OpenLLM/openllm-python/README.md

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    <h1 align="center">🦾 OpenLLM: Self-Hosting LLMs Made Easy</h1>
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## 📖 Introduction

OpenLLM helps developers **run any open-source LLMs**, such as Llama 2 and Mistral, as **OpenAI-compatible API endpoints**, locally and in the cloud, optimized for serving throughput and production deployment.


- 🚂 Support a wide range of open-source LLMs including LLMs fine-tuned with your own data
- ⛓️ OpenAI compatible API endpoints for seamless transition from your LLM app to open-source LLMs
- 🔥 State-of-the-art serving and inference performance
- 🎯 Simplified cloud deployment via [BentoML](https://www.bentoml.com)


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## 💾 TL/DR

For starter, we provide two ways to quickly try out OpenLLM:

### Jupyter Notebooks

Try this [OpenLLM tutorial in Google Colab: Serving Llama 2 with OpenLLM](https://colab.research.google.com/github/bentoml/OpenLLM/blob/main/examples/llama2.ipynb).

### Docker

We provide a docker container that helps you start running OpenLLM:

```bash
docker run --rm -it -p 3000:3000 ghcr.io/bentoml/openllm start facebook/opt-1.3b --backend pt
```

> [!NOTE]
> Given you have access to GPUs and have setup [nvidia-docker](https://github.com/NVIDIA/nvidia-container-toolkit),  you can additionally pass in `--gpus`
> to use GPU for faster inference and optimization
>```bash
> docker run --rm --gpus all -p 3000:3000 -it ghcr.io/bentoml/openllm start HuggingFaceH4/zephyr-7b-beta --backend vllm
> ```


## 🏃 Get started

The following provides instructions for how to get started with OpenLLM locally.

### Prerequisites

You have installed Python 3.8 (or later) and `pip`. We highly recommend using a [Virtual Environment](https://docs.python.org/3/library/venv.html) to prevent package conflicts.

### Install OpenLLM

Install OpenLLM by using `pip` as follows:

```bash
pip install openllm
```

To verify the installation, run:

```bash
$ openllm -h

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

   ██████╗ ██████╗ ███████╗███╗   ██╗██╗     ██╗     ███╗   ███╗
  ██╔═══██╗██╔══██╗██╔════╝████╗  ██║██║     ██║     ████╗ ████║
  ██║   ██║██████╔╝█████╗  ██╔██╗ ██║██║     ██║     ██╔████╔██║
  ██║   ██║██╔═══╝ ██╔══╝  ██║╚██╗██║██║     ██║     ██║╚██╔╝██║
  ╚██████╔╝██║     ███████╗██║ ╚████║███████╗███████╗██║ ╚═╝ ██║
   ╚═════╝ ╚═╝     ╚══════╝╚═╝  ╚═══╝╚══════╝╚══════╝╚═╝     ╚═╝.

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

Options:
  -v, --version  Show the version and exit.
  -h, --help     Show this message and exit.

Commands:
  build       Package a given models into a BentoLLM.
  import      Setup LLM interactively.
  models      List all supported models.
  prune       Remove all saved models, (and optionally bentos) built with OpenLLM locally.
  query       Query a LLM interactively, from a terminal.
  start       Start a LLMServer for any supported LLM.

Extensions:
  build-base-container  Base image builder for BentoLLM.
  dive-bentos           Dive into a BentoLLM.
  get-containerfile     Return Containerfile of any given Bento.
  get-prompt            Get the default prompt used by OpenLLM.
  list-bentos           List available bentos built by OpenLLM.
  list-models           This is equivalent to openllm models...
  playground            OpenLLM Playground.
```

### Start a LLM server

OpenLLM allows you to quickly spin up an LLM server using `openllm start`. For example, to start a [phi-2](https://huggingface.co/microsoft/phi-2) server, run the following:

```bash
TRUST_REMOTE_CODE=True openllm start microsoft/phi-2
```

This starts the server at [http://0.0.0.0:3000/](http://0.0.0.0:3000/). OpenLLM downloads the model to the BentoML local Model Store if it has not been registered before. To view your local models, run `bentoml models list`.

