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Create

To start a new fine-tuning job: 
tg fine-tuning create --training-file [FILE_ID | PATH] --model [MODEL]

# Shorthand
tg ft -c --training-file [FILE_ID | PATH] --model [MODEL]
You must provide either --model (to start from a base model) or --from-checkpoint (to resume from a previous job). Before the job is submitted, the CLI prints an estimated price and asks for confirmation. Pass --confirm (or -y) to skip the prompt in scripts and CI.
If --training-file (or --validation-file) is a local path, the CLI uploads the file to the Files API automatically before kicking off the job.

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Parameters

FlagDescription
--training-file/-t [string | Path]required
Training file ID from the Files API or a local path to upload. The maximum allowed file size is 25 GB.
--model [string]Base model to fine-tune. See supported models. Required unless --from-checkpoint is set.
--from-checkpoint [string]Continue training from a previous fine-tuning job. Format: JOB_ID/OUTPUT_MODEL_NAME:STEP. The step is optional; the final checkpoint is used when omitted. Mutually exclusive with --model.
--validation-file/-v [string]Validation file ID from the Files API or a local path to upload. Required when --n-evals > 0. The maximum allowed file size is 25 GB.
--suffix [string]Up to 40 characters appended to the fine-tuned model name. Recommended to differentiate fine-tuned models.
--packing/--no-packingWhether to use sequence packing for training. Default: enabled.
--max-seq-length [integer]Maximum sequence length to use for training. Required when --no-packing is set. Defaults to the maximum allowed for the model and training type.
--n-epochs/-ne [integer]Number of epochs to fine-tune on the dataset. Default: 1. Min: 1. Max: 20.
--n-evals [integer]Number of evaluation loops to run on the validation set. Default: 0. Min: 0. Max: 100.
--n-checkpoints/-c [integer]The number of checkpoints to save during training. Default: 1. One checkpoint is always saved on the last epoch. Must be 1 ≤ n-checkpoints ≤ n-epochs.
--batch-size/-b [integer | max]Batch size for each training iteration. See supported models for min and max batch sizes per model. Default: max.
--learning-rate/-lr [float]Learning rate multiplier. Default: 0.00001. Min: 0.00000001. Max: 0.01.
--lr-scheduler-type [linear | cosine]Learning rate scheduler type. Default: cosine.
--min-lr-ratio [float]Ratio of the final learning rate to the peak learning rate. Default: 0.0. Min: 0.0. Max: 1.0.
--scheduler-num-cycles [float]Number or fraction of cycles for the cosine learning rate scheduler. Must be non-negative. Default: 0.5.
--warmup-ratio [float]Fraction of steps at the start of training to linearly warm up the learning rate. Default: 0.0. Min: 0.0. Max: 1.0.
--max-grad-norm [float]Max gradient norm for gradient clipping. Set to 0 to disable. Default: 1.0. Min: 0.0.
--weight-decay [float]Weight decay for the optimizer. Default: 0.0. Min: 0.0.
--random-seed [integer]Random seed for reproducible training. Uses the server default if unset.
--confirm/-ySkip the price-confirmation prompt. Useful in scripts and CI.
--train-on-inputs [true | false | auto]Whether to mask user messages in conversational data or prompts in instruction data.

auto infers from the data format:
  • Datasets with the "text" field (general format): inputs are not masked.
  • Datasets with the "messages" field (conversational format) or "prompt" and "completion" fields (instruction format): inputs are masked.
Default: auto.
--train-vision/--no-train-visionUpdate the vision encoder parameters. Default: false. Only available for vision-language models.
--from-hf-model [string]Hugging Face Hub repository to start training from. Should match the base model’s architecture and size. When --lora is set with --lora-trainable-modules all-linear, the modules k_proj, o_proj, q_proj, v_proj are targeted for adapter training.
--hf-model-revision [string]Revision (branch name or commit hash) of the Hugging Face Hub model.
--hf-api-token [string]Hugging Face API token for downloading from a private repo or uploading the output model.
--hf-output-repo-name [string]Hugging Face repo to upload the fine-tuned model to.

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Weights & Biases

FlagDescription
--wandb-api-key [string]Your Weights & Biases API key. Falls back to the WANDB_API_KEY environment variable.
--wandb-base-url [string]Base URL of a dedicated Weights & Biases instance. Leave empty if you are not using a self-hosted instance.
--wandb-project-name [string]Weights & Biases project for your run. Defaults to together.
--wandb-name [string]Weights & Biases run name.
--wandb-entity [string]Weights & Biases entity (team or user).

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LoRA

FlagDescription
--lora/--no-loraForce LoRA fine-tuning (--lora) or full fine-tuning (--no-lora). When omitted, the API auto-detects: it defaults to LoRA on most base models, and inherits the parent job’s training type when --from-checkpoint is set.
--lora-r [integer]Rank for LoRA adapter weights. Default: 8. Min: 1. Max: 64.
--lora-alpha [integer]Alpha for LoRA adapter training. Default: 8. Min: 1.
--lora-dropout [float]Dropout probability for LoRA layers. Default: 0.0. Min: 0.0. Max: 1.0.
--lora-trainable-modules [string]Comma-separated list of LoRA trainable modules. Default: all-linear. See supported modules for LoRA training.

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Preference fine-tuning (DPO, RPO, SimPO)

FlagDescription
--training-method [sft | dpo]Training method. sft is supervised fine-tuning; dpo is Direct Preference Optimization. Default: sft. The DPO method also accepts the RPO and SimPO loss modifiers below.
--dpo-beta [float]Beta parameter for DPO training. Only used when --training-method dpo.
--dpo-normalize-logratios-by-lengthNormalize logratios by sample length. Only used when --training-method dpo. Default: false.
--rpo-alpha [float]RPO alpha parameter (adds NLL term to the DPO loss). Only used when --training-method dpo.
--simpo-gamma [float]SimPO gamma parameter. Only used when --training-method dpo.
The id field in the JSON response contains the fine-tune job ID (ft-…) that you use to retrieve status, list events, cancel the job, and download weights.

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List

To list past and running fine-tune jobs: 
tg fine-tuning list

# Shorthand
tg ft ls
Jobs are listed newest first.

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Retrieve

To retrieve metadata for a job, including its current status: 
tg fine-tuning retrieve [FT_ID]

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List events

To list events of a past or running job: 
tg fine-tuning list-events [FT_ID]

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Cancel

To cancel a running job: 
tg fine-tuning cancel [FT_ID]

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List checkpoints

To list saved checkpoints of a job: 
tg fine-tuning list-checkpoints [FT_ID]

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Download model weights

To download the weights of a fine-tuned model, run: 
# Download the model to the current working directory.
tg fine-tuning download [FT_ID]
The command downloads Zstandard-compressed (.zst) weights. To extract them, run tar -xf filename.

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Parameters

FlagDescription
--output-dir/-o [Path]Output directory.
--checkpoint-step/-s [integer]Download a specific checkpoint’s weights. Defaults to the latest checkpoint.
--checkpoint-type/-c [merged | adapter | default]Checkpoint type. merged and adapter apply to LoRA jobs only; default resolves to merged for LoRA jobs and to the full model for non-LoRA jobs. Default: merged.

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Delete

To delete a fine-tuning job: 
tg fine-tuning delete [FT_ID]

# Shorthand
tg ft -d [FT_ID]

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Parameters

FlagDescription
--forceBypass confirmation prompt.