Meta 模型 API
| 屬性 | 詳細資訊 |
|---|---|
| 說明 | Meta 的 Model API 可存取 Meta 的 Muse Spark 系列推理模型。 |
| LiteLLM 上的提供者路由 | meta/ |
| 支援的端點 | /chat/completions, /responses, /v1/messages |
| API 參考 | Meta Model API 參考 ↗ |
必要變數
Environment Variables
os.environ["META_API_KEY"] = "" # your Meta Model API key
請求預設會送至 https://api.meta.ai/v1。設定 META_API_BASE 以覆寫 API 基底位址。
支援的模型
資訊
我們會持續維護模型、價格、token 視窗等清單。請見此處。
| 模型 ID | 輸入上下文長度 | 輸入多模態 | 輸出多模態 |
|---|---|---|---|
muse-spark-1.1 | 1M | 文字、圖片、影片、PDF | 文字 |
muse-spark-1.1 支援 function calling、parallel function calling、structured outputs、prompt caching、web search grounding，以及透過 reasoning_effort（"minimal" 到 "xhigh"）進行 reasoning。
此 API 也原生公開 Anthropic Messages 格式，因此 LiteLLM 會將 /v1/messages 請求未經翻譯地轉送至 https://api.meta.ai/v1/messages，並保留 Anthropic 專屬功能，例如 thinking blocks。
使用方式 - LiteLLM Python SDK
非串流
Meta Model API Non-streaming Completion
import os
import litellm
from litellm import completion
os.environ["META_API_KEY"] = "" # your Meta Model API key
messages = [{"content": "Hello, how are you?", "role": "user"}]
response = completion(model="meta/muse-spark-1.1", messages=messages)
串流
Meta Model API Streaming Completion
import os
import litellm
from litellm import completion
os.environ["META_API_KEY"] = "" # your Meta Model API key
messages = [{"content": "Hello, how are you?", "role": "user"}]
response = completion(
model="meta/muse-spark-1.1",
messages=messages,
stream=True
)
for chunk in response:
print(chunk)
推理努力程度
muse-spark-1.1 接受 reasoning_effort 值 "minimal"、"low"、"medium"、"high"，以及 "xhigh"。
Meta Model API Reasoning Effort
import os
import litellm
from litellm import completion
os.environ["META_API_KEY"] = "" # your Meta Model API key
messages = [{"content": "What is 15% of 2840?", "role": "user"}]
response = completion(
model="meta/muse-spark-1.1",
messages=messages,
reasoning_effort="xhigh"
)
print(response.choices[0].message.content)
print(response.usage.completion_tokens_details.reasoning_tokens)
函式呼叫
Meta Model API Function Calling
import os
import litellm
from litellm import completion
os.environ["META_API_KEY"] = "" # your Meta Model API key
messages = [{"content": "What's the weather like in San Francisco?", "role": "user"}]
tools = [
{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA"
},
"unit": {
"type": "string",
"enum": ["celsius", "fahrenheit"]
}
},
"required": ["location"]
}
}
}
]
response = completion(
model="meta/muse-spark-1.1",
messages=messages,
tools=tools,
tool_choice="auto"
)
print(response.choices[0].message.tool_calls)
使用方式 - LiteLLM Proxy
請將以下內容加入您的 LiteLLM Proxy 設定文件：
config.yaml
model_list:
- model_name: muse-spark-1.1
litellm_params:
model: meta/muse-spark-1.1
api_key: os.environ/META_API_KEY
啟動您的 LiteLLM Proxy 伺服器：
Start LiteLLM Proxy
litellm --config config.yaml
# RUNNING on http://0.0.0.0:4000
- OpenAI SDK
- LiteLLM SDK
- cURL
Meta Model API via Proxy - Non-streaming
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-proxy-api-key" # Your proxy API key
)
response = client.chat.completions.create(
model="muse-spark-1.1",
messages=[{"role": "user", "content": "Write a short poem about AI."}],
reasoning_effort="minimal"
)
print(response.choices[0].message.content)
Meta Model API via Proxy - Streaming
from openai import OpenAI
client = OpenAI(
base_url="http://localhost:4000", # Your proxy URL
api_key="your-proxy-api-key" # Your proxy API key
)
response = client.chat.completions.create(
model="muse-spark-1.1",
messages=[{"role": "user", "content": "Write a short poem about AI."}],
stream=True
)
for chunk in response:
if chunk.choices[0].delta.content is not None:
print(chunk.choices[0].delta.content, end="")
Meta Model API via Proxy - LiteLLM SDK
import litellm
response = litellm.completion(
model="litellm_proxy/muse-spark-1.1",
messages=[{"role": "user", "content": "Write a short poem about AI."}],
api_base="http://localhost:4000",
api_key="your-proxy-api-key"
)
print(response.choices[0].message.content)
Meta Model API via Proxy - cURL
curl http://localhost:4000/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-proxy-api-key" \
-d '{
"model": "muse-spark-1.1",
"messages": [{"role": "user", "content": "Write a short poem about AI."}],
"reasoning_effort": "minimal"
}'
Anthropic 訊息 API
Proxy 的 /v1/messages 路由會將 meta/ 模型的請求轉送至 Meta 原生相容 Anthropic 的端點，且不經翻譯。
Meta Model API via Proxy - /v1/messages
curl http://localhost:4000/v1/messages \
-H "Content-Type: application/json" \
-H "Authorization: Bearer your-proxy-api-key" \
-d '{
"model": "muse-spark-1.1",
"max_tokens": 2048,
"messages": [{"role": "user", "content": "Write a short poem about AI."}]
}'