To interact with the server, you can visit the web UI at [http://0.0.0.0:3000/](http://0.0.0.0:3000/) or send a request using `curl`. You can also use OpenLLM’s built-in Python client to interact with the server:

```python
import openllm

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

Alternatively, use the `openllm query` command to query the model:

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

OpenLLM seamlessly supports many models and their variants. You can specify different variants of the model to be served. For example:

```bash
openllm start <model_id> --<options>
```

> [!NOTE]
> OpenLLM supports specifying fine-tuning weights and quantized weights
> for any of the supported models as long as they can be loaded with the model
> architecture. Use the `openllm models` command to see the complete list of supported
> models, their architectures, and their variants.

> [!IMPORTANT]
> If you are testing OpenLLM on CPU, you might want to pass in `DTYPE=float32`. By default,
> OpenLLM will set model `dtype` to `bfloat16` for the best performance.
> ```bash
> DTYPE=float32 openllm start microsoft/phi-2
> ```
> This will also applies to older GPUs. If your GPUs doesn't support `bfloat16`, then you also
> want to set `DTYPE=float16`.

## 🧩 Supported models

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

<!-- update-readme.py: start -->
<details>

<summary>Baichuan</summary>


### Quickstart



> **Note:** Baichuan requires to install with:
> ```bash
> pip install "openllm[baichuan]"
> ```


Run the following command to quickly spin up a Baichuan server:

```bash
TRUST_REMOTE_CODE=True openllm start baichuan-inc/baichuan-7b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Baichuan variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=baichuan) to see more Baichuan-compatible models.



### Supported models

You can specify any of the following Baichuan models via `openllm start`:


- [baichuan-inc/baichuan2-7b-base](https://huggingface.co/baichuan-inc/baichuan2-7b-base)
- [baichuan-inc/baichuan2-7b-chat](https://huggingface.co/baichuan-inc/baichuan2-7b-chat)
- [baichuan-inc/baichuan2-13b-base](https://huggingface.co/baichuan-inc/baichuan2-13b-base)
- [baichuan-inc/baichuan2-13b-chat](https://huggingface.co/baichuan-inc/baichuan2-13b-chat)

</details>

<details>

<summary>ChatGLM</summary>


### Quickstart



> **Note:** ChatGLM requires to install with:
> ```bash
> pip install "openllm[chatglm]"
> ```


Run the following command to quickly spin up a ChatGLM server:

```bash
TRUST_REMOTE_CODE=True openllm start thudm/chatglm-6b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any ChatGLM variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=chatglm) to see more ChatGLM-compatible models.



### Supported models

You can specify any of the following ChatGLM models via `openllm start`:


- [thudm/chatglm-6b](https://huggingface.co/thudm/chatglm-6b)
- [thudm/chatglm-6b-int8](https://huggingface.co/thudm/chatglm-6b-int8)
- [thudm/chatglm-6b-int4](https://huggingface.co/thudm/chatglm-6b-int4)
- [thudm/chatglm2-6b](https://huggingface.co/thudm/chatglm2-6b)
- [thudm/chatglm2-6b-int4](https://huggingface.co/thudm/chatglm2-6b-int4)
- [thudm/chatglm3-6b](https://huggingface.co/thudm/chatglm3-6b)

</details>

<details>

<summary>Dbrx</summary>


### Quickstart



> **Note:** Dbrx requires to install with:
> ```bash
> pip install "openllm[dbrx]"
> ```


Run the following command to quickly spin up a Dbrx server:

```bash
TRUST_REMOTE_CODE=True openllm start databricks/dbrx-instruct
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Dbrx variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=dbrx) to see more Dbrx-compatible models.



