🦾 OpenLLM

An open platform for operating large language models (LLMs) in production.
Fine-tune, serve, deploy, and monitor any LLMs with ease.

📖 Introduction

With OpenLLM, you can run inference with any open-source large-language models, deploy to the cloud or on-premises, and build powerful AI apps.

🚂 State-of-the-art LLMs: built-in supports a wide range of open-source LLMs and model runtime, including StableLM, Falcon, Dolly, Flan-T5, ChatGLM, StarCoder and more.

🔥 Flexible APIs: serve LLMs over RESTful API or gRPC with one command, query via WebUI, CLI, our Python/Javascript client, or any HTTP client.

⛓️ Freedom To Build: First-class support for LangChain, BentoML and HuggingFace that allows you to easily create your own AI apps by composing LLMs with other models and services.

🎯 Streamline Deployment: Automatically generate your LLM server Docker Images or deploy as serverless endpoint via ☁️ BentoCloud.

🤖️ Bring your own LLM: Fine-tune any LLM to suit your needs with LLM.tuning(). (Coming soon)

🏃‍ Getting Started

To use OpenLLM, you need to have Python 3.8 (or newer) and pip installed on your system. We highly recommend using a Virtual Environment to prevent package conflicts.

You can install OpenLLM using pip as follows:

pip install openllm

To verify if it's installed correctly, run:

$ openllm -h

Usage: openllm [OPTIONS] COMMAND [ARGS]...

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  An open platform for operating large language models in production.
  Fine-tune, serve, deploy, and monitor any LLMs with ease.

Starting an LLM Server

To start an LLM server, use openllm start. For example, to start a dolly-v2 server:

openllm start dolly-v2

Following this, a Web UI will be accessible at http://localhost:3000 where you can experiment with the endpoints and sample input prompts.

OpenLLM provides a built-in Python client, allowing you to interact with the model. In a different terminal window or a Jupyter notebook, create a client to start interacting with the model:

>>> import openllm
>>> client = openllm.client.HTTPClient('http://localhost:3000')
>>> client.query('Explain to me the difference between "further" and "farther"')

You can also use the openllm query command to query the model from the terminal:

export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'Explain to me the difference between "further" and "farther"'

Visit http://localhost:3000/docs.json for OpenLLM's API specification.

Users can also specify different variants of the model to be served, by providing the --model-id argument, e.g.:

openllm start flan-t5 --model-id google/flan-t5-large

Use the openllm models command to see the list of models and their variants supported in OpenLLM.

🧩 Supported Models

The following models are currently supported in OpenLLM. By default, OpenLLM doesn't include dependencies to run all models. The extra model-specific dependencies can be installed with the instructions below:

pip install "openllm[flan-t5]"

pip install openllm

pip install "openllm[chatglm]"

pip install "openllm[starcoder]"

pip install "openllm[falcon]"

pip install openllm

pip install openllm

Runtime Implementations (Experimental)

Different LLMs may have multiple runtime implementations. For instance, they might use Pytorch (pt), Tensorflow (tf), or Flax (flax).

If you wish to specify a particular runtime for a model, you can do so by setting the OPENLLM_{MODEL_NAME}_FRAMEWORK={runtime} environment variable before running openllm start.

For example, if you want to use the Tensorflow (tf) implementation for the flan-t5 model, you can use the following command:

OPENLLM_FLAN_T5_FRAMEWORK=tf openllm start flan-t5

Note For GPU support on Flax, refers to Jax's installation to make sure that you have Jax support for the corresponding CUDA version.

Integrating a New Model

OpenLLM encourages contributions by welcoming users to incorporate their custom LLMs into the ecosystem. Check out Adding a New Model Guide to see how you can do it yourself.

⚙️ Integrations

OpenLLM is not just a standalone product; it's a building block designed to easily integrate with other powerful tools. We currently offer integration with BentoML and LangChain.

BentoML

OpenLLM models can be integrated as a Runner in your BentoML service. These runners have a generate method that takes a string as a prompt and returns a corresponding output string. This will allow you to plug and play any OpenLLM models with your existing ML workflow.

import bentoml
import openllm

model = "dolly-v2"

llm_config = openllm.AutoConfig.for_model(model)
llm_runner = openllm.Runner(model, llm_config=llm_config)

svc = bentoml.Service(
    name=f"llm-dolly-v2-service", runners=[llm_runner]
)

@svc.api(input=Text(), output=Text())
async def prompt(input_text: str) -> str:
    answer = await llm_runner.generate(input_text)
    return answer

HuggingFace Agents

OpenLLM seamlessly integrates with HuggingFace Agents.

Warning The HuggingFace Agent is still at experimental stage. It is recommended to OpenLLM with pip install 'openllm[nightly]' to get the latest API update for HuggingFace agent.

import transformers

agent = transformers.HfAgent("http://localhost:3000/hf/agent")  # URL that runs the OpenLLM server

agent.run("Is the following `text` positive or negative?", text="I don't like how this models is generate inputs")

Note Only starcoder is currently supported with Agent integration. The example aboved was also ran with four T4s on EC2 g4dn.12xlarge

If you want to use OpenLLM client to ask questions to the running agent, you can also do so:

import openllm

client = openllm.client.HTTPClient("http://localhost:3000")

client.ask_agent(
    task="Is the following `text` positive or negative?",
    text="What are you thinking about?",
)

LangChain (⏳Coming Soon!)

In future LangChain releases, you'll be able to effortlessly invoke OpenLLM models, like so:

from langchain.llms import OpenLLM
llm = OpenLLM.for_model(model_name='flan-t5')
llm("What is the difference between a duck and a goose?")

if you have an OpenLLM server deployed elsewhere, you can connect to it by specifying its URL:

from langchain.llms import OpenLLM
llm = OpenLLM.for_model(server_url='http://localhost:8000', server_type='http')
llm("What is the difference between a duck and a goose?")

🚀 Deploying to Production

To deploy your LLMs into production:

1. Building a Bento: With OpenLLM, you can easily build a Bento for a specific model, like dolly-v2, using the build command.:

   openllm build dolly-v2

   A Bento, in BentoML, is the unit of distribution. It packages your program's source code, models, files, artifacts, and dependencies.

2. Containerize your Bento

   bentoml containerize <name:version>

   BentoML offers a comprehensive set of options for deploying and hosting online ML services in production. To learn more, check out the Deploying a Bento guide.

🍇 Telemetry

OpenLLM collects usage data to enhance user experience and improve the product. We only report OpenLLM's internal API calls and ensure maximum privacy by excluding sensitive information. We will never collect user code, model data, or stack traces. For usage tracking, check out the code.

You can opt-out of usage tracking by using the --do-not-track CLI option:

openllm [command] --do-not-track

Or by setting environment variable OPENLLM_DO_NOT_TRACK=True:

export OPENLLM_DO_NOT_TRACK=True

👥 Community

Engage with like-minded individuals passionate about LLMs, AI, and more on our Discord!

OpenLLM is actively maintained by the BentoML team. Feel free to reach out and join us in our pursuit to make LLMs more accessible and easy-to-use👉 Join our Slack community!

🎁 Contributing

We welcome contributions! If you're interested in enhancing OpenLLM's capabilities or have any questions, don't hesitate to reach out in our discord channel.

Checkout our Developer Guide if you wish to contribute to OpenLLM's codebase.