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Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt

Use this file to discover all available pages before exploring further.

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

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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.
    Cohere fine-tuning dashboard

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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.

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