SambaStack Model Custom Resource - SambaNova Documentation

A Model defines which checkpoints are available for each architecture, along with the tokenizer to use. It is the source of checkpoints for a deployment: checkpoint paths are never declared in a bundle or a deployment, and the operator resolves them from the Model you reference. You author a Model resource when bringing your own checkpoint. Serving a custom checkpoint against an already-supported architecture requires adding a Model resource only, leaving the profile untouched. Model resources use apiVersion: sambanova.ai/v1alpha1.

Compatibility between a checkpoint and a profile is defined by model_arch, but the operator does not verify it. Select a checkpoint whose architecture is compatible with the profile you reference. See Identify available models and profiles.

Structure

apiVersion: sambanova.ai/v1alpha1
kind: Model
metadata:
  name: deepseek-v3-2
spec:
  name: DeepSeek-V3.2
  owner: no-reply@sambanova.ai
  checkpoints:
    deepseek:
      versions:
        "1":
          source: gs://<SAMBASTACK_ARTIFACTS_BUCKET>/path/to/checkpoint
          checkpoint_status: stable
          tool_support: true
  tokenizer:
    path: ./DeepSeek-V3.2_tokenizer
  metadata:
    capabilities:
      - text

Field	Required	Description
`metadata.name`	Yes	Kubernetes resource name. This value is used in `modelConfigs[].model` references.
`spec.name`	Yes	Serving name used in the inference API.
`spec.aliases`	No	Alternative names that route to this model.
`spec.checkpoints.<arch>`	Yes	Checkpoints keyed by architecture. The `<arch>` key must match the `model_arch` value of the PEFs in the profile that this model is paired with.
`spec.checkpoints.<arch>.versions.<version>.source`	Yes	Path pointing to the model checkpoint. Find available checkpoints in Supported Models and Bundles.
`spec.checkpoints.<arch>.versions.<version>.checkpoint_status`	Yes	Lifecycle status of this checkpoint version. See PEF and checkpoint lifecycle status.
`spec.checkpoints.<arch>.versions.<version>.tool_support`	No	Whether this checkpoint is compatible with tools and function calling. Defaults to `false`.
`spec.checkpoints.<arch>.versions.<version>.vision_embedding_checkpoint`	No	Companion vision embedding checkpoint, used for multimodal models.
`spec.tokenizer.path`	Yes	Path to the local tokenizer to use. For a custom checkpoint, set this to the tokenizer of the base model that the checkpoint was derived from.
`spec.tokenizer.endpointUrls`	No	URLs to send tokenizer requests to. One URL is selected at random per request.
`spec.owner`	Yes	Email address of the model owner for tracking and notifications.
`spec.tool_support`	No	Whether the model supports using tools.
`spec.public`	No	Whether the model appears in the public models list.
`spec.metadata.capabilities`	Yes	Capabilities of the model, for example `text`, `vision`, `audio`, `embeddings`, or `reasoning`.
`spec.metadata`	No	Additional catalog metadata surfaced in the models API and documentation: `provider`, `license`, `overview`, `architecture`, `languages`, `category`, `text_only`, and `vocabulary_size`.
`spec.expertFields`	No	Defaults applied to every expert instance of this model at deployment time: `output_processor`, `postprocess_parser`, `enable_reasoning_effort`, and `tiktoken_vocab_rel_path`. Bundle-side values for the same keys take precedence.
`spec.price`	No	Pricing for the model: `input_tokens`, `output_tokens`, `input_cache_read`, `input_cache_write`, and `input_audio_duration_per_hour`.
`spec.fallback`	No	Fallback configuration, typically for multimodal support. Takes `model` and `when_missing`, both required when set.
`spec.api_forward_url`	No	Forwards traffic for this model to another cluster.

The paths to checkpoints hosted by SambaNova will be provided to you by your SambaNova contact. If you have hosted your own checkpoints, you can include those paths in the source fields above.

ModelProfile

The runtime shape a model is paired with

Deploying custom checkpoints

The workflow for registering and serving your own checkpoint

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