The Finetune function of the Together Python Library is used to create, manage, and monitor fine-tune jobs.
Help
See all commands with:
together finetune --help
Create
To start a new fine-tune job:
together finetune create -t <FILE-ID> -m <MODEL>
Other arguments:
--training-file,-t(file-id, required) -- Specifies a training file with the file-id of a previously uploaded file (See Files).--model,-m(model name, optional) -- Specifies the base model to fine-tune. Default:togethercomputer/RedPajama-INCITE-7B-Chat.--suffix,-s(string, optional) -- Up to 40 characters that will be added to your fine-tuned model name. It is recommended to add this to differentiate fine-tuned models. Default: None.--n-epochs,-ne(integer, optional) -- Number of epochs to fine-tune on the dataset. Default: 4, Min: 1, Max: 20--n-checkpoints,-c(integer, optional) -- The number of checkpoints to save during training. Default: 1 One checkpoint is always saved on the last epoch for the trained model. The number of checkpoints must be < the number of epochs. If a larger number is given, the number of epochs will be used for the number of checkpoints.--batch-size,-b(integer, optional) -- The batch size to use for each training iteration. The batch size is the number of training samples/examples used in a batch. See the model page for min and max batch sizes for each model.--learning-rate,-lr(float optional) -- The learning rate multiplier to use for training. Default: 0.00001, Min: 0.00000001, Max: 0.01--wandb-api-key-- Your own Weights & Biases API key. If you provide the key, you can monitor your job progress on your Weights & Biases page. If not set WANDB_API_KEY environment variable is used.
The id field in the JSON response contains the value for the fine-tune job ID (ft-id) that can be used to get the status, retrieve logs, cancel the job, and download weights.
List
To list past and running fine-tune jobs:
together finetune list
The jobs will be sorted oldest-to-newest with the newest jobs at the bottom of the list.
Retrieve
To retrieve metadata on a job:
together finetune retrieve <FT-ID>
Monitor Events
To list events of a past or running job:
together finetune list-events <FT-ID>
Cancel
To cancel a running job:
together finetune cancel <FT-ID>
Status
To get the status of a job:
together finetune status <FT-ID>
Checkpoints
To list saved-checkpoints of a job:
together finetune checkpoints <FT-ID>
Download Model and Checkpoint Weights
To download the weights of a fine-tuned model, run:
together finetune download <FT-ID>
This command will download ZSTD compressed weights of the model. To extract the weights, run tar -xf filename.
Other arguments:
--output,-o(filename, optional) -- Specify the output filename. Default:<MODEL-NAME>.tar.zst--step,-s(integer, optional) -- Download a specific checkpoint's weights. Defaults to download the latest weights. Default:-1