--- title: "Meta Llama" url: "/docs/providers/meta_llama" canonical_url: "https://docs.litellm.ai/docs/providers/meta_llama" type: "docs" last_updated: "2026-10-08" related: - "/docs/providers/meta" - "/docs/providers/milvus_vector_stores" --- # Meta Llama > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt | 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 ↗](https://llama.developer.meta.com?utm_source=partner-litellm&utm_medium=website) | ## 必要變數 {#required-variables} ```python showLineNumbers title="Environment Variables" os.environ["LLAMA_API_KEY"] = "" # your Meta Llama API key ``` ## 支援的模型 {#supported-models} :::info 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](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json)。 ::: | 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-FP8` | 128k | 4028 | 文字、圖片 | 文字 | | `Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | 文字、圖片 | 文字 | | `Llama-3.3-70B-Instruct` | 128k | 4028 | 文字 | 文字 | | `Llama-3.3-8B-Instruct` | 128k | 4028 | 文字 | 文字 | ## 使用方式 - LiteLLM Python SDK {#usage---litellm-python-sdk} ### 非串流 {#non-streaming} ```python showLineNumbers title="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 {#streaming} ```python showLineNumbers title="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 {#function-calling} ```python showLineNumbers title="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 {#tool-use} ```python showLineNumbers title="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 {#usage---litellm-proxy} 請將下列內容新增至您的 LiteLLM Proxy 設定檔： ```yaml showLineNumbers title="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： ```bash showLineNumbers title="Start LiteLLM Proxy" litellm --config config.yaml # RUNNING on http://0.0.0.0:4000 ``` **OpenAI SDK** ```python showLineNumbers title="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) ``` ```python showLineNumbers title="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="") ``` **LiteLLM SDK** ```python showLineNumbers title="Meta Llama via Proxy - LiteLLM SDK" import litellm # Configure LiteLLM to use your proxy response = litellm.completion( model="litellm_proxy/meta_llama/Llama-3.3-70B-Instruct", 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) ``` ```python showLineNumbers title="Meta Llama via Proxy - LiteLLM SDK Streaming" import litellm # Configure LiteLLM to use your proxy with streaming response = litellm.completion( model="litellm_proxy/meta_llama/Llama-3.3-70B-Instruct", messages=[{"role": "user", "content": "Write a short poem about AI."}], api_base="http://localhost:4000", api_key="your-proxy-api-key", stream=True ) for chunk in response: if hasattr(chunk.choices[0], 'delta') and chunk.choices[0].delta.content is not None: print(chunk.choices[0].delta.content, end="") ``` **cURL** ```bash showLineNumbers title="Meta Llama via Proxy - cURL" curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer your-proxy-api-key" \ -d '{ "model": "meta_llama/Llama-3.3-70B-Instruct", "messages": [{"role": "user", "content": "Write a short poem about AI."}] }' ``` ```bash showLineNumbers title="Meta Llama via Proxy - cURL Streaming" curl http://localhost:4000/v1/chat/completions \ -H "Content-Type: application/json" \ -H "Authorization: Bearer your-proxy-api-key" \ -d '{ "model": "meta_llama/Llama-3.3-70B-Instruct", "messages": [{"role": "user", "content": "Write a short poem about AI."}], "stream": true }' ``` 如需關於使用 LiteLLM Proxy 的更詳細資訊，請參閱 [LiteLLM Proxy 文件](../providers/litellm_proxy)。 ## Related pages - [Meta 模型 API](https://docs.litellm.ai/docs/providers/meta.md) - [Milvus - 向量儲存](https://docs.litellm.ai/docs/providers/milvus_vector_stores.md)