---
title: "List job"
method: GET
path: "/fine-tunes/{id}"
tags: ["Fine-tuning"]
---

# List job

`GET /fine-tunes/{id}`

List the metadata for a single fine-tuning job.

## Path parameters

- `id` string, required — The ID of the job to retrieve

## Response `200`

Fine-tune job details retrieved successfully

- FinetuneResponse
  - `id` string, uuid, required
  - `training_file` string
  - `validation_file` string
  - `model` string
  - `model_output_name` string
  - `model_output_path` string
  - `tokenized_dataset_path` string — Storage path for the tokenized dataset archive generated for this fine-tune job.
  - `tokenized_dataset_uploaded_at` string, date-time — Timestamp when the tokenized dataset archive was uploaded.
  - `model_object_id` string — Together model registry object ID for the final model weights (e.g. `ml_...`).
  - `model_object_name` string — Together model registry name for the final model weights, formatted as `<project_slug>/<model_name>`.
  - `model_object_revision_id` string — Together model registry revision ID for the final model weights (e.g. `rv_...`).
  - `adapter_object_id` string — Together model registry object ID for the final adapter weights on LoRA jobs.
  - `adapter_object_name` string — Together model registry name for the final adapter weights on LoRA jobs, formatted as `<project_slug>/<model_name>-adapter`.
  - `adapter_object_revision_id` string — Together model registry revision ID for the final adapter weights on LoRA jobs.
  - `user_id` string, required — ID of the user who created the fine-tune job.
  - `trainingfile_numlines` integer
  - `trainingfile_size` integer
  - `created_at` string, date-time
  - `updated_at` string, date-time
  - `started_at` string, date-time
  - `n_epochs` integer
  - `n_checkpoints` integer
  - `n_evals` integer
  - `batch_size` union
    - integer
    - 'max'
  - `learning_rate` number
  - `lr_scheduler` LRScheduler
    - `lr_scheduler_type` 'linear' | 'cosine', required
    - `lr_scheduler_args` union
      - LinearLRSchedulerArgs
        - `min_lr_ratio` number — The ratio of the final learning rate to the peak learning rate
      - CosineLRSchedulerArgs
        - `min_lr_ratio` number, required — The ratio of the final learning rate to the peak learning rate
        - `num_cycles` number, required — Number or fraction of cycles for the cosine learning rate scheduler
  - `warmup_ratio` number
  - `max_grad_norm` number
  - `weight_decay` number
  - `eval_steps` integer
  - `train_on_inputs` union
    - boolean
    - 'auto'
  - `training_method` union
    - object
      - `method` 'sft', required
      - `train_on_inputs` boolean, required — Whether to mask user messages in conversational data or prompts in instruction data.
    - object
      - `method` 'dpo', required
      - `dpo_beta` number
      - `rpo_alpha` number
      - `dpo_normalize_logratios_by_length` boolean
      - `dpo_reference_free` boolean
      - `simpo_gamma` number
  - `training_type` union
    - object
      - `type` 'Full', required
    - object — LoRA training configuration for a fine-tuning job.
      - `type` 'Lora', required — Identifies this request as a LoRA fine-tune.
      - `lora_r` integer, required — Rank of the LoRA adapter matrices.
      - `lora_alpha` integer, required — Scaling factor applied to the LoRA adapter weights.
      - `lora_dropout` number — Dropout probability applied to LoRA adapter inputs.
      - `lora_trainable_modules` string — Comma-separated LoRA target modules. Use `all-linear` for model defaults; MoE expert modules (`w_up`, `w_gate`, `w_down`) can be combined with attention modules on compatible models. Fine-tunes that target any expert module produce adapter-only output.
  - `multimodal_params` MultimodalParams
    - `train_vision` boolean — Whether to train the vision encoder of the model. Only available for multimodal models.
  - `status` 'pending' | 'queued' | 'running' | 'compressing' | 'uploading' | 'cancel_requested' | 'cancelled' | 'error' | 'completed', required
  - `job_id` string
  - `events` FineTuneEvent[]
    - `object` 'fine-tune-event', required — The object type, which is always `fine-tune-event`.
    - `created_at` string, required
    - `level` string
    - `message` string, required
    - `type` 'job_pending' | 'job_start' | 'job_stopped' | 'model_downloading' | 'model_download_complete' | 'training_data_downloading' | 'training_data_download_complete' | 'validation_data_downloading' | 'validation_data_download_complete' | 'wandb_init' | 'training_start' | 'checkpoint_save' | 'billing_limit' | 'epoch_complete' | 'training_complete' | 'model_compressing' | 'model_compression_complete' | 'model_uploading' | 'model_upload_complete' | 'job_complete' | 'job_error' | 'cancel_requested' | 'job_restarted' | 'refund' | 'warning' | 'early_stopped' | 'tokenized_dataset_upload_complete', required
    - `param_count` integer
    - `token_count` integer
    - `total_steps` integer
    - `wandb_url` string
    - `step` integer
    - `checkpoint_path` string
    - `model_path` string
    - `tokenized_dataset_path` string — Storage path for the tokenized dataset archive associated with this event.
    - `early_stopping_best_step` integer, nullable — For early_stopped events, the selected best-checkpoint step when a finite best metric exists. If early_stopping_best_metric_value is null, this is the halt step.
    - `early_stopping_best_metric_value` number, nullable — For early_stopped events, the best validation loss observed. Null if no improving evaluation was recorded.
  - `token_count` integer
  - `param_count` integer
  - `total_price` integer
  - `epochs_completed` integer
  - `queue_depth` integer
  - `wandb_project_name` string
  - `wandb_url` string
  - `from_checkpoint` string
  - `from_hf_model` string
  - `hf_model_revision` string
  - `progress` FineTuneProgress — Progress information for a fine-tuning job
    - `estimate_available` boolean, required — Whether time estimate is available
    - `seconds_remaining` integer, required — Estimated time remaining in seconds for the fine-tuning job to next state
  - `early_stopped` boolean — Whether the early-stopping criterion triggered.
  - `early_stopping_best_step` integer — Step associated with the selected early-stopping artifact. When early_stopping_best_metric is null, no finite best metric was recorded; this is the halt step, not a best-checkpoint step.
  - `early_stopping_best_metric` number, nullable — Best validation loss observed, corresponding to early_stopping_best_step. Null if no improving evaluation was recorded (for example, a non-finite first evaluation).

---

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