# Source: https://docs.fireworks.ai/tools-sdks/firectl/commands/supervised-fine-tuning-job-create.md

> ## Documentation Index
> Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# firectl supervised-fine-tuning-job create

> Creates a supervised fine-tuning job.

```
firectl supervised-fine-tuning-job create [flags]
```

### Examples

```
firectl supervised-fine-tuning-job create \
	--base-model llama-v3-8b-instruct \
	--dataset sample-dataset \
	--output-model name-of-the-trained-model

# Create from source job:
firectl supervised-fine-tuning-job create \
	--source-job my-previous-job \
	--output-model new-model

```

### Flags

```
      --base-model string                   The base model for the supervised fine-tuning job. Only one of base-model or warm-start-from should be specified.
      --dataset string                      The dataset for the supervised fine-tuning job. (Required)
      --output-model string                 The output model for the supervised fine-tuning job.
      --job-id string                       The ID of the supervised fine-tuning job. If not set, it will be autogenerated.
      --warm-start-from string              The model to warm start from. If set, base-model must not be set.
      --source-job string                   The source supervised fine-tuning job to copy configuration from. If other flags are set, they will override the source job's configuration.
      --evaluation-dataset string           The evaluation dataset for the supervised fine-tuning job.
      --epochs int32                        The number of epochs for the supervised fine-tuning job.
      --learning-rate float32               The learning rate for the supervised fine-tuning job.
      --max-context-length int32            Maximum token length for sequences within each training batch. Shorter sequences are concatenated; longer sequences are truncated.
      --batch-size int32                    The batch size measured in tokens. Maximum number of tokens packed into each training batch/step. A single sequence will not be split across batches.
      --batch-size-samples int32            Number of samples per gradient update. If set to k, gradients update after every k samples. By default (0), gradients update based on batch-size (tokens).
      --gradient-accumulation-steps int32   The number of batches to accumulate gradients before updating the model parameters. The effective batch size will be batch-size multiplied by this value. (default 1)
      --learning-rate-warmup-steps int32    The number of learning rate warmup steps for the supervised fine-tuning job.
      --lora-rank int32                     The rank of the LoRA layers for the supervised fine-tuning job. Set to 0 for full parameter tuning. (default 8)
      --optimizer-weight-decay float32      Weight decay (L2 regularization) for the optimizer. Default in trainer is 0.01.
      --full-parameter-tuning               Enable full parameter fine-tuning instead of LoRA. Equivalent to --lora-rank=0. Requires bf16 precision.
                                            
      --wandb-api-key string                [WANDB_API_KEY] WandB API Key. (Required if any WandB flag is set)
      --wandb-project string                [WANDB_PROJECT] WandB Project. (Required if any WandB flag is set)
      --wandb-entity string                 [WANDB_ENTITY] WandB Entity. (Required if any WandB flag is set)
      --wandb                               Enable WandB
                                            
                                            
      --aws-credentials-secret string       [AWS_CREDENTIALS_SECRET] AWS credentials secret (mutually exclusive with --aws-iam-role)
      --aws-iam-role string                 [AWS_IAM_ROLE_ARN] AWS IAM role ARN (mutually exclusive with --aws-credentials-secret)
                                            
      --display-name string                 The display name of the supervised fine-tuning job.
      --early-stop                          Enable early stopping for the supervised fine-tuning job.
      --quiet                               If set, only errors will be printed.
      --eval-auto-carveout                  If set, the evaluation dataset will be auto-carved.
      --mtp-enable                          If set, enables MTP (Multi-Token-Prediction) layer (only available for Deepseek finetuning).
      --mtp-num-draft-tokens int32          Number of draft tokens in MTP. Needs to be between 1 and 3. Default is 1.
      --mtp-freeze-base-model               If set, freezes the base model parameters during MTP training.
      --dry-run                             Print the request proto without running it.
  -o, --output Output                       Set the output format to "text", "json", or "flag". (default text)
  -h, --help                                help for create
```

### Global flags

```
  -a, --account-id string   The Fireworks account ID. If not specified, reads account_id from ~/.fireworks/auth.ini.
      --api-key string      An API key used to authenticate with Fireworks.
  -p, --profile string      fireworks auth and settings profile to use.
```