Cerebras

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

提示

我們支援所有 Cerebras 模型，只要在傳送 litellm 請求時將 model=cerebras/<any-model-on-cerebras> 設為前綴即可

API 金鑰

# env variable
os.environ['CEREBRAS_API_KEY']

範例用法

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)

範例用法 - 串流

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)

搭配 LiteLLM Proxy Server 使用

以下是如何透過 LiteLLM Proxy Server 呼叫 Cerebras 模型

1. 修改 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. 啟動 proxy

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

3. 向 LiteLLM Proxy Server 傳送請求

OpenAI Python v1.0.0+

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)

curl

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"
        }
    ],
}'