Github

Github

GitHub의 모든 모델을 지원해요. litellm 요청 시 model=github/<any-model-on-github> 접두사로 설정하기만 하면 돼요. 회사 접두사는 무시해요: meta/Llama-3.2-11B-Vision-Instruct는 model=github/Llama-3.2-11B-Vision-Instruct가 돼요.

출처: 문서

본문

API 키

# env variable
os.environ['GITHUB_API_KEY']

샘플 사용법 (Sample Usage)

from litellm import completion
import os

os.environ['GITHUB_API_KEY'] = ""

response = completion(
    model="github/Llama-3.2-11B-Vision-Instruct",
    messages=[
        {"role": "user", "content": "hello from litellm"}
    ],
)
print(response)

샘플 사용법 - 스트리밍 (Streaming)

from litellm import completion
import os

os.environ['GITHUB_API_KEY'] = ""

response = completion(
    model="github/Llama-3.2-11B-Vision-Instruct",
    messages=[
        {"role": "user", "content": "hello from litellm"}
    ],
    stream=True
)

for chunk in response:
    print(chunk)

LiteLLM Proxy 사용법

1. config.yaml에 Github 모델 설정

model_list:
  - model_name: github-Llama-3.2-11B-Vision-Instruct  # Model Alias to use for requests
    litellm_params:
      model: github/Llama-3.2-11B-Vision-Instruct
      api_key: "os.environ/GITHUB_API_KEY"  # ensure you have `GITHUB_API_KEY` in your .env

2. Proxy 시작

litellm --config config.yaml

3. 테스트

curl:

curl --location 'http://0.0.0.0:4000/chat/completions' \
  --header 'Content-Type: application/json' \
  --data ' {
    "model": "github-Llama-3.2-11B-Vision-Instruct",
    "messages": [
      {
        "role": "user",
        "content": "what llm are you"
      }
    ]
  }'

OpenAI v1.0.0+:

import openai

client = openai.OpenAI(
    api_key="anything",
    base_url="http://0.0.0.0:4000"
)

response = client.chat.completions.create(
    model="github-Llama-3.2-11B-Vision-Instruct",
    messages = [
        {
            "role": "user",
            "content": "this is a test request, write a short poem"
        }
    ]
)
print(response)

Langchain:

from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage

chat = ChatOpenAI(
    openai_api_base="http://0.0.0.0:4000",  # set openai_api_base to the LiteLLM Proxy
    model = "github-Llama-3.2-11B-Vision-Instruct",
    temperature=0.1
)

messages = [
    SystemMessage(
        content="You are a helpful assistant that im using to make a test request to."
    ),
    HumanMessage(
        content="test from litellm. tell me why it's amazing in 1 sentence"
    ),
]
response = chat(messages)
print(response)

지원 모델 - 모든 Github 모델 지원!

Function Calling

# Example dummy function hard coded to return the current weather
import json

def get_current_weather(location, unit="fahrenheit"):
    """Get the current weather in a given location"""
    if "tokyo" in location.lower():
        return json.dumps({"location": "Tokyo", "temperature": "10", "unit": "celsius"})
    elif "san francisco" in location.lower():
        return json.dumps(
            {"location": "San Francisco", "temperature": "72", "unit": "fahrenheit"}
        )
    elif "paris" in location.lower():
        return json.dumps({"location": "Paris", "temperature": "22", "unit": "celsius"})
    else:
        return json.dumps({"location": location, "temperature": "unknown"})

# Step 1: send the conversation and available functions to the model
messages = [
    {
        "role": "system",
        "content": "You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.",
    },
    {
        "role": "user",
        "content": "What's the weather like in San Francisco?",
    },
]
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_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 = litellm.completion(
    model="github/Llama-3.2-11B-Vision-Instruct",
    messages=messages,
    tools=tools,
    tool_choice="auto",  # auto is default, but we'll be explicit
)
print("Response\n", response)

response_message = response.choices[0].message
tool_calls = response_message.tool_calls

# Step 2: check if the model wanted to call a function
if tool_calls:
    # Step 3: call the function
    # Note: the JSON response may not always be valid; be sure to handle errors
    available_functions = {
        "get_current_weather": get_current_weather,
    }
    messages.append(response_message)  # extend conversation with assistant's reply
    # ... (계속: 각 tool call에 대해 함수 실행하고 결과를 messages에 추가)

더 알아보기 (Learn more)

• LiteLLM 개요
• LiteLLM Python SDK 사용법
• LiteLLM Proxy Server 설정
• 지원되는 공급업체 전체 목록