open-gpt-torch 0.0.8
An open-source cloud-native of large multi-modal models (LMMs) serving framework.
License: Apache-2.0
Requires Python: >=3.8,<4.0
Keywords: Pytorch,LMM,GPT,LLM,multi-modality,cloud-native,model-serving,model-inference,llama,vicuna,stabellm

# ☄️ OpenGPT

<p align="center">
<a href="https://github.com/jina-ai/opengpt"><img src="https://github.com/jina-ai/opengpt/blob/main/.github/images/logo.png" alt="OpenGPT: An open-source cloud-native large-scale multimodal model serving framework" width="300px"></a>
<br>
</p>

> "A playful and whimsical vector art of a Stochastic Tigger, wearing a t-shirt with a "GPT" text printed logo, surrounded by colorful geometric shapes.  –ar 1:1 –upbeta"
>
> — Prompts and logo art was produced with  [PromptPerfect](https://promptperfect.jina.ai/) & [Stable Diffusion X](https://clipdrop.co/stable-diffusion)


![](https://img.shields.io/badge/Made%20with-JinaAI-blueviolet?style=flat)
[![PyPI](https://img.shields.io/pypi/v/open_gpt_torch)](https://pypi.org/project/open_gpt_torch/)
[![PyPI - License](https://img.shields.io/pypi/l/open_gpt_torch)](https://pypi.org/project/open_gpt_torch/)

**OpenGPT** is an open-source _cloud-native_ large-scale **_multimodal models_** (LMMs) serving framework. 
It is designed to simplify the deployment and management of large language models, on a distributed cluster of GPUs.
We aim to make it a one-stop solution for a centralized and accessible place to gather techniques for optimizing large-scale multimodal models and make them easy to use for everyone.


## Table of contents

- [Features](#features)
- [Supported models](#supported-models)
- [Get started](#get-started)
- [Build a model serving in one line](#build-a-model-serving-in-one-line)
- [Cloud-native deployment](#cloud-native-deployment)
- [Roadmap](#roadmap)

## Features

OpenGPT provides the following features to make it easy to deploy and serve **large multi-modal models** (LMMs) at scale:

- Support for multi-modal models on top of large language models
- Scalable architecture for handling high traffic loads
- Optimized for low-latency inference
- Automatic model partitioning and distribution across multiple GPUs
- Centralized model management and monitoring
- REST API for easy integration with existing applications

## Updates

- **2023-05-12**: 🎉We have released the first version `v0.0.1` of OpenGPT. You can install it with `pip install open_gpt_torch`.

## Supported Models

<details>

OpenGPT supports the following models out of the box:

- LLM (Large Language Model)

  - [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/): open and efficient foundation language models by Meta
  - [Pythia](https://github.com/EleutherAI/pythia): a collection of models developed to facilitate interpretability research by EleutherAI
  - [StableLM](https://github.com/Stability-AI/StableLM): series of large language models by Stability AI
  - [Vicuna](https://vicuna.lmsys.org/): a chat assistant fine-tuned from LLaMA on user-shared conversations by LMSYS
  - [MOSS](https://txsun1997.github.io/blogs/moss.html): conversational language model from Fudan University

- LMM (Large Multi-modal Model)

  - [OpenFlamingo](https://github.com/mlfoundations/open_flamingo): an open source version of DeepMind's [Flamingo](https://www.deepmind.com/blog/tackling-multiple-tasks-with-a-single-visual-language-model) model
  - [MiniGPT-4](https://minigpt-4.github.io/): aligns a frozen visual encoder with a frozen LLM, Vicuna, using just one projection layer. 

For more details about the supported models, please see the [Model Zoo](./MODEL_ZOO.md).

</details>


## Roadmap

You can view our roadmap with features that are planned, started, and completed on the [Roadmap discussion](https://github.com/jina-ai/opengpt/discussions/categories/roadmap) category.

