litellm/docs/my-website/docs/providers/deepinfra.md

DeepInfra

https://deepinfra.com/

:::tip

We support ALL DeepInfra models, just set model=deepinfra/<any-model-on-deepinfra> as a prefix when sending litellm requests

:::

API Key

# env variable
os.environ['DEEPINFRA_API_KEY']

Sample Usage

from litellm import completion
import os

os.environ['DEEPINFRA_API_KEY'] = ""
response = completion(
    model="deepinfra/meta-llama/Llama-2-70b-chat-hf", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}]
)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['DEEPINFRA_API_KEY'] = ""
response = completion(
    model="deepinfra/meta-llama/Llama-2-70b-chat-hf", 
    messages=[{"role": "user", "content": "write code for saying hi from LiteLLM"}],
    stream=True
)

for chunk in response:
    print(chunk)

Chat Models

Model Name	Function Call
meta-llama/Meta-Llama-3-8B-Instruct	completion(model="deepinfra/meta-llama/Meta-Llama-3-8B-Instruct", messages)
meta-llama/Meta-Llama-3-70B-Instruct	completion(model="deepinfra/meta-llama/Meta-Llama-3-70B-Instruct", messages)
meta-llama/Llama-2-70b-chat-hf	completion(model="deepinfra/meta-llama/Llama-2-70b-chat-hf", messages)
meta-llama/Llama-2-7b-chat-hf	completion(model="deepinfra/meta-llama/Llama-2-7b-chat-hf", messages)
meta-llama/Llama-2-13b-chat-hf	completion(model="deepinfra/meta-llama/Llama-2-13b-chat-hf", messages)
codellama/CodeLlama-34b-Instruct-hf	completion(model="deepinfra/codellama/CodeLlama-34b-Instruct-hf", messages)
mistralai/Mistral-7B-Instruct-v0.1	completion(model="deepinfra/mistralai/Mistral-7B-Instruct-v0.1", messages)
jondurbin/airoboros-l2-70b-gpt4-1.4.1	completion(model="deepinfra/jondurbin/airoboros-l2-70b-gpt4-1.4.1", messages)