> ## 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. # List all jobs > List the metadata for all fine-tuning jobs. Returns a list of FinetuneResponseTruncated objects. ## OpenAPI ````yaml GET /fine-tunes openapi: 3.1.0 info: title: Together APIs description: The Together REST API. See https://docs.together.ai for more details. version: 2.0.0 termsOfService: https://www.together.ai/terms-of-service contact: name: Together Support url: https://www.together.ai/contact license: name: MIT url: https://github.com/togethercomputer/openapi/blob/main/LICENSE servers: - url: https://api.together.ai/v1 security: - bearerAuth: [] paths: /fine-tunes: get: tags: - Fine-tuning summary: List all jobs description: >- List the metadata for all fine-tuning jobs. Returns a list of FinetuneResponseTruncated objects. responses: '200': description: List of fine-tune jobs content: application/json: schema: $ref: '#/components/schemas/FinetuneTruncatedList' x-codeSamples: - lang: Python label: Together AI SDK (v2) source: | # Docs for v1 can be found by changing the above selector ^ from together import Together import os client = Together( api_key=os.environ.get("TOGETHER_API_KEY"), ) response = client.fine_tuning.list() for fine_tune in response.data: print(f"ID: {fine_tune.id}, Status: {fine_tune.status}") - lang: Python label: Together AI SDK (v1) source: | from together import Together import os client = Together( api_key=os.environ.get("TOGETHER_API_KEY"), ) response = client.fine_tuning.list() for fine_tune in response.data: print(f"ID: {fine_tune.id}, Status: {fine_tune.status}") - lang: TypeScript label: Together AI SDK (TypeScript) source: | import Together from "together-ai"; const client = new Together({ apiKey: process.env.TOGETHER_API_KEY, }); const response = await client.fineTuning.list(); for (const fineTune of response.data) { console.log(fineTune.id, fineTune.status); } - lang: JavaScript label: Together AI SDK (JavaScript) source: | import Together from "together-ai"; const client = new Together({ apiKey: process.env.TOGETHER_API_KEY, }); const response = await client.fineTuning.list(); for (const fineTune of response.data) { console.log(fineTune.id, fineTune.status); } - lang: Shell label: cURL source: | curl "https://api.together.ai/v1/fine-tunes" \ -H "Authorization: Bearer $TOGETHER_API_KEY" \ -H "Content-Type: application/json" components: schemas: FinetuneTruncatedList: type: object required: - data properties: data: type: array items: $ref: '#/components/schemas/FinetuneResponseTruncated' FinetuneResponseTruncated: type: object description: >- A truncated version of the fine-tune response, used for POST /fine-tunes, GET /fine-tunes and POST /fine-tunes/{id}/cancel endpoints required: - id - status - created_at - updated_at example: id: ft-01234567890123456789 status: completed created_at: '2023-05-17T17:35:45.123Z' updated_at: '2023-05-17T18:46:23.456Z' user_id: user_01234567890123456789 owner_address: user@example.com total_price: 1500 token_count: 850000 events: [] model: meta-llama/Llama-2-7b-hf model_output_name: mynamespace/meta-llama/Llama-2-7b-hf-32162631 n_epochs: 3 training_file: file-01234567890123456789 wandb_project_name: my-finetune-project properties: id: type: string description: Unique identifier for the fine-tune job status: $ref: '#/components/schemas/FinetuneJobStatus' created_at: type: string format: date-time description: Creation timestamp of the fine-tune job updated_at: type: string format: date-time description: Last update timestamp of the fine-tune job started_at: type: string format: date-time description: Start timestamp of the current stage of the fine-tune job user_id: type: string description: Identifier for who created the job. owner_address: type: string description: Owner address information total_price: type: integer description: Total price for the fine-tuning job token_count: type: integer description: Count of tokens processed events: type: array items: $ref: '#/components/schemas/FineTuneEvent' description: Events related to this fine-tune job training_file: type: string description: File-ID of the training file validation_file: type: string description: File-ID of the validation file packing: type: boolean description: Whether sequence packing is being used for training. max_seq_length: type: integer description: >- Maximum sequence length to use for training. If not specified, uses the maximum allowed for the model and training method. model: type: string description: Base model used for fine-tuning model_output_name: type: string suffix: type: string description: Suffix added to the fine-tuned model name n_epochs: type: integer description: Number of training epochs n_evals: type: integer description: Number of evaluations during training n_checkpoints: type: integer description: