llm-cerebras 0.1.8
Plugin for LLM adding fast Cerebras inference API support
Requires Python: >3.7

# llm-cerebras

This is a plugin for [LLM](https://llm.datasette.io/) that adds support for the Cerebras inference API.

## Installation

Install this plugin in the same environment as LLM.

```bash
pip install llm-cerebras
```

## Configuration

You'll need to provide an API key for Cerebras.

```bash
llm keys set cerebras
```

## Listing available models

The plugin automatically fetches the latest available models from the Cerebras API and caches them for 24 hours.

```bash
llm models list | grep cerebras
# CerebrasModel: cerebras-llama3.1-8b
# CerebrasModel: cerebras-llama3.3-70b
# CerebrasModel: cerebras-llama-4-scout-17b-16e-instruct
# CerebrasModel: cerebras-qwen-3-32b
```

## Refreshing models

To get the latest models from the Cerebras API and update the cache:

```bash
llm cerebras refresh
```

This will fetch the current list of available models and save them to the cache. The models are automatically cached for 24 hours, so you typically don't need to refresh manually unless you want to check for newly released models.

## Schema Support

The llm-cerebras plugin supports schemas for structured output. You can use either compact schema syntax or full JSON Schema:

```bash
# Using compact schema syntax
llm -m cerebras-llama3.3-70b 'invent a dog' --schema 'name, age int, breed'

# Using multi-item schema for lists
llm -m cerebras-llama3.3-70b 'invent three dogs' --schema-multi 'name, age int, breed'

# Using full JSON Schema 
llm -m cerebras-llama3.3-70b 'invent a dog' --schema '{
  "type": "object",
  "properties": {
    "name": {"type": "string"},
    "age": {"type": "integer"},
    "breed": {"type": "string"}
  },
  "required": ["name", "age", "breed"]
}'
```

### Schema with Descriptions

You can add descriptions to your schema fields to guide the model:

```bash
llm -m cerebras-llama3.3-70b 'invent a famous scientist' --schema '
name: the full name including any titles
field: their primary field of study
year_born int: year of birth
year_died int: year of death, can be null if still alive
achievements: a list of their major achievements
'
```

### Creating Schema Templates

You can save schemas as templates for reuse:

```bash
# Create a template
llm -m cerebras-llama3.3-70b --schema 'title, director, year int, genre' --save movie_template

# Use the template
llm -t movie_template 'suggest a sci-fi movie from the 1980s'
```

## Development

To set up this plugin locally, first checkout the code. Then create a new virtual environment:

```bash
cd llm-cerebras
python -m venv venv
source venv/bin/activate
```

Now install the dependencies and test dependencies:

```bash
pip install -e '.[test]'
```

### Running Tests

To run the unit tests:

```bash
pytest tests/test_cerebras.py tests/test_schema_support.py
```

To run integration tests (requires a valid API key):

```bash
pytest tests/test_integration.py
```

To run automated user workflow tests:

```bash
pytest tests/test_automated_user.py
```

You can run specific test types using markers:

```bash
pytest -m "integration"  # Run only integration tests
pytest -m "user"         # Run only user workflow tests
```

## License

Apache 2.0