leverages the `mlflow.langchain` flavor to package and log the models to MLflow, `mlflow.evaluate()` to evaluate each model's performance on a small example dataset, and `mlflow.pyfunc.load_model()` to load and score the best packaged model on a new example article.
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To run the example as an MLflow Project, simply execute the following command from this directory:
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```
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$ cd summarization && mlflow run .
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```
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To run the example as a Python script, simply execute the following command from this directory:
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```
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$ cd summarization && python summarization.py
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```
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Note that this example requires MLflow 2.4.0 or greater to run. Additionally, you must have [LangChain](https://python.langchain.com/en/latest/index.html) and the [OpenAI Python client](https://pypi.org/project/openai/) installed in order to run the example. We also recommend installing the [Hugging Face Evaluate library](https://huggingface.co/docs/evaluate/index) to compute [ROUGE metrics](<https://en.wikipedia.org/wiki/ROUGE_(metric)>) for summary quality. Finally, you must specify a valid OpenAI API key in the `OPENAI_API_KEY` environment variable.
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## Question answering
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The `question_answering/question_answering.py` script uses prompt engineering to build two models that answer questions about MLflow.
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It leverages the `mlflow.openai` flavor to package and log the models to MLflow, `mlflow.evaluate()` to evaluate each model's performance on some example questions, and `mlflow.pyfunc.load_model()` to load and score the best packaged model on a new example question.
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To run the example as an MLflow Project, simply execute the following command from this directory:
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```
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$ cd question_answering && mlflow run .
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```
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To run the example as a Python script, simply execute the following command from this directory:
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```
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$ cd question_answering && python question_answering.py
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```
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Note that this example requires MLflow 2.4.0 or greater to run. Additionally, you must have the [OpenAI Python client](https://pypi.org/project/openai/), [tiktoken](https://pypi.org/project/tiktoken/), and [tenacity](https://pypi.org/project/tenacity/) installed in order to run the example. Finally, you must specify a valid OpenAI API key in the `OPENAI_API_KEY` environment variable.
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