> ## Documentation Index > Fetch the complete documentation index at: https://docs.simplismart.ai/llms.txt > Use this file to discover all available pages before exploring further. # Retrieve List of LLM/VLM Jobs By Org ID > Fetches a list of all LLM or VLM training jobs associated with the specified organization. ## OpenAPI ````yaml openapi-specs/playground/llm-training.yaml GET /job/get/ openapi: 3.1.0 info: title: LLM/VLM Training API description: API endpoints for training, monitoring, and managing LLM/VLM models version: 2.0.0 servers: - url: https://training-suite.app.simplismart.ai description: Training Suite Production Server security: - BearerAuth: [] tags: - name: Training description: LLM/VLM model training endpoints paths: /job/get/: get: tags: - Training summary: Retrieve details of a specific LLM/VLM training job description: >- Fetch metadata and configuration details of a training job identified by the provided request_id and org_id. operationId: getTrainingJob parameters: - in: query name: org_id schema: type: string required: true description: Organization ID to which the training job belongs. example: 0bf00b43-430a-4ca3-a8b3-b13cc8dc6d4f - in: query name: request_id schema: type: string required: true description: Unique identifier for the training job request. example: 3fe92c25-e5fd-4281-93df-5aa886064dbd - in: header name: Authorization schema: type: string required: true description: Bearer token for authentication and authorization. example: Bearer responses: '200': description: The requested training job details were retrieved successfully. content: application/json: schema: $ref: '#/components/schemas/GetTrainingJobResponse' '400': description: Invalid request, such as missing or incorrect parameters. '401': description: Unauthorized access due to invalid or missing authorization token. '404': description: Training job not found for the provided request_id and org_id. '500': description: Internal server error while retrieving the training job. components: schemas: GetTrainingJobResponse: type: object properties: request_id: type: string description: Unique identifier for the training job request experiment_name: type: string description: Name of the training experiment status: type: string description: Current status of the training job enum: - QUEUED - RUNNING - COMPLETED - FAILED - CANCELED created_at: type: string format: date-time description: Timestamp when the job was created updated_at: type: string format: date-time description: Timestamp when the job was last updated started_at: type: string format: date-time description: Timestamp when the job started running nullable: true completed_at: type: string format: date-time description: Timestamp when the job completed nullable: true progress: type: number description: Progress percentage of the training job (0-100) dataset_config: $ref: '#/components/schemas/DatasetConfig' model_details: $ref: '#/components/schemas/ModelDetails' train_config: $ref: '#/components/schemas/TrainConfig' dataset_details: $ref: '#/components/schemas/DatasetDetails' infra_config: $ref: '#/components/schemas/InfraConfig' output_model_path: type: string description: Path to the trained model output (when completed) nullable: true error_message: type: string description: Error message if the job failed nullable: true DatasetConfig: type: object properties: preprocessing: type: object properties: lazy_tokenize: type: boolean description: Whether to tokenize lazily during training streaming: type: boolean description: Whether to use streaming mode for large datasets prompt: type: object properties: system: type: string nullable: true description: System prompt to prepend to inputs max_length: type: integer description: Maximum token length for sequences template: type: string nullable: true description: Custom prompt template split: type: object properties: type: type: string enum: - random - sequential description: Type of dataset split ratios: type: array items: type: number description: Split ratios for train/validation/test ModelDetails: type: object properties: base_model: type: string description: Base model identifier (e.g., HuggingFace model path) ownership: type: string enum: - public - private description: Whether the model is publicly accessible or private source_type: type: string enum: - hf - s3 - local description: Source type for the base model model_type: type: string enum: - llm - vlm description: Type of model (Language Model or Vision-Language Model) quantization: type: object properties: quant_bits: type: integer enum: - 4 - 8 - 16 description: Quantization bit precision TrainConfig: type: object properties: type: type: string enum: - sft - dpo - ppo description: >- Training type (Supervised Fine-Tuning, Direct Preference Optimization, Proximal Policy Optimization) torch_dtype: type: string enum: - float16 - bfloat16 - float32 description: PyTorch data type for training task_type: type: string enum: - causal_lm - seq2seq description: Task type for the model train_type: type: string enum: - lora - qlora - full description: Training method (LoRA, QLoRA, or Full fine-tuning) tuner_backend: type: string description: Backend used for parameter-efficient fine-tuning hyperparameters: type: object properties: num_epochs: type: integer description: Number of training epochs per_device_train_batch_size: type: integer description: Batch size per device for training per_device_eval_batch_size: type: integer description: Batch size per device for evaluation gradient_checkpointing: type: boolean description: Whether to use gradient checkpointing for memory efficiency save_steps: type: integer description: Save checkpoint every N steps save_total_limit: type: integer description: Maximum number of checkpoints to keep eval_steps: type: integer description: Evaluate model every N steps logging_steps: type: integer description: Log metrics every N steps learning_rate: type: number description: Learning rate for optimization dataloader_num_workers: type: integer description: Number of workers for data loading adapter_config: type: object properties: r: type: integer description: LoRA rank parameter alpha: type: integer description: LoRA alpha parameter for scaling dropout: type: number description: Dropout rate for adapter layers targets: type: array items: type: string description: Target modules for applying adapters distributed: type: object properties: type: type: string enum: - ddp - fsdp - deepspeed description: Distributed training strategy DatasetDetails: type: object properties: dataset_name: type: string description: Name of the dataset dataset_path: type: string description: Path or URI to the dataset dataset_description: type: string description: Description of the dataset dataset_type: type: string enum: - jsonl - csv - parquet description: File format of the dataset dataset_format: type: string enum: - sharegpt - alpaca - instruction description: Data format/schema of the dataset source_type: type: string enum: - s3 - gcs - local - hf description: Source location type ownership: type: string enum: - public - private description: Whether the dataset is publicly accessible or private secret_id: type: string description: ID of the secret containing credentials for private datasets nullable: true region: type: string description: Cloud region for the dataset storage nullable: true InfraConfig: type: object properties: gpu_type: type: string enum: - h100 - a100 - a10g - v100 - t4 description: Type of GPU to use for training gpu_count: type: integer minimum: 1 description: Number of GPUs per node infra_type: type: string description: Infrastructure provider type node_count: type: integer minimum: 1 description: Number of compute nodes for distributed training securitySchemes: BearerAuth: type: http scheme: bearer bearerFormat: JWT description: JWT token for authentication ````