Models

Models power your AI applications. Add a model to a project to deploy it, then make inference requests.

Deploy a model

Add a model to your project, then deploy it:

adaptive.models.add_to_project(model="llama-3.1-8b-instruct")
adaptive.models.deploy(model="llama-3.1-8b-instruct", wait=True)

# Or use attach() to do both in one call
adaptive.models.attach(model="llama-3.1-8b-instruct", wait=True)

Parameter	Type	Required	Description
`model`	str	Yes	Model key from the registry
`wait`	bool	No	Block until model is online (default: False)
`make_default`	bool	No	Set as default model for the project

The model becomes available within a few minutes. Adaptive supports most transformer-based models including Llama, Qwen, Gemma, Mistral, and DeepSeek. See Integrations for proprietary models.Multimodal models (suffixed -multimodal) include a vision encoder that is automatically spawned alongside the decoder. The vision encoder is frozen during training. See Multimodal StringThread for supported models and image handling.See SDK Reference for all model methods.

Deploy a model

The Models page lists all models in your registry

Navigate to your project and open the Models tab. Click Add Model and select from the registry.

Adapters appear indented under their backbone model

Fine-tuned adapter models appear indented under their base model.Multimodal models (suffixed -multimodal) include a vision encoder that is automatically spawned alongside the decoder. The vision encoder is frozen during training. See Multimodal StringThread for supported models and image handling.

Start

Core

Advanced

Deploy

Updates

Deploy a model

Deploy a model

​Deploy a model

​Deploy a model

Deploy a model

Deploy a model