​
Installation

1

Install the latest version of LLMstudio using pip

We suggest that you create and activate a new environment using conda
pip install llmstudio
2

Install bun if you want to use the UI

curl -fsSL https://bun.sh/install | bash
3

Create a .env file at the same path you’ll run LLMstudio

OPENAI_API_KEY="sk-api_key"
ANTHROPIC_API_KEY="sk-api_key"
4

Now you should be able to run LLMstudio using the following command.

llmstudio server --ui

When the --ui flag is set, you’ll be able to access the UI at http://localhost:3000

You are done setting up LLMstudio!

​
Python Client

Using it in a Python notebook is also fairly simple! Just run the following cell:

from llmstudio.providers import LLM
model = LLM("anthropic/claude-2.1")
model.chat("What are Large Language Models?")

The output will be a ChatCompletion, using the OpenAI format.

ChatCompletion(
    id='9faa33e3-cda0-4d23-9217-c82ff7325b94',
    choices=[
        Choice(
            finish_reason='stop',
            index=0,
            logprobs=None,
            message=ChatCompletionMessage(
                content="I am an artificial intelligence called OpenAI. I don't have a personal name because I am not a human.",
                role='assistant',
                function_call=None,
                tool_calls=None
            )
        )
    ],
    created=1718623278,
    model='gpt-4',
    object='chat.completion',
    system_fingerprint=None,
    usage=None,
    session_id=None,
    chat_input="What's your name",
    chat_output="I am an artificial intelligence called OpenAI. I don't have a personal name because I am not a human.",
    context=[{'role': 'user', 'content': "What's your name"}],
    provider='openai',
    timestamp=1718623280.9204,
    parameters={'temperature': None, 'max_tokens': None, 'top_p': None, 'frequency_penalty': 0, 'presence_penalty': 0},
    metrics={
        'input_tokens': 4,
        'output_tokens': 23,
        'total_tokens': 27,
        'cost_usd': 0.0015,
        'latency_s': 2.6993088722229004,
        'time_to_first_token_s': 0.9924077987670898,
        'inter_token_latency_s': 0.0711073378721873,
        'tokens_per_second': 9.26162998879499
    }
)

​
Installation

1

Install the latest version of LLMstudio using pip

We suggest that you create and activate a new environment using conda
pip install llmstudio
2

Install bun if you want to use the UI

curl -fsSL https://bun.sh/install | bash
3

Create a .env file at the same path you’ll run LLMstudio

OPENAI_API_KEY="sk-api_key"
ANTHROPIC_API_KEY="sk-api_key"
4

Now you should be able to run LLMstudio using the following command.

llmstudio server --ui

When the --ui flag is set, you’ll be able to access the UI at http://localhost:3000

You are done setting up LLMstudio!

​
Python Client

Using it in a Python notebook is also fairly simple! Just run the following cell:

from llmstudio.providers import LLM
model = LLM("anthropic/claude-2.1")
model.chat("What are Large Language Models?")

The output will be a ChatCompletion, using the OpenAI format.

ChatCompletion(
    id='9faa33e3-cda0-4d23-9217-c82ff7325b94',
    choices=[
        Choice(
            finish_reason='stop',
            index=0,
            logprobs=None,
            message=ChatCompletionMessage(
                content="I am an artificial intelligence called OpenAI. I don't have a personal name because I am not a human.",
                role='assistant',
                function_call=None,
                tool_calls=None
            )
        )
    ],
    created=1718623278,
    model='gpt-4',
    object='chat.completion',
    system_fingerprint=None,
    usage=None,
    session_id=None,
    chat_input="What's your name",
    chat_output="I am an artificial intelligence called OpenAI. I don't have a personal name because I am not a human.",
    context=[{'role': 'user', 'content': "What's your name"}],
    provider='openai',
    timestamp=1718623280.9204,
    parameters={'temperature': None, 'max_tokens': None, 'top_p': None, 'frequency_penalty': 0, 'presence_penalty': 0},
    metrics={
        'input_tokens': 4,
        'output_tokens': 23,
        'total_tokens': 27,
        'cost_usd': 0.0015,
        'latency_s': 2.6993088722229004,
        'time_to_first_token_s': 0.9924077987670898,
        'inter_token_latency_s': 0.0711073378721873,
        'tokens_per_second': 9.26162998879499
    }
)