LM Studio

https://lmstudio.ai/docs/basics/server

提示

我們支援所有 LM Studio 模型，送出 litellm 請求時只需將 model=lm_studio/<any-model-on-lmstudio> 設為前綴

屬性	詳細資訊
說明	探索、下載並執行本機 LLM。
LiteLLM 上的提供者路由	lm_studio/
提供者文件	LM Studio ↗
支援的 OpenAI 端點	/chat/completions, /embeddings, /completions

API 金鑰

# env variable
os.environ['LM_STUDIO_API_BASE']
os.environ['LM_STUDIO_API_KEY'] # optional, default is empty

範例用法

from litellm import completion
import os

os.environ['LM_STUDIO_API_BASE'] = ""

response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ]
)
print(response)

範例用法 - 串流

from litellm import completion
import os

os.environ['LM_STUDIO_API_KEY'] = ""
response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[
        {
            "role": "user",
            "content": "What's the weather like in Boston today in Fahrenheit?",
        }
    ],
    stream=True,
)

for chunk in response:
    print(chunk)

與 LiteLLM Proxy Server 搭配使用

以下說明如何使用 LiteLLM Proxy Server 呼叫 LM Studio 模型

1. 修改 config.yaml

model_list:
  - model_name: my-model
    litellm_params:
      model: lm_studio/<your-model-name>  # add lm_studio/ prefix to route as LM Studio provider
      api_key: api-key                 # api key to send your model

2. 啟動 proxy

$ litellm --config /path/to/config.yaml

3. 向 LiteLLM Proxy Server 送出請求

OpenAI Python v1.0.0+

import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="my-model",
    messages = [
        {
            "role": "user",
            "content": "what llm are you"
        }
    ],
)

print(response)

curl

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "my-model",
    "messages": [
        {
        "role": "user",
        "content": "what llm are you"
        }
    ],
}'

支援的參數

請參閱支援的參數。

嵌入

from litellm import embedding
import os 

os.environ['LM_STUDIO_API_BASE'] = "http://localhost:8000"
response = embedding(
    model="lm_studio/jina-embeddings-v3",
    input=["Hello world"],
)
print(response)

結構化輸出

LM Studio 透過 JSON Schema 支援結構化輸出。您可以使用 response_format 傳入 pydantic 模型或原始 schema。 LiteLLM 會將 schema 以 { "type": "json_schema", "json_schema": {"schema": <your schema>} } 送出。

from pydantic import BaseModel
from litellm import completion

class Book(BaseModel):
    title: str
    author: str
    year: int

response = completion(
    model="lm_studio/llama-3-8b-instruct",
    messages=[{"role": "user", "content": "Tell me about The Hobbit"}],
    response_format=Book,
)
print(response.choices[0].message.content)