OwnedModelSchema

base_model_idintegerrequired

ID of the base model this owned model was derived from.

created_atstringrequired

Human-readable creation timestamp.

model_idintegerrequired

Unique numeric identifier for this owned model.

model_namestringrequired

Name of the owned model (user-assigned or auto-generated).

organizationintegerrequired

Organization ID that owns this model.

statestringrequired

Current lifecycle state of the model (e.g., "dormant", "deploying", "deployed").

typestringrequired

Model origin type. Values: "fine_tuned", "third_party".

user_idintegerrequired

ID of the user who owns this model.

base_model_data object

Full metadata of the base model.

available_for_finetuningbooleanrequired

Whether this model can be used for fine-tuning jobs.

available_for_inferencebooleanrequired

Whether this model can be deployed for inference.

default_batch_sizeintegerrequired

Total batch size used during fine-tuning.

default_gradient_accumulation_stepsintegerrequired

Number of gradient accumulation steps for fine-tuning.

default_lrnumberrequired

Learning rate for fine-tuning.

default_micro_batch_sizeintegerrequired

Per-device micro-batch size for fine-tuning.

deployment_records[]required

List of active deployment records associated with this model.

display_namestringrequired

Human-readable name for the model shown in the UI.

hf_repostringrequired

Hugging Face repository identifier for this model.

model_idintegerrequired

Unique numeric identifier for the base model.

model_namestringrequired

Canonical model identifier used in API requests.

model_typestringrequired

Category of model. Common values: "language_model", "image_model".

supported_context_lenintegerrequired

Maximum context length (in tokens) supported by this model.

supported_locationsstring[]required

List of region identifiers where this model is available for deployment.

training_time_per_lognumberrequired

Estimated training time (in seconds) per log step, used for duration projections.

training_time_y_interceptnumberrequired

Y-intercept of the training time regression curve, used for fine-tuning duration estimates.

allowed_hf_lora_adapter_sizeinteger

Maximum allowed size (in bytes) for a Hugging Face LoRA adapter when fine-tuning this model.

input_token_cost_per_mnumber

Cost in USD per million input tokens for inference.

is_third_partyboolean

Whether this model was imported by the user (third-party) rather than provided natively by Hyperstack.

output_token_cost_per_mnumber

Cost in USD per million output tokens for inference.

training_cost_per_lognumber

Estimated training cost per log step, used for fine-tuning cost projections.

input_modalitiesstring[]

Input modalities the model accepts, such as text and image. A model that lists image supports image-to-text input.

output_modalitiesstring[]

Output modalities the model produces, such as text.

created_at_unixnumber

Unix epoch timestamp of model creation.

current_statestring

Detailed current state label, if available.

dataset_idinteger

ID of the training dataset used to fine-tune this model, if applicable.

deployment_recordsobject[]

Active deployment records for this model.

download_task_metadataobject

Metadata about the model download task, if this is a third-party import.

job_elapsed_minutesnumber

Total elapsed minutes for the fine-tuning job, if applicable.

last_deployed_onstring

Human-readable timestamp of the last deployment.

last_deployed_on_unixnumber

Unix epoch timestamp of the last deployment.

last_usedstring

Human-readable timestamp of the last inference request.

last_used_unixnumber

Unix epoch timestamp of the last inference request.

model_configobject

Fine-tuning configuration parameters used to train this model.

model_evaluationobject

Evaluation results for this model, if available.

model_evaluation_stateobject

Current status of each evaluation benchmark (e.g., mix_eval, needlehaystack).

parent_idinteger

ID of the parent model if this model was fine-tuned from another owned model.

self_hostedboolean

Whether this model is self-hosted by the user.

third_party_modelobject

Third-party import metadata (provider, repo, status, etc.), if applicable.

training_elapsed_minutesnumber

Elapsed training time in minutes for the fine-tuning job.

training_ended_atstring

Human-readable timestamp when training completed.

training_ended_at_unixnumber

Unix epoch timestamp when training completed.

updated_atstring

Human-readable timestamp of the most recent update.

OwnedModelSchema

{
  "base_model_id": 0,
  "created_at": "string",
  "model_id": 0,
  "model_name": "string",
  "organization": 0,
  "state": "string",
  "type": "string",
  "user_id": 0,
  "base_model_data": {
    "available_for_finetuning": true,
    "available_for_inference": true,
    "default_batch_size": "total",
    "default_gradient_accumulation_steps": "number",
    "default_lr": "learning",
    "default_micro_batch_size": "per-device",
    "deployment_records": [
      null
    ],
    "display_name": "string",
    "hf_repo": "string",
    "model_id": 0,
    "model_name": "string",
    "model_type": "string",
    "supported_context_len": 0,
    "supported_locations": [
      "string"
    ],
    "training_time_per_log": 0,
    "training_time_y_intercept": 0,
    "allowed_hf_lora_adapter_size": 0,
    "input_token_cost_per_m": 0,
    "is_third_party": true,
    "output_token_cost_per_m": 0,
    "training_cost_per_log": 0
  },
  "created_at_unix": 0,
  "current_state": "string",
  "dataset_id": 0,
  "deployment_records": [
    {}
  ],
  "download_task_metadata": {},
  "job_elapsed_minutes": 0,
  "last_deployed_on": "string",
  "last_deployed_on_unix": 0,
  "last_used": "string",
  "last_used_unix": 0,
  "model_config": {},
  "model_evaluation": {},
  "model_evaluation_state": {},
  "parent_id": 0,
  "self_hosted": true,
  "third_party_model": {},
  "training_elapsed_minutes": 0,
  "training_ended_at": "string",
  "training_ended_at_unix": 0,
  "updated_at": "string"
}