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Azure AI Studio

Using Mistral models deployed on Azure AI Studio​

Sample Usage - setting env vars​

Set MISTRAL_AZURE_API_KEY and MISTRAL_AZURE_API_BASE in your env

MISTRAL_AZURE_API_KEY = "zE************""
MISTRAL_AZURE_API_BASE = "https://Mistral-large-nmefg-serverless.eastus2.inference.ai.azure.com/v1"
from litellm import completion
import os

response = completion(
    model="mistral/Mistral-large-dfgfj", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - passing api_base and api_key to litellm.completion​

from litellm import completion
import os

response = completion(
    model="mistral/Mistral-large-dfgfj", 
    api_base="https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com",
    api_key = "JGbKodRcTp****"
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

[LiteLLM Proxy] Using Mistral Models​

Set this on your litellm proxy config.yaml

model_list:
  - model_name: mistral
    litellm_params:
      model: mistral/Mistral-large-dfgfj
      api_base: https://Mistral-large-dfgfj-serverless.eastus2.inference.ai.azure.com
      api_key: JGbKodRcTp****