diff --git a/README.md b/README.md index 67d9e0c0..51a467ae 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,6 @@ -# 🦾 OpenLLM: Self-Hosting LLMs Made Easy +
+

🦾 OpenLLM: Self-Hosting LLMs Made Easy

+
[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202-green.svg)](https://github.com/bentoml/OpenLLM/blob/main/LICENSE) [![Releases](https://img.shields.io/pypi/v/openllm.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/openllm) @@ -25,16 +27,110 @@ openllm hello OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also add a [model repository to run custom models](#set-up-a-custom-repository) with OpenLLM. -| Model | Parameters | Quantization | Required GPU | Start a Server | -| ---------------- | ---------- | ------------ | ------------- | ----------------------------------- | -| Llama 3.3 | 70B | - | 80Gx2 | `openllm serve llama3.3:70b` | -| Llama 3.2 | 3B | - | 12G | `openllm serve llama3.2:3b` | -| Llama 3.2 Vision | 11B | - | 80G | `openllm serve llama3.2:11b-vision` | -| Mistral | 7B | - | 24G | `openllm serve mistral:7b` | -| Qwen 2.5 | 1.5B | - | 12G | `openllm serve qwen2.5:1.5b` | -| Qwen 2.5 Coder | 7B | - | 24G | `openllm serve qwen2.5-coder:7b` | -| Gemma 2 | 9B | - | 24G | `openllm serve gemma2:9b` | -| Phi3 | 3.8B | - | 12G | `openllm serve phi3:3.8b` | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelParametersRequired GPUStart a Server
deepseek-r1671B80Gx16openllm serve deepseek-r1:671b-fc3d
deepseek-r1-distill14B80Gopenllm serve deepseek-r1-distill:qwen2.5-14b-98a9
deepseek-v3671B80Gx16openllm serve deepseek-v3:671b-instruct-d7ec
gemma22B12Gopenllm serve gemma2:2b-instruct-747d
llama3.18B24Gopenllm serve llama3.1:8b-instruct-3c0c
llama3.21B24Gopenllm serve llama3.2:1b-instruct-f041
llama3.370B80Gx2openllm serve llama3.3:70b-instruct-b850
mistral8B24Gopenllm serve mistral:8b-instruct-50e8
mistral-large123B80Gx4openllm serve mistral-large:123b-instruct-1022
mistralai24B80Gopenllm serve mistralai:24b-small-instruct-2501-0e69
mixtral7B80Gx2openllm serve mixtral:8x7b-instruct-v0.1-b752
phi414B80Gopenllm serve phi4:14b-c12d
pixtral12B80Gopenllm serve pixtral:12b-240910-c344
qwen2.57B24Gopenllm serve qwen2.5:7b-instruct-3260
qwen2.5-coder7B24Gopenllm serve qwen2.5-coder:7b-instruct-e75d
qwen2.5vl3B24Gopenllm serve qwen2.5vl:3b-instruct-4686
... @@ -46,15 +142,16 @@ To start an LLM server locally, use the `openllm serve` command and specify the > [!NOTE] > OpenLLM does not store model weights. A Hugging Face token (HF_TOKEN) is required for gated models. +> > 1. Create your Hugging Face token [here](https://huggingface.co/settings/tokens). -> 2. Request access to the gated model, such as [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B). +> 2. Request access to the gated model, such as [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). > 3. Set your token as an environment variable by running: > ```bash > export HF_TOKEN= > ``` ```bash -openllm serve llama3:8b +openllm serve openllm serve llama3.2:1b-instruct-f041 ``` The server will be accessible at [http://localhost:3000](http://localhost:3000/), providing OpenAI-compatible APIs for interaction. You can call the endpoints with different frameworks and tools that support OpenAI-compatible APIs. Typically, you may need to specify the following: @@ -79,7 +176,7 @@ client = OpenAI(base_url='http://localhost:3000/v1', api_key='na') # print(model_list) chat_completion = client.chat.completions.create( - model="meta-llama/Meta-Llama-3-8B-Instruct", + model="meta-llama/Llama-3.2-1B-Instruct", messages=[ { "role": "user", @@ -94,7 +191,6 @@ for chunk in chat_completion: -
LlamaIndex @@