---
title: "List all jobs"
method: GET
path: "/fine-tunes"
tags: ["Fine-tuning"]
---

# List all jobs

`GET /fine-tunes`

List the metadata for all fine-tuning jobs. Returns a list of FinetuneResponseTruncated objects.

## Response `200`

List of fine-tune jobs

- FinetuneTruncatedList
  - `data` FinetuneResponseTruncated[], required
    - `id` string, required — Unique identifier for the fine-tune job
    - `status` 'pending' | 'queued' | 'running' | 'compressing' | 'uploading' | 'cancel_requested' | 'cancelled' | 'error' | 'completed', required
    - `created_at` string, date-time, required — Creation timestamp of the fine-tune job
    - `updated_at` string, date-time, required — Last update timestamp of the fine-tune job
    - `started_at` string, date-time — Start timestamp of the current stage of the fine-tune job
    - `user_id` string, required — ID of the user who created the fine-tune job.
    - `owner_address` string — Owner address information
    - `total_price` integer — Total price for the fine-tuning job
    - `token_count` integer — Count of tokens processed
    - `events` FineTuneEvent[] — Events related to this fine-tune job
      - `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.
    - `training_file` string — File-ID of the training file
    - `validation_file` string — File-ID of the validation file
    - `packing` boolean — Whether sequence packing is being used for training.
    - `max_seq_length` integer — Maximum sequence length to use for training. If not specified, uses the maximum allowed for the model and training method.
    - `model` string — Base model used for fine-tuning
    - `model_output_name` string
    - `suffix` string — Suffix added to the fine-tuned model name
    - `n_epochs` integer — Number of training epochs
    - `n_evals` integer — Number of evaluations during training
    - `n_checkpoints` integer — Number of checkpoints saved during training
    - `batch_size` integer — Batch size used for training
    - `training_type` union — Type of training used (full or LoRA)
      - FullTrainingType
        - `type` 'Full', required
      - LoRATrainingType — 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.
    - `training_method` union — Method of training used
      - TrainingMethodSFT
        - `method` 'sft', required
        - `train_on_inputs` boolean, required — Whether to mask user messages in conversational data or prompts in instruction data.
      - TrainingMethodDPO
        - `method` 'dpo', required
        - `dpo_beta` number
        - `rpo_alpha` number
        - `dpo_normalize_logratios_by_length` boolean
        - `dpo_reference_free` boolean
        - `simpo_gamma` number
    - `learning_rate` number — Learning rate used for training
    - `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 — Ratio of warmup steps
    - `max_grad_norm` number — Maximum gradient norm for clipping
    - `weight_decay` number — Weight decay value used
    - `random_seed` integer, nullable — Random seed used for training. Integer when set; null if not stored (e.g. legacy jobs) or no explicit seed was recorded.
    - `wandb_project_name` string — Weights & Biases project name
    - `wandb_name` string — Weights & Biases run name
    - `from_checkpoint` string — Checkpoint used to continue training
    - `from_hf_model` string — Hugging Face Hub repo to start training from
    - `hf_model_revision` string — The revision of the Hugging Face Hub model to continue training from
    - `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.

---

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