## Get Started

### Installation

Install the package with `pip`:

```bash
pip install open_gpt_torch
```

### Quickstart

```python
import open_gpt

model = open_gpt.create_model(
    'stabilityai/stablelm-tuned-alpha-3b', device='cuda', precision='fp16'
)

prompt = "The quick brown fox jumps over the lazy dog."

output = model.generate(
    prompt,
    max_length=100,
    temperature=0.9,
    top_k=50,
    top_p=0.95,
    repetition_penalty=1.2,
    do_sample=True,
    num_return_sequences=1,
)
```

We use the [stabilityai/stablelm-tuned-alpha-3b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b) as the open example model as it is relatively small and fast to download.

> **Warning**
> In the above example, we use `precision='fp16'` to reduce the memory usage and speed up the inference with some loss in accuracy on text generation tasks. 
> You can also use `precision='fp32'` instead as you like for better performance. 

> **Note**
> It usually takes a while (several minutes) for the first time to download and load the model into the memory.


In most cases of large model serving, the model cannot fit into a single GPU. To solve this problem, we also provide a `device_map` option (supported by `accecleate` package) to automatically partition the model and distribute it across multiple GPUs:

```python
model = open_gpt.create_model(
    'stabilityai/stablelm-tuned-alpha-3b', precision='fp16', device_map='balanced'
)
```

In the above example, `device_map="balanced"` evenly split the model on all available GPUs, making it possible for you to serve large models.

> **Note**
> The `device_map` option is supported by the [accelerate](https://github.com/huggingface/accelerate) package. 


See [examples on how to use opengpt with different models.](./examples) 🔥


## Build a model serving in one line

To do so, you can use the `serve` command:

```bash
opengpt serve stabilityai/stablelm-tuned-alpha-3b --precision fp16 --device_map balanced
```

💡 **Tip**: you can inspect the available options with `opengpt serve --help`.

This will start a gRPC and HTTP server listening on port `51000` and `52000` respectively. 
Once the server is ready, as shown below:
<details>
<summary>Click to expand</summary>
<img src="https://github.com/jina-ai/opengpt/blob/main/.github/images/serve_ready.png" width="600px">
</details>

You can then send requests to the server:

```python
import requests

prompt = "The quick brown fox jumps over the lazy dog."

response = requests.post(
    "http://localhost:51000/generate",
    json={
        "prompt": prompt,
        "max_length": 100,
        "temperature": 0.9,
        "top_k": 50,
        "top_p": 0.95,
        "repetition_penalty": 1.2,
        "do_sample": True,
        "num_return_sequences": 1,
    },
)
```

What's more, we also provide a [Python client](https://github.com/jina-ai/inference-client/) (`inference-client`) for you to easily interact with the server:

```python
from open_gpt import Client

client = Client()

# connect to the model server
model = client.get_model(endpoint='grpc://0.0.0.0:51000')

prompt = "The quick brown fox jumps over the lazy dog."

output = model.generate(
    prompt,
    max_length=100,
    temperature=0.9,
    top_k=50,
    top_p=0.95,
    repetition_penalty=1.2,
    do_sample=True,
    num_return_sequences=1,
)
```

💡 **Tip**: To display the list of available commands, please use the `list` command.

## Cloud-native deployment

You can also deploy the server to a cloud provider like Jina Cloud or AWS.
To do so, you can use `deploy` command:

### Jina Cloud

using predefined executor

```bash
opengpt deploy stabilityai/stablelm-tuned-alpha-3b --precision fp16 --device_map balanced --cloud jina --replicas 1
```

It will give you a HTTP url and a gRPC url by default:
```bash
https://{random-host-name}-http.wolf.jina.ai
grpcs://{random-host-name}-grpc.wolf.jina.ai
```


### AWS

TBD

## Contributing

We welcome contributions from the community! To contribute, please submit a pull request following our contributing guidelines.

## License

OpenGPT is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.
Copyright 2020-2022 Jina AI Limited.  All rights reserved.

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