Getting started

# create a virtual environment
python3 -m venv .venv
# activate environment in current shell
. .venv/bin/activate
# install openai python client
pip install openai
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Choose a model

• meta-llama/Llama-2-70b-chat-hf
• meta-llama/Llama-2-7b-chat-hf
• codellama/CodeLlama-34b-Instruct-hf

Run OpenAI chat.completion

import openai

stream = True # or False

# Point OpenAI client to our endpoint
openai.api_key = "<YOUR DEEPINFRA API KEY>"
openai.api_base = "https://api.deepinfra.com/v1/openai"

# Your chosen model here
MODEL_DI = "meta-llama/Llama-2-70b-chat-hf"
chat_completion = openai.ChatCompletion.create(
    model=MODEL_DI,
    messages=[{"role": "user", "content": "Hello world"}],
    stream=stream,
    max_tokens=100,
    # top_p=0.5,
)

if stream:
    # print the chat completion
    for event in chat_completion:
        print(event.choices)
else:
    print(chat_completion.choices[0].message.content)
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Note that both streaming and batch mode are supported.

Existing OpenAI integration

If you're already using OpenAI chat completion in your project, you need to change the api_key, api_base and model params:

import openai

# set these before running any completions
openai.api_key = "YOUR DEEPINFRA TOKEN"
openai.api_base = "https://api.deepinfra.com/v1/openai"

openai.ChatCompletion.create(
    model="CHOSEN MODEL HERE",
    # ...
)
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Pricing

Our OpenAI API compatible models are priced on token output (just like OpenAI). Our current price is $1 / 1M tokens.

Docs

Check the docs for more in-depth information and examples openai api.