### Supported models

You can specify any of the following Dbrx models via `openllm start`:


- [databricks/dbrx-instruct](https://huggingface.co/databricks/dbrx-instruct)
- [databricks/dbrx-base](https://huggingface.co/databricks/dbrx-base)

</details>

<details>

<summary>DollyV2</summary>


### Quickstart

Run the following command to quickly spin up a DollyV2 server:

```bash
TRUST_REMOTE_CODE=True openllm start databricks/dolly-v2-3b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any DollyV2 variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=dolly_v2) to see more DollyV2-compatible models.



### Supported models

You can specify any of the following DollyV2 models via `openllm start`:


- [databricks/dolly-v2-3b](https://huggingface.co/databricks/dolly-v2-3b)
- [databricks/dolly-v2-7b](https://huggingface.co/databricks/dolly-v2-7b)
- [databricks/dolly-v2-12b](https://huggingface.co/databricks/dolly-v2-12b)

</details>

<details>

<summary>Falcon</summary>


### Quickstart



> **Note:** Falcon requires to install with:
> ```bash
> pip install "openllm[falcon]"
> ```


Run the following command to quickly spin up a Falcon server:

```bash
TRUST_REMOTE_CODE=True openllm start tiiuae/falcon-7b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Falcon variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=falcon) to see more Falcon-compatible models.



### Supported models

You can specify any of the following Falcon models via `openllm start`:


- [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
- [tiiuae/falcon-40b](https://huggingface.co/tiiuae/falcon-40b)
- [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
- [tiiuae/falcon-40b-instruct](https://huggingface.co/tiiuae/falcon-40b-instruct)

</details>

<details>

<summary>FlanT5</summary>


### Quickstart

Run the following command to quickly spin up a FlanT5 server:

```bash
TRUST_REMOTE_CODE=True openllm start google/flan-t5-large
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any FlanT5 variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=flan_t5) to see more FlanT5-compatible models.



### Supported models

You can specify any of the following FlanT5 models via `openllm start`:


- [google/flan-t5-small](https://huggingface.co/google/flan-t5-small)
- [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
- [google/flan-t5-large](https://huggingface.co/google/flan-t5-large)
- [google/flan-t5-xl](https://huggingface.co/google/flan-t5-xl)
- [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl)

</details>

<details>

<summary>Gemma</summary>


### Quickstart



> **Note:** Gemma requires to install with:
> ```bash
> pip install "openllm[gemma]"
> ```


Run the following command to quickly spin up a Gemma server:

```bash
TRUST_REMOTE_CODE=True openllm start google/gemma-7b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Gemma variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=gemma) to see more Gemma-compatible models.



### Supported models

You can specify any of the following Gemma models via `openllm start`:


- [google/gemma-7b](https://huggingface.co/google/gemma-7b)
- [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it)
- [google/gemma-2b](https://huggingface.co/google/gemma-2b)
- [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it)

</details>

<details>

<summary>GPTNeoX</summary>


### Quickstart

Run the following command to quickly spin up a GPTNeoX server:

```bash
TRUST_REMOTE_CODE=True openllm start eleutherai/gpt-neox-20b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any GPTNeoX variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=gpt_neox) to see more GPTNeoX-compatible models.



### Supported models

You can specify any of the following GPTNeoX models via `openllm start`:


- [eleutherai/gpt-neox-20b](https://huggingface.co/eleutherai/gpt-neox-20b)

</details>

<details>

<summary>Llama</summary>


### Quickstart



> **Note:** Llama requires to install with:
> ```bash
> pip install "openllm[llama]"
> ```


Run the following command to quickly spin up a Llama server:

```bash
TRUST_REMOTE_CODE=True openllm start NousResearch/llama-2-7b-hf
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Llama variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=llama) to see more Llama-compatible models.



### Supported models

You can specify any of the following Llama models via `openllm start`:


- [meta-llama/Llama-2-70b-chat-hf](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)
- [meta-llama/Llama-2-13b-chat-hf](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)
- [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
- [meta-llama/Llama-2-70b-hf](https://huggingface.co/meta-llama/Llama-2-70b-hf)
- [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf)
- [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
- [NousResearch/llama-2-70b-chat-hf](https://huggingface.co/NousResearch/llama-2-70b-chat-hf)
- [NousResearch/llama-2-13b-chat-hf](https://huggingface.co/NousResearch/llama-2-13b-chat-hf)
- [NousResearch/llama-2-7b-chat-hf](https://huggingface.co/NousResearch/llama-2-7b-chat-hf)
- [NousResearch/llama-2-70b-hf](https://huggingface.co/NousResearch/llama-2-70b-hf)
- [NousResearch/llama-2-13b-hf](https://huggingface.co/NousResearch/llama-2-13b-hf)
- [NousResearch/llama-2-7b-hf](https://huggingface.co/NousResearch/llama-2-7b-hf)

