Cohere fine-tuning

How to Fine-Tune Cohere models using W&B.

With W&B you can log your Cohere model’s fine-tuning metrics and configuration to analyze and understand the performance of your models and share the results with your colleagues.

This guide from Cohere has a full example of how to kick off a fine-tuning run and you can find the Cohere API docs here

Log your Cohere fine-tuning results

To add Cohere fine-tuning logging to your W&B workspace:

1. Create a WandbConfig with your W&B API key, W&B entity and project name. Create an API key at https://wandb.ai/settings

2. Pass this config to the FinetunedModel object along with your model name, dataset and hyperparameters to kick off your fine-tuning run.

   from cohere.finetuning import WandbConfig, FinetunedModel
   
   # create a config with your W&B details
   wandb_ft_config = WandbConfig(
       api_key="<wandb_api_key>",
       entity="my-entity", # must be a valid enitity associated with the provided API key
       project="cohere-ft",
   )
   
   ...  # set up your datasets and hyperparameters
   
   # start a fine-tuning run on cohere
   cmd_r_finetune = co.finetuning.create_finetuned_model(
     request=FinetunedModel(
       name="command-r-ft",
       settings=Settings(
         base_model=...
         dataset_id=...
         hyperparameters=...
         wandb=wandb_ft_config  # pass your W&B config here
       ),
     ),
   )
   

3. View your model’s fine-tuning training and validation metrics and hyperparameters in the W&B project that you created.

   

Organize runs

Your W&B runs are automatically organized and can be filtered/sorted based on any configuration parameter such as job type, base model, learning rate and any other hyper-parameter.

In addition, you can rename your runs, add notes or create tags to group them.

Resources

• Cohere Fine-tuning Example