跳至主要內容

Meta Llama

屬性詳細資訊
說明Meta 的 Llama API 提供對 Meta 大型語言模型系列的存取。
LiteLLM 上的提供者路由meta_llama/
支援的端點/chat/completions, /completions, /responses
API 參考Llama API 參考 ↗

必要變數​

Environment Variables
os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

支援的模型​

資訊

此處列出的所有模型 https://llama.developer.meta.com/docs/models/ 都受支援。我們積極維護模型清單、token 視窗等資訊。在這裡。

模型 ID輸入上下文長度輸出上下文長度輸入模態輸出模態
Llama-4-Scout-17B-16E-Instruct-FP8128k4028文字、圖片文字
Llama-4-Maverick-17B-128E-Instruct-FP8128k4028文字、圖片文字
Llama-3.3-70B-Instruct128k4028文字文字
Llama-3.3-8B-Instruct128k4028文字文字

使用方式 - LiteLLM Python SDK​

非串流​

Meta Llama Non-streaming Completion
import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

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

# Meta Llama call
response = completion(model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8", messages=messages)

串流​

Meta Llama Streaming Completion
import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

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

# Meta Llama call with streaming
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    stream=True
)

for chunk in response:
    print(chunk)

函式呼叫​

Meta Llama Function Calling
import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

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

# Define the function
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"]
            }
        }
    }
]

# Meta Llama call with function calling
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.tool_calls)

工具使用​

Meta Llama Tool Use
import os
import litellm
from litellm import completion

os.environ["LLAMA_API_KEY"] = ""  # your Meta Llama API key

messages = [{"content": "Create a chart showing the population growth of New York City from 2010 to 2020", "role": "user"}]

# Define the tools
tools = [
    {
        "type": "function",
        "function": {
            "name": "create_chart",
            "description": "Create a chart with the provided data",
            "parameters": {
                "type": "object",
                "properties": {
                    "chart_type": {
                        "type": "string",
                        "enum": ["bar", "line", "pie", "scatter"],
                        "description": "The type of chart to create"
                    },
                    "title": {
                        "type": "string",
                        "description": "The title of the chart"
                    },
                    "data": {
                        "type": "object",
                        "description": "The data to plot in the chart"
                    }
                },
                "required": ["chart_type", "title", "data"]
            }
        }
    }
]

# Meta Llama call with tool use
response = completion(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=messages,
    tools=tools,
    tool_choice="auto"
)

print(response.choices[0].message.content)

使用方式 - LiteLLM Proxy​

請將下列內容新增至您的 LiteLLM Proxy 設定檔：

config.yaml
model_list:
  - model_name: meta_llama/Llama-3.3-70B-Instruct
    litellm_params:
      model: meta_llama/Llama-3.3-70B-Instruct
      api_key: os.environ/LLAMA_API_KEY

  - model_name: meta_llama/Llama-3.3-8B-Instruct
    litellm_params:
      model: meta_llama/Llama-3.3-8B-Instruct
      api_key: os.environ/LLAMA_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
Meta Llama via Proxy - Non-streaming
from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Non-streaming response
response = client.chat.completions.create(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    messages=[{"role": "user", "content": "Write a short poem about AI."}]
)

print(response.choices[0].message.content)
Meta Llama via Proxy - Streaming
from openai import OpenAI

# Initialize client with your proxy URL
client = OpenAI(
    base_url="http://localhost:4000",  # Your proxy URL
    api_key="your-proxy-api-key"       # Your proxy API key
)

# Streaming response
response = client.chat.completions.create(
    model="meta_llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    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="")

如需關於使用 LiteLLM Proxy 的更詳細資訊，請參閱 LiteLLM Proxy 文件。

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