Fine-tuning FAQs
Q: What is fine-tuning in LLMOps?
A: Fine-tuning is the process of adapting a pre-trained language model to specific tasks or domains using additional training data, improving its performance for targeted applications.
Q: Which models can I fine-tune in LLMOps?
A: LLMOps supports fine-tuning of various open-source models, including LLaMA-2, Mistral, Phi-2, Gemma, CodeLlama, and Falcon.
Q: How do I prepare my dataset for fine-tuning?
A: Use the Data Source Manager to upload and preprocess your dataset. Ensure your data is in the correct format (e.g., question-answer pairs for chatbots, or text samples for language tasks).
Q: What fine-tuning techniques are available in LLMOps?
A: LLMOps offers various techniques including Full Fine-Tuning, LoRA (Low-Rank Adaptation), QLoRA, Prefix Tuning, and P-Tuning v2.
Q: How long does the fine-tuning process typically take?
A: The duration depends on the model size, dataset size, and chosen compute resources. It can range from a few hours to several days for large models and datasets.
Q: Can I monitor the progress of my fine-tuning job?
A: Yes, you can track the progress of your fine-tuning job in real-time through the Jobs dashboard, which shows metrics like loss and perplexity.
Q: How do I evaluate the performance of my fine-tuned model?
A: LLMOps provides evaluation metrics and allows you to test your model on a held-out validation set. You can also use the Playground feature to interactively test your model.
Q: Can I fine-tune a model for multiple tasks simultaneously?
A: Yes, LLMOps supports multi-task fine-tuning. You can prepare a dataset with multiple task types and use the appropriate fine-tuning technique.
Q: What compute resources are available for fine-tuning?
A: LLMOps offers various compute options from cloud providers like AWS, GCP, and Azure, with different GPU configurations to suit your needs.
Q: How can I optimize my fine-tuning process for better results?
A: Experiment with different hyperparameters, use techniques like learning rate scheduling, and consider data augmentation. LLMOps provides guidance and best practices for optimization.