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Cerebras

https://inference-docs.cerebras.ai/api-reference/chat-completions

tip

We support ALL Cerebras models, just set model=cerebras/<any-model-on-cerebras> as a prefix when sending litellm requests

API Key​

# env variable
os.environ['CEREBRAS_API_KEY']

Sample Usage​

from litellm import completion
import os

os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
    model="cerebras/meta/llama3-70b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)
print(response)

Sample Usage - Streaming​

from litellm import completion
import os

os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
    model="cerebras/meta/llama3-70b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    stream=True,
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)

for chunk in response:
    print(chunk)

Usage with LiteLLM Proxy Server​

Here's how to call a Cerebras model with the LiteLLM Proxy Server

  1. Modify the config.yaml 

    model_list:
      - model_name: my-model
        litellm_params:
          model: cerebras/<your-model-name>  # add cerebras/ prefix to route as Cerebras provider
          api_key: api-key                 # api key to send your model

  1. Start the proxy 

    $ litellm --config /path/to/config.yaml

  2. Send Request to LiteLLM Proxy Server

    import openai
    client = openai.OpenAI(
        api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
        base_url="http://0.0.0.0:4000" # litellm-proxy-base url
    )

    response = client.chat.completions.create(
        model="my-model",
        messages = [
            {
                "role": "user",
                "content": "what llm are you"
            }
        ],
    )

    print(response)