Choosing a Fine-Tuning Method in Generative AI
OCI Generative AI fine-tunes the pretrained base models using a method that matches the base model. The following table lists the method that Generative AI uses to train each type of base model:
Pretrained Base Model | Training Method |
---|---|
cohere.command-r-16k
|
|
meta.llama-3.1-70b-instruct
|
|
cohere.command (being deprecated) |
|
cohere.command-light (being deprecated) |
|
meta.llama-3-70b-instruct (being deprecated) |
|
Note
For information about the hyperparameters used for each training method, see Hyperparameters for Fine-Tuning a Model in Generative AI.
For information about the hyperparameters used for each training method, see Hyperparameters for Fine-Tuning a Model in Generative AI.
Choosing Between T-Few
and Vanilla
For the cohere.command
and cohere.command-light
models,
OCI
Generative AI has two training methods:
T-Few
, and Vanilla
. Use the following guidelines to help
you choose the best training method for your use cases.
Feature | Options and Recommendations |
---|---|
Training methods for cohere.command and
cohere.command-light
|
|
Dataset Size |
Using small datasets for the |
Complexity |
|
Hosting |
|