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

os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
    model="cerebras/llama3-70b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit? (Write in JSON)",
        }
    ],
    max_tokens=10,
        
    # The prompt should include JSON if 'json_object' is selected; otherwise, you will get error code 400.
    response_format={ "type": "json_object" },
    seed=123,
    stop=["\
\
"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)
print(response)

Sample Usage - Streaming

from litellm import completion

os.environ['CEREBRAS_API_KEY'] = ""
response = completion(
    model="cerebras/llama3-70b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit? (Write in JSON)",
        }
    ],
    stream=True,
    max_tokens=10,

    # The prompt should include JSON if 'json_object' is selected; otherwise, you will get error code 400.
    response_format={ "type": "json_object" }, 
    seed=123,
    stop=["\
\
"],
    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

2. Start the proxy

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

3. Send Request to LiteLLM Proxy Server

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)

curl --location 'http://0.0.0.0:4000/chat/completions' \\
    --header 'Authorization: Bearer sk-1234' \\
    --header 'Content-Type: application/json' \\
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'