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Supported problem types

Overview​

H2O LLM Studio supports various problem types that allow users to fine-tune models for different tasks. The five supported problem types are explained below.

Causal language modeling​

  • Description: Causal language modeling involves predicting the next token in a sequence, based only on the preceding tokens (i.e., the left side of the sequence). It is commonly used for tasks such as text generation. It is used to fine-tune large language models.

Causal classification modeling​

  • Description: Causal classification modeling involves assigning one or more categorical target labels to an input text. It is used for fine-tuning models to perform text classification tasks.

  • Supported classification tasks: Binary, multi-class, and multi-label classification.

Causal regression modeling​

  • Description: Causal regression modeling assigns one or more continuous target labels to an input text. It is used to fine-tune models for text regression tasks.

  • Supported regression tasks: Multi-label regression.

Sequence to sequence modeling​

  • Description: A type of machine learning architecture designed to transform one sequence into another. It is commonly used for tasks like machine translation, text summarization, and speech recognition.

DPO modeling​

  • Description: The DPO modeling is used to fine-tune large language models using Direct Preference Optimization (DPO), a method that helps large, unsupervised language models better match human preferences using a simple classification approach.
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