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Academy/Action Pack
🎯 Action PackintermediateFree

Anyscale Fine-tuning

Fine-tune open-source models on Anyscale's managed infrastructure using Ray for distributed training. Leverage DeepSpeed and FSDP for efficient, scalable fine-tuning. This action pack guides you through the process.

fine-tuningraydistributedenterpriseanyscale

5 Steps

  1. 1

    Set up Anyscale CLI: Install and configure the Anyscale CLI to interact with the Anyscale platform. Ensure you have an Anyscale account and API key.

  2. 2

    Create a Fine-tuning Job Specification: Define a YAML file specifying the model, dataset, and hyperparameter configurations for your fine-tuning job. Refer to Anyscale documentation for the schema.

  3. 3

    Submit the Fine-tuning Job: Use the Anyscale CLI to submit the fine-tuning job, referencing the YAML specification file.

  4. 4

    Monitor the Job: Track the progress of your fine-tuning job using the Anyscale dashboard or CLI. Monitor metrics such as loss and training time.

  5. 5

    Deploy the Fine-tuned Model: Once the job completes successfully, deploy the fine-tuned model for inference. Follow Anyscale's deployment guidelines for optimal performance.

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