llm-cerebras

This is a plugin for LLM that adds support for the Cerebras inference API.

Installation

Install this plugin in the same environment as LLM.

pip install llm-cerebras

Configuration

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

llm keys set cerebras

Listing available models

llm models list | grep cerebras
# cerebras-llama3.1-8b - Cerebras
# cerebras-llama3.3-70b - Cerebras
# cerebras-deepseek-r1-distill-llama-70b - Cerebras

Schema Support

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

# 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:

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:

# 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:

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

Now install the dependencies and test dependencies:

pip install -e '.[test]'

Running Tests

To run the unit tests:

pytest tests/test_cerebras.py tests/test_schema_support.py

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

pytest tests/test_integration.py

To run automated user workflow tests:

pytest tests/test_automated_user.py

You can run specific test types using markers:

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

License

Apache 2.0