Number of checkpoints saved during training batch_size: type: integer description: Batch size used for training training_type: oneOf: - $ref: '#/components/schemas/FullTrainingType' - $ref: '#/components/schemas/LoRATrainingType' description: Type of training used (full or LoRA) training_method: oneOf: - $ref: '#/components/schemas/TrainingMethodSFT' - $ref: '#/components/schemas/TrainingMethodDPO' description: Method of training used learning_rate: type: number format: float description: Learning rate used for training lr_scheduler: $ref: '#/components/schemas/LRScheduler' description: Learning rate scheduler configuration warmup_ratio: type: number format: float description: Ratio of warmup steps max_grad_norm: type: number format: float description: Maximum gradient norm for clipping weight_decay: type: number format: float description: Weight decay value used random_seed: type: integer nullable: true description: > 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: type: string description: Weights & Biases project name wandb_name: type: string description: Weights & Biases run name from_checkpoint: type: string description: Checkpoint used to continue training from_hf_model: type: string description: Hugging Face Hub repo to start training from hf_model_revision: type: string description: The revision of the Hugging Face Hub model to continue training from progress: $ref: '#/components/schemas/FineTuneProgress' description: Progress information for the fine-tuning job FinetuneJobStatus: type: string enum: - pending - queued - running - compressing - uploading - cancel_requested - cancelled - error - completed FineTuneEvent: type: object required: - object - created_at - message - type - param_count - token_count - total_steps - wandb_url - step - checkpoint_path - model_path - training_offset - hash properties: object: description: The object type, which is always `fine-tune-event`. const: fine-tune-event created_at: type: string level: anyOf: - $ref: '#/components/schemas/FinetuneEventLevels' message: type: string type: $ref: '#/components/schemas/FinetuneEventType' param_count: type: integer token_count: type: integer total_steps: type: integer wandb_url: type: string step: type: integer checkpoint_path: type: string model_path: type: string training_offset: type: integer hash: type: string FullTrainingType: type: object properties: type: type: string enum: - Full required: - type LoRATrainingType: type: object properties: type: type: string enum: - Lora lora_r: type: integer lora_alpha: type: integer lora_dropout: type: number format: float default: 0 lora_trainable_modules: type: string default: all-linear required: - type - lora_r - lora_alpha TrainingMethodSFT: type: object properties: method: type: string enum: - sft train_on_inputs: oneOf: - type: boolean - type: string enum: - auto type: boolean default: auto description: >- Whether to mask user messages in conversational data or prompts in instruction data. required: - method - train_on_inputs TrainingMethodDPO: type: object properties: method: type: string enum: - dpo dpo_beta: type: number format: float default: 0.1 rpo_alpha: type: number format: float default: 0 dpo_normalize_logratios_by_length: type: boolean default: false dpo_reference_free: type: boolean default: false simpo_gamma: type: number format: float default: 0 required: - method LRScheduler: type: object properties: lr_scheduler_type: type: string enum: - linear - cosine lr_scheduler_args: oneOf: - $ref: '#/components/schemas/LinearLRSchedulerArgs' - $ref: '#/components/schemas/CosineLRSchedulerArgs' required: - lr_scheduler_type FineTuneProgress: type: object description: Progress information for a fine-tuning job required: - estimate_available - seconds_remaining properties: estimate_available: type: boolean description: Whether time estimate is available seconds_remaining: type: integer description: >- Estimated time remaining in seconds for the fine-tuning job to next state FinetuneEventLevels: type: string enum: - null - info - warning - error - legacy_info - legacy_iwarning - legacy_ierror FinetuneEventType: type: string enum: - 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 LinearLRSchedulerArgs: type: object properties: min_lr_ratio: type: number format: float default: 0 description: The ratio of the final learning rate to the peak learning rate CosineLRSchedulerArgs: type: object properties: min_lr_ratio: type: number format: float default: 0 description: The ratio of the final learning rate to the peak learning rate num_cycles: type: number format: float default: 0.5 description: Number or fraction of cycles for the cosine learning rate scheduler required: - min_lr_ratio - num_cycles securitySchemes: bearerAuth: type: http scheme: bearer x-bearer-format: bearer x-default: default ````