Crusoe

概覽

屬性	詳細資訊
說明	Crusoe Cloud 為開源大型語言模型提供 GPU 加速推論，並針對效能與成本效率進行最佳化。
LiteLLM 提供者路由	crusoe/
提供者文件連結	Crusoe Managed Inference 文件 ↗
Base URL	https://managed-inference-api-proxy.crusoecloud.com/v1
支援的操作	/chat/completions

我們支援所有 Crusoe 模型，只要在送出 completion 請求時將 crusoe/ 設為前綴

可用模型

模型	說明	上下文視窗
crusoe/deepseek-ai/DeepSeek-R1-0528	DeepSeek R1 推理模型（2025 年 5 月）	163,840 tokens
crusoe/deepseek-ai/DeepSeek-V3-0324	DeepSeek V3 聊天模型（2025 年 3 月）	163,840 tokens
crusoe/google/gemma-3-12b-it	Google Gemma 3 12B 指令微調	131,072 tokens
crusoe/meta-llama/Llama-3.3-70B-Instruct	Llama 3.3 70B 指令微調	131,072 tokens
crusoe/moonshotai/Kimi-K2-Thinking	Kimi K2 延伸思考模型	262,144 tokens
crusoe/openai/gpt-oss-120b	OpenAI 120B 開源模型	131,072 tokens
crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507	Qwen3 235B MoE 指令微調	262,144 tokens

必要變數

Environment Variables

os.environ["CRUSOE_API_KEY"] = ""  # your Crusoe API key

用法 - LiteLLM Python SDK

非串流

Crusoe Non-streaming Completion

import os
import litellm
from litellm import completion

os.environ["CRUSOE_API_KEY"] = ""  # your Crusoe API key

messages = [{"content": "Hello, how are you?", "role": "user"}]

# Crusoe call
response = completion(
    model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
    messages=messages
)

print(response)

串流

Crusoe Streaming Completion

import os
import litellm
from litellm import completion

os.environ["CRUSOE_API_KEY"] = ""  # your Crusoe API key

messages = [{"content": "Write a short story about AI", "role": "user"}]

# Crusoe call with streaming
response = completion(
    model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

函式呼叫

Crusoe Function Calling

import os
import litellm
from litellm import completion

os.environ["CRUSOE_API_KEY"] = ""  # your Crusoe API key

tools = [{
    "type": "function",
    "function": {
        "name": "get_weather",
        "description": "Get the current weather in a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                }
            },
            "required": ["location"]
        }
    }
}]

messages = [{"role": "user", "content": "What's the weather in Boston?"}]

response = completion(
    model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response)

用法 - LiteLLM Proxy Server

config.yaml

model_list:
  - model_name: llama-3.3-70b
    litellm_params:
      model: crusoe/meta-llama/Llama-3.3-70B-Instruct
      api_key: os.environ/CRUSOE_API_KEY
  - model_name: deepseek-r1
    litellm_params:
      model: crusoe/deepseek-ai/DeepSeek-R1-0528
      api_key: os.environ/CRUSOE_API_KEY
  - model_name: deepseek-v3
    litellm_params:
      model: crusoe/deepseek-ai/DeepSeek-V3-0324
      api_key: os.environ/CRUSOE_API_KEY
  - model_name: qwen3-235b
    litellm_params:
      model: crusoe/Qwen/Qwen3-235B-A22B-Instruct-2507
      api_key: os.environ/CRUSOE_API_KEY
  - model_name: kimi-k2
    litellm_params:
      model: crusoe/moonshotai/Kimi-K2-Thinking
      api_key: os.environ/CRUSOE_API_KEY

自訂 API Base

選項 1：環境變數

Custom API Base via env var

import os
from litellm import completion

os.environ["CRUSOE_API_BASE"] = "https://custom.crusoecloud.com/v1"
os.environ["CRUSOE_API_KEY"] = ""  # your API key

response = completion(
    model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
    messages=[{"content": "Hello!", "role": "user"}],
)

選項 2：直接傳入

Custom API Base via parameter

from litellm import completion

response = completion(
    model="crusoe/meta-llama/Llama-3.3-70B-Instruct",
    messages=[{"content": "Hello!", "role": "user"}],
    api_base="https://custom.crusoecloud.com/v1",
    api_key="your-api-key",
)

支援的 OpenAI 參數

• temperature
• max_tokens
• max_completion_tokens
• top_p
• frequency_penalty
• presence_penalty
• stop
• n
• stream
• tools
• tool_choice
• response_format
• seed
• user
• logit_bias
• logprobs
• top_logprobs