</details>

<details>

<summary>Mistral</summary>


### Quickstart



> **Note:** Mistral requires to install with:
> ```bash
> pip install "openllm[mistral]"
> ```


Run the following command to quickly spin up a Mistral server:

```bash
TRUST_REMOTE_CODE=True openllm start mistralai/Mistral-7B-Instruct-v0.1
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Mistral variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=mistral) to see more Mistral-compatible models.



### Supported models

You can specify any of the following Mistral models via `openllm start`:


- [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha)
- [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
- [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
- [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)

</details>

<details>

<summary>Mixtral</summary>


### Quickstart



> **Note:** Mixtral requires to install with:
> ```bash
> pip install "openllm[mixtral]"
> ```


Run the following command to quickly spin up a Mixtral server:

```bash
TRUST_REMOTE_CODE=True openllm start mistralai/Mixtral-8x7B-Instruct-v0.1
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Mixtral variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=mixtral) to see more Mixtral-compatible models.



### Supported models

You can specify any of the following Mixtral models via `openllm start`:


- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)

</details>

<details>

<summary>MPT</summary>


### Quickstart



> **Note:** MPT requires to install with:
> ```bash
> pip install "openllm[mpt]"
> ```


Run the following command to quickly spin up a MPT server:

```bash
TRUST_REMOTE_CODE=True openllm start mosaicml/mpt-7b-instruct
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any MPT variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=mpt) to see more MPT-compatible models.



### Supported models

You can specify any of the following MPT models via `openllm start`:


- [mosaicml/mpt-7b](https://huggingface.co/mosaicml/mpt-7b)
- [mosaicml/mpt-7b-instruct](https://huggingface.co/mosaicml/mpt-7b-instruct)
- [mosaicml/mpt-7b-chat](https://huggingface.co/mosaicml/mpt-7b-chat)
- [mosaicml/mpt-7b-storywriter](https://huggingface.co/mosaicml/mpt-7b-storywriter)
- [mosaicml/mpt-30b](https://huggingface.co/mosaicml/mpt-30b)
- [mosaicml/mpt-30b-instruct](https://huggingface.co/mosaicml/mpt-30b-instruct)
- [mosaicml/mpt-30b-chat](https://huggingface.co/mosaicml/mpt-30b-chat)

</details>

<details>

<summary>OPT</summary>


### Quickstart



> **Note:** OPT requires to install with:
> ```bash
> pip install "openllm[opt]"
> ```


Run the following command to quickly spin up a OPT server:

```bash
openllm start facebook/opt-1.3b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any OPT variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=opt) to see more OPT-compatible models.



### Supported models

You can specify any of the following OPT models via `openllm start`:


- [facebook/opt-125m](https://huggingface.co/facebook/opt-125m)
- [facebook/opt-350m](https://huggingface.co/facebook/opt-350m)
- [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b)
- [facebook/opt-2.7b](https://huggingface.co/facebook/opt-2.7b)
- [facebook/opt-6.7b](https://huggingface.co/facebook/opt-6.7b)
- [facebook/opt-66b](https://huggingface.co/facebook/opt-66b)

</details>

<details>

<summary>Phi</summary>


### Quickstart



> **Note:** Phi requires to install with:
> ```bash
> pip install "openllm[phi]"
> ```


Run the following command to quickly spin up a Phi server:

```bash
TRUST_REMOTE_CODE=True openllm start microsoft/phi-2
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Phi variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=phi) to see more Phi-compatible models.



### Supported models

You can specify any of the following Phi models via `openllm start`:


- [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
- [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5)

</details>

<details>

<summary>Qwen</summary>


### Quickstart



> **Note:** Qwen requires to install with:
> ```bash
> pip install "openllm[qwen]"
> ```


Run the following command to quickly spin up a Qwen server:

```bash
TRUST_REMOTE_CODE=True openllm start qwen/Qwen-7B-Chat
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Qwen variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=qwen) to see more Qwen-compatible models.



