Cerebras

Cerebras

Cerebras의 모든 모델을 지원해요. litellm 요청 시 model=cerebras/<any-model-on-cerebras> 접두사로 설정하기만 하면 돼요.

출처: 문서

본문

API 키

# env variable
os.environ['CEREBRAS_API_KEY']

샘플 사용법 (Sample Usage)

from litellm import completion
import os

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=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)
print(response)

샘플 사용법 - 스트리밍 (Streaming)

from litellm import completion
import os

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=["\n\n"],
    temperature=0.2,
    top_p=0.9,
    tool_choice="auto",
    tools=[],
    user="user",
)

for chunk in response:
    print(chunk)

Proxy 사용법

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

Proxy 시작:

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

OpenAI 호출:

import openai

client = openai.OpenAI(
    api_key="sk-<your-litellm-api-key>",  # 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:

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

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