Custom Models¶

Lamia Studio supports models beyond the built-in Anthropic and OpenAI options, including Ollama local models and custom LLM adapters.

Ollama (Local Models)¶

To use local models via Ollama:

Install and start Ollama on your machine
Add the model to your project config.yaml:

model_chain:
  - name: "ollama:llama3"
    max_retries: 2

The model will appear in the chat dropdown as llama3 (ollama).

No API key is needed for Ollama — it runs locally.

Custom LLM Adapters¶

Lamia supports custom LLM adapters for providers not built into the system. These are Python files placed in the extensions/adapters/ folder of your project. See Lamia Docs — Custom LLM Adapters for the adapter API and implementation guide.

Creating an Adapter¶

An adapter is a Python file that implements the Lamia LLM adapter interface. The file name should match the provider name returned by the adapter's name() method — for example, a provider named "my_provider" should live in my_provider.py.

Place it in your project's extensions/adapters/ folder:

your-project/
├── extensions/
│   └── adapters/
│       └── my_provider.py
└── config.yaml

Lamia Studio automatically copies custom adapters from your workspace into the IDE's internal folder (~/.lamia/ide/extensions/adapters/). The copied file is named after the provider name from the adapter's name() classmethod, ensuring unique identification.

Add the adapter's provider to your config.yaml:

model_chain:
  - name: "my_provider:my-model-v1"
    max_retries: 3

API Keys for Custom Adapters¶

For custom providers that require API keys, add the key to ~/.lamia/.env:

# The env var name is defined by the adapter's env_var_names() method
MY_PROVIDER_API_KEY=your-key-here

Listing Available Models¶

Use the CLI to discover models from any provider:

# List models from all configured providers
lamia models

# List models from a specific provider
lamia models --provider anthropic

Custom adapters can support this by implementing the models() classmethod.