Principle:Cohere ai Cohere python Finetuning Configuration
| Field | Value |
|---|---|
| Type | Principle |
| Source | Cohere Python SDK |
| Domain | Fine-tuning Model Training Configuration |
| Last Updated | 2026-02-15 |
| Implemented By | Implementation:Cohere_ai_Cohere_python_FinetunedModel_Settings |
Overview
A configuration pattern for specifying fine-tuning parameters including base model selection, dataset binding, and training hyperparameters.
Description
Finetuning Configuration defines the complete specification for a fine-tuning job: which base model to customize (via BaseType like "BASE_TYPE_CHAT"), which dataset to train on, and optional hyperparameters (epochs, learning rate, batch size, LoRA rank/alpha/target modules, early stopping). The configuration is encapsulated in a FinetunedModel object with nested Settings and Hyperparameters objects.
Usage
Create a FinetunedModel with Settings (base model + dataset) and optional Hyperparameters before submitting to the fine-tuning API. LoRA parameters control the parameter-efficient fine-tuning approach.
Theoretical Basis
LoRA (Low-Rank Adaptation) fine-tuning adds small trainable rank decomposition matrices to frozen pretrained weights, dramatically reducing the number of trainable parameters. The rank parameter controls the expressiveness-efficiency tradeoff. Alpha scales the LoRA updates. Target modules select which layers receive LoRA adapters.