How to fine-tune OpenAI’s large language models (LLMs)

[kapwilson]

I am looking for the more detailed resources comparing RAG to fine-tuning methods in AI models to processing text data with LLM in laymen notes. I have found one resource but looking for the more  detailed view https://www.softwebsolutions.com/resources/fine-tune-llm.html

[Kaniz_Fatma · Accepted solution]

Hi @kapwilson, It seems you’re encountering an issue with using archive files in your Spark application submitted as a Jar task.

Archive Files in Spark Applications: When submitting Spark applications, you can include additional files (such as Python dependencies) using the --files or --archives options. However, the --archives option is more suitable for your use case because it allows you to distribute a Python virtual environment (with all its dependencies) as an archive file.

1. Using --archives Option: To use archive files in your Spark application, follow these steps:

   • Create the Archive File: First, create a Python virtual environment with the required libraries. Then, package this environment into a tarball (.tar.gz) file. For example, let’s assume you’ve created an environment named myenv and packaged it into myenv.tar.gz.

   • Upload to DBFS: Upload the myenv.tar.gz file to Databricks DBFS (Databricks File System). You can do this using the Databricks UI or the Databricks CLI.

   • Submit Your Spark Application: When submitting your Spark application, use the --archives option to specify the path to your archive file. For example:

     spark-submit --archives dbfs:/path/to/myenv.tar.gz#myenv my_app.py
     

     Here, myenv is the name of the archive (used as a prefix), and my_app.py is your Spark application.

   • Access the Archive in Your Code: Inside your Spark application code, you can access the contents of the archive using the relative path. For example:

     from pyspark.sql import SparkSession
     
     spark = SparkSession.builder.appName("MyApp").getOrCreate()
     
     # Add the archive to the Spark context
     spark.sparkContext.addArchive("dbfs:/path/to/myenv.tar.gz")
     
     # Now you can use the Python interpreter from the archive
     python_path = "./myenv/bin/python"
     # ... rest of your code ...
     

2. Directory Structure: The relative path you use in your code (./myenv/bin/python) should match the directory structure inside the archive. Make sure that the Python interpreter (python) is located at ./myenv/bin/python within the archive.

3. Driver and Executor Nodes: You mentioned that the archive file is present in different directories on the driver and executor nodes. This is expected behaviour. The archive is distributed to all nodes, and each node extracts it to a local directory. You don’t need to worry about the specific paths on each node; just use the relative path as shown above.

4. Version Compatibility: Ensure that the Python version in your virtual environment matches the Python version used by Spark. If there’s a mismatch, you might encounter issues.

5. Additional Considerations:

   • If you have multiple Python files, consider packaging them into a .zip or .egg file and using the --py-files option instead.
   • Check the Spark documentation for any updates related to archive files and their usage¹.

I hope this helps! Let me know if you have any further questions or need additional assistance. 😊