--- title: "Meta Llama" description: "import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';" type: skill canonical_url: https://claudary.paisolsolutions.com/skills/meta-llama source: "Claudary" difficulty: intermediate author: "Claude Code Knowledge Pack" date: 2026-07-10T11:31:00.459Z license: CC-BY-4.0 attribution: "Meta Llama — Claudary (https://claudary.paisolsolutions.com/skills/meta-llama)" --- # Meta Llama import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; ## Overview import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem'; # Meta Llama | Property | Details | |-------|-------| | Description | Meta's Llama API provides access to Meta's family of large language models. | | Provider Route on LiteLLM | `meta_llama/` | | Supported Endpoints | `/chat/completions`, `/completions`, `/responses` | | API Reference | [Llama API Reference ↗](https://llama.developer.meta.com?utm_source=partner-litellm&utm_medium=website) | ## Required Variables ```python showLineNumbers title="Environment Variables" os.environ["LLAMA_API_KEY"] = "" # your Meta Llama API key ``` ## Supported Models :::info All models listed here https://llama.developer.meta.com/docs/models/ are supported. We actively maintain the list of models, token window, etc. [here](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json). ::: | Model ID | Input context length | Output context length | Input Modalities | Output Modalities | | --- | --- | --- | --- | --- | | `Llama-4-Scout-17B-16E-Instruct-FP8` | 128k | 4028 | Text, Image | Text | | `Llama-4-Maverick-17B-128E-Instruct-FP8` | 128k | 4028 | Text, Image | Text | | `Llama-3.3-70B-Instruct` | 128k | 4028 | Text | Text | | `Llama-3.3-8B-Instruct` | 128k | 4028 | Text | Text | ## Usage - LiteLLM Python SDK ### 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) ``` ### 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) ``` ### 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) ``` ### 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) ``` ## Usage - LiteLLM Proxy Add the following to your LiteLLM Proxy configuration file: ```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 ``` Start your LiteLLM Proxy server: ```bash showLineNumbers title="Start LiteLLM Proxy" litellm --config config.yaml # RUNNING on http://0.0.0.0:4000 ``` ```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="") ``` ```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="") ``` ```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 }' ``` For more detailed information on using the LiteLLM Proxy, see the [LiteLLM Proxy documentation](../providers/litellm_proxy). --- Source: [Claudary](https://claudary.paisolsolutions.com/skills/meta-llama) · https://claudary.paisolsolutions.com