### Supported models

You can specify any of the following Qwen models via `openllm start`:


- [qwen/Qwen-7B-Chat](https://huggingface.co/qwen/Qwen-7B-Chat)
- [qwen/Qwen-7B-Chat-Int8](https://huggingface.co/qwen/Qwen-7B-Chat-Int8)
- [qwen/Qwen-7B-Chat-Int4](https://huggingface.co/qwen/Qwen-7B-Chat-Int4)
- [qwen/Qwen-14B-Chat](https://huggingface.co/qwen/Qwen-14B-Chat)
- [qwen/Qwen-14B-Chat-Int8](https://huggingface.co/qwen/Qwen-14B-Chat-Int8)
- [qwen/Qwen-14B-Chat-Int4](https://huggingface.co/qwen/Qwen-14B-Chat-Int4)

</details>

<details>

<summary>StableLM</summary>


### Quickstart



> **Note:** StableLM requires to install with:
> ```bash
> pip install "openllm[stablelm]"
> ```


Run the following command to quickly spin up a StableLM server:

```bash
TRUST_REMOTE_CODE=True openllm start stabilityai/stablelm-tuned-alpha-3b
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any StableLM variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=stablelm) to see more StableLM-compatible models.



### Supported models

You can specify any of the following StableLM models via `openllm start`:


- [stabilityai/stablelm-tuned-alpha-3b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b)
- [stabilityai/stablelm-tuned-alpha-7b](https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b)
- [stabilityai/stablelm-base-alpha-3b](https://huggingface.co/stabilityai/stablelm-base-alpha-3b)
- [stabilityai/stablelm-base-alpha-7b](https://huggingface.co/stabilityai/stablelm-base-alpha-7b)

</details>

<details>

<summary>StarCoder</summary>


### Quickstart



> **Note:** StarCoder requires to install with:
> ```bash
> pip install "openllm[starcoder]"
> ```


Run the following command to quickly spin up a StarCoder server:

```bash
TRUST_REMOTE_CODE=True openllm start bigcode/starcoder
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any StarCoder variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=starcoder) to see more StarCoder-compatible models.



### Supported models

You can specify any of the following StarCoder models via `openllm start`:


- [bigcode/starcoder](https://huggingface.co/bigcode/starcoder)
- [bigcode/starcoderbase](https://huggingface.co/bigcode/starcoderbase)

</details>

<details>

<summary>Yi</summary>


### Quickstart



> **Note:** Yi requires to install with:
> ```bash
> pip install "openllm[yi]"
> ```


Run the following command to quickly spin up a Yi server:

```bash
TRUST_REMOTE_CODE=True openllm start 01-ai/Yi-6B
```
In a different terminal, run the following command to interact with the server:

```bash
export OPENLLM_ENDPOINT=http://localhost:3000
openllm query 'What are large language models?'
```


> **Note:** Any Yi variants can be deployed with OpenLLM. Visit the [HuggingFace Model Hub](https://huggingface.co/models?sort=trending&search=yi) to see more Yi-compatible models.



### Supported models

You can specify any of the following Yi models via `openllm start`:


- [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B)
- [01-ai/Yi-34B](https://huggingface.co/01-ai/Yi-34B)
- [01-ai/Yi-6B-200K](https://huggingface.co/01-ai/Yi-6B-200K)
- [01-ai/Yi-34B-200K](https://huggingface.co/01-ai/Yi-34B-200K)

</details>

<!-- update-readme.py: stop -->

More models will be integrated with OpenLLM and we welcome your contributions if you want to incorporate your custom LLMs into the ecosystem. Check out [Adding a New Model Guide](https://github.com/bentoml/OpenLLM/blob/main/ADDING_NEW_MODEL.md) to learn more.

## 💻 Run your model on multiple GPUs

OpenLLM allows you to start your model server on multiple GPUs and specify the number of workers per resource assigned using the `--workers-per-resource` option. For example, if you have 4 available GPUs, you set the value as one divided by the number as only one instance of the Runner server will be spawned.

```bash
TRUST_REMOTE_CODE=True openllm start microsoft/phi-2 --workers-per-resource 0.25
```

> [!NOTE]
> The amount of GPUs required depends on the model size itself.
> You can use [the Model Memory Calculator from Hugging Face](https://huggingface.co/spaces/hf-accelerate/model-memory-usage) to
> calculate how much vRAM is needed to train and perform big model
> inference on a model and then plan your GPU strategy based on it.

When using the `--workers-per-resource` option with the `openllm build` command, the environment variable is saved into the resulting Bento.

For more information, see [Resource scheduling strategy](https://docs.bentoml.org/en/latest/guides/scheduling.html#).

## 🛞 Runtime implementations

Different LLMs may support multiple runtime implementations. Models that have `vLLM` (`vllm`) supports will use vLLM by default, otherwise it fallback to use `PyTorch` (`pt`).

To specify a specific runtime for your chosen model, use the `--backend` option. For example:

```bash
openllm start meta-llama/Llama-2-7b-chat-hf --backend vllm
```

Note:

1. To use the vLLM backend, you need a GPU with at least the Ampere architecture or newer and CUDA version 11.8.
2. To see the backend options of each model supported by OpenLLM, see the Supported models section or run `openllm models`.

## 📐 Quantization

Quantization is a technique to reduce the storage and computation requirements for machine learning models, particularly during inference. By approximating floating-point numbers as integers (quantized values), quantization allows for faster computations, reduced memory footprint, and can make it feasible to deploy large models on resource-constrained devices.

OpenLLM supports the following quantization techniques

- [LLM.int8(): 8-bit Matrix Multiplication](https://arxiv.org/abs/2208.07339) through [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
- [SpQR: A Sparse-Quantized Representation for Near-Lossless LLM Weight Compression
  ](https://arxiv.org/abs/2306.03078) through [bitsandbytes](https://github.com/TimDettmers/bitsandbytes)
- [AWQ: Activation-aware Weight Quantization](https://arxiv.org/abs/2306.00978),
- [GPTQ: Accurate Post-Training Quantization](https://arxiv.org/abs/2210.17323)
- [SqueezeLLM: Dense-and-Sparse Quantization](https://arxiv.org/abs/2306.07629).

### PyTorch backend

With PyTorch backend, OpenLLM supports `int8`, `int4`, and `gptq`.

For using int8 and int4 quantization through `bitsandbytes`, you can use the following command:

```bash
TRUST_REMOTE_CODE=True openllm start microsoft/phi-2 --quantize int8
```

To run inference with `gptq`, simply pass `--quantize gptq`:

```bash
openllm start TheBloke/Llama-2-7B-Chat-GPTQ --quantize gptq
```

> [!NOTE]
> In order to run GPTQ, make sure you run `pip install "openllm[gptq]"`
> first to install the dependency. From the GPTQ paper, it is recommended to quantized the weights before serving.
> See [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) for more information on GPTQ quantization.

### vLLM backend

With vLLM backend, OpenLLM supports `awq`, `squeezellm`

To run inference with `awq`, simply pass `--quantize awq`:

```bash
openllm start TheBloke/zephyr-7B-alpha-AWQ --quantize awq
```

To run inference with `squeezellm`, simply pass `--quantize squeezellm`:

```bash
openllm start squeeze-ai-lab/sq-llama-2-7b-w4-s0 --quantize squeezellm --serialization legacy
```

> [!IMPORTANT]
> Since both `squeezellm` and `awq` are weight-aware quantization methods, meaning the quantization is done during training, all pre-trained weights needs to get quantized before inference time. Make sure to find compatible weights on HuggingFace Hub for your model of choice.

## 🛠️ Serving fine-tuning layers

[PEFT](https://huggingface.co/docs/peft/index), or Parameter-Efficient Fine-Tuning, is a methodology designed to fine-tune pre-trained models more efficiently. Instead of adjusting all model parameters, PEFT focuses on tuning only a subset, reducing computational and storage costs. [LoRA](https://huggingface.co/docs/peft/conceptual_guides/lora) (Low-Rank Adaptation) is one of the techniques supported by PEFT. It streamlines fine-tuning by using low-rank decomposition to represent weight updates, thereby drastically reducing the number of trainable parameters.

With OpenLLM, you can take advantage of the fine-tuning feature by serving models with any PEFT-compatible layers using the `--adapter-id` option. For example:

```bash
openllm start facebook/opt-6.7b --adapter-id aarnphm/opt-6-7b-quotes:default
```

OpenLLM also provides flexibility by supporting adapters from custom file paths:

```bash
openllm start facebook/opt-6.7b --adapter-id /path/to/adapters:local_adapter
```

To use multiple adapters, use the following format:

```bash
openllm start facebook/opt-6.7b --adapter-id aarnphm/opt-6.7b-lora:default --adapter-id aarnphm/opt-6.7b-french:french_lora
```

By default, all adapters will be injected into the models during startup. Adapters can be specified per request via `adapter_name`:

```bash
curl -X 'POST' \
  'http://localhost:3000/v1/generate' \
  -H 'accept: application/json' \
  -H 'Content-Type: application/json' \
  -d '{
  "prompt": "What is the meaning of life?",
  "stop": [
    "philosopher"
  ],
  "llm_config": {
    "max_new_tokens": 256,
    "temperature": 0.75,
    "top_k": 15,
    "top_p": 1
  },
  "adapter_name": "default"
}'
```

To include this into the Bento, you can specify the `--adapter-id` option when using the `openllm build` command:

```bash
openllm build facebook/opt-6.7b --adapter-id ...
```

If you use a relative path for `--adapter-id`, you need to add `--build-ctx`.

```bash
openllm build facebook/opt-6.7b --adapter-id ./path/to/adapter_id --build-ctx .
```

> [!IMPORTANT]
> Fine-tuning support is still experimental and currently only works with PyTorch backend. vLLM support is coming soon.


## ⚙️ Integrations

OpenLLM is not just a standalone product; it's a building block designed to
integrate with other powerful tools easily. We currently offer integration with
[OpenAI's Compatible Endpoints](https://platform.openai.com/docs/api-reference/completions/object),
[LlamaIndex](https://www.llamaindex.ai/),
[LangChain](https://github.com/hwchase17/langchain), and
[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents).

### OpenAI Compatible Endpoints

OpenLLM Server can be used as a drop-in replacement for OpenAI's API. Simply
specify the base_url to `llm-endpoint/v1` and you are good to go:

```python
import openai

client = openai.OpenAI(
  base_url='http://localhost:3000/v1', api_key='na'
)  # Here the server is running on localhost:3000

completions = client.completions.create(
  prompt='Write me a tag line for an ice cream shop.', model=model, max_tokens=64, stream=stream
)
```

The compatible endpoints supports `/completions`, `/chat/completions`, and `/models`

> [!NOTE]
> You can find out OpenAI example clients under the
> [examples](https://github.com/bentoml/OpenLLM/tree/main/examples) folder.


### [LlamaIndex](https://docs.llamaindex.ai/en/stable/examples/llm/openllm/)

To start a local LLM with `llama_index`, simply use `llama_index.llms.openllm.OpenLLM`:

```python
import asyncio
from llama_index.llms.openllm import OpenLLM

llm = OpenLLM('HuggingFaceH4/zephyr-7b-alpha')

llm.complete('The meaning of life is')


async def main(prompt, **kwargs):
  async for it in llm.astream_chat(prompt, **kwargs):
    print(it)


asyncio.run(main('The time at San Francisco is'))
```

If there is a remote LLM Server running elsewhere, then you can use `llama_index.llms.openllm.OpenLLMAPI`:

```python
from llama_index.llms.openllm import OpenLLMAPI
```

> [!NOTE]
> All synchronous and asynchronous API from `llama_index.llms.LLM` are supported.

### [LangChain](https://python.langchain.com/docs/integrations/llms/openllm/)

Spin up an OpenLLM server, and connect to it by specifying its URL:

```python
from langchain.llms import OpenLLM

llm = OpenLLM(server_url='http://44.23.123.1:3000', server_type='http')
llm('What is the difference between a duck and a goose? And why there are so many Goose in Canada?')
```

### Transformers Agents

OpenLLM seamlessly integrates with
[Transformers Agents](https://huggingface.co/docs/transformers/transformers_agents).

> [!WARNING]
> The Transformers Agent is still at an experimental stage. It is
> recommended to install OpenLLM with `pip install -r nightly-requirements.txt`
> to get the latest API update for HuggingFace agent.

```python
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")
```

<!-- hatch-fancy-pypi-readme interim stop -->

![Gif showing Agent integration](/.github/assets/agent.gif)

<br/>

<!-- hatch-fancy-pypi-readme meta start -->

## 🚀 Deploying models to production

There are several ways to deploy your LLMs:

### 🐳 Docker container

1. **Building a Bento**: With OpenLLM, you can easily build a Bento for a
   specific model, like `mistralai/Mistral-7B-Instruct-v0.1`, using the `build` command.:

   ```bash
   openllm build mistralai/Mistral-7B-Instruct-v0.1
   ```

   A
   [Bento](https://docs.bentoml.com/en/latest/concepts/bento.html#what-is-a-bento),
   in BentoML, is the unit of distribution. It packages your program's source
   code, models, files, artefacts, and dependencies.

2. **Containerize your Bento**

   ```bash
   bentoml containerize <name:version>
   ```

   This generates a OCI-compatible docker image that can be deployed anywhere
   docker runs. For best scalability and reliability of your LLM service in
   production, we recommend deploy with BentoCloud。

### ☁️ BentoCloud

Deploy OpenLLM with [BentoCloud](https://www.bentoml.com/), the inference platform
for fast moving AI teams.

1. **Create a BentoCloud account:** [sign up here](https://bentoml.com/)

2. **Log into your BentoCloud account:**

   ```bash
   bentoml cloud login --api-token <your-api-token> --endpoint <bento-cloud-endpoint>
   ```

> [!NOTE]
> Replace `<your-api-token>` and `<bento-cloud-endpoint>` with your
> specific API token and the BentoCloud endpoint respectively.

3. **Bulding a Bento**: With OpenLLM, you can easily build a Bento for a
   specific model, such as `mistralai/Mistral-7B-Instruct-v0.1`:

   ```bash
   openllm build mistralai/Mistral-7B-Instruct-v0.1
   ```

4. **Pushing a Bento**: Push your freshly-built Bento service to BentoCloud via
   the `push` command:

   ```bash
   bentoml push <name:version>
   ```

5. **Deploying a Bento**: Deploy your LLMs to BentoCloud with a single
   `bentoml deployment create` command following the
   [deployment instructions](https://docs.bentoml.com/en/latest/reference/cli.html#bentoml-deployment-create).

## 👥 Community

Engage with like-minded individuals passionate about LLMs, AI, and more on our
[Discord](https://l.bentoml.com/join-openllm-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!](https://l.bentoml.com/join-slack)

## 🎁 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](https://l.bentoml.com/join-openllm-discord).

Checkout our
[Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md)
if you wish to contribute to OpenLLM's codebase.

## 🍇 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](https://github.com/bentoml/OpenLLM/blob/main/openllm-core/src/openllm_core/utils/analytics.py).

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

```bash
openllm [command] --do-not-track
```

Or by setting the environment variable `OPENLLM_DO_NOT_TRACK=True`:

```bash
export OPENLLM_DO_NOT_TRACK=True
```

## 📔 Citation

If you use OpenLLM in your research, we provide a [citation](./CITATION.cff) to
use:

```bibtex
@software{Pham_OpenLLM_Operating_LLMs_2023,
author = {Pham, Aaron and Yang, Chaoyu and Sheng, Sean and  Zhao, Shenyang and Lee, Sauyon and Jiang, Bo and Dong, Fog and Guan, Xipeng and Ming, Frost},
license = {Apache-2.0},
month = jun,
title = {{OpenLLM: Operating LLMs in production}},
url = {https://github.com/bentoml/OpenLLM},
year = {2023}
}
```

<!-- hatch-fancy-pypi-readme meta stop -->