> ## Documentation Index > Fetch the complete documentation index at: https://docs.together.ai/llms.txt > Use this file to discover all available pages before exploring further. # Fine-tuning > Create, monitor, and manage fine-tuning jobs from your terminal. ## Create To start a new fine-tuning job: ```bash theme={null} 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. ### Parameters | Flag | Description | | ------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--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 [the model page](/docs/fine-tuning-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-packing` | Whether 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 [the model page](/docs/fine-tuning-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/-y` | Skip 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:Default: `auto`. | | `--train-vision/--no-train-vision` | Update 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. | #### Weights & Biases | Flag | Description | | ------------------------------- | ----------------------------------------------------------------------------------------------------------- | | `--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). | #### LoRA | Flag | Description | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `--lora/--no-lora` | Force 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](/docs/fine-tuning-lora-supported-modules). | #### Preference fine-tuning (DPO, RPO, SimPO) | Flag | Description | | ------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `--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-length` | Normalize 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. ## List To list past and running fine-tune jobs: ```bash theme={null} tg fine-tuning list # Shorthand tg ft ls ``` Jobs are listed newest first. ## Retrieve To retrieve metadata for a job, including its current status: ```bash theme={null} tg fine-tuning retrieve [FT_ID] ``` ## List events To list events of a past or running job: ```bash theme={null} tg fine-tuning list-events [FT_ID] ``` ## Cancel To cancel a running job: ```bash theme={null} tg fine-tuning cancel [FT_ID] ``` ## List checkpoints To list saved checkpoints of a job: ```bash theme={null} tg fine-tuning list-checkpoints [FT_ID] ``` ## Download model weights To download the weights of a fine-tuned model, run: ```bash Basic theme={null} # Download the model to the current working directory. tg fine-tuning download [FT_ID] ``` ```bash Specify Directory theme={null} tg fine-tuning download [FT_ID] \ --output-dir ./models ``` ```bash Download checkpoint theme={null} # Use `tg fine-tuning list-checkpoints` to find the checkpoint index. tg fine-tuning download [FT_ID] \ --checkpoint-step 0 ``` ```bash Download LoRA adapter theme={null} tg fine-tuning download [FT_ID] \ --checkpoint-type adapter ``` The command downloads Zstandard-compressed (`.zst`) weights. To extract them, run `tar -xf filename`. ### Parameters | Flag | Description | | ----------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `--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`. | ## Delete To delete a fine-tuning job: ```bash theme={null} tg fine-tuning delete [FT_ID] # Shorthand tg ft -d [FT_ID] ``` ### Parameters | Flag | Description | | --------- | --------------------------- | | `--force` | Bypass confirmation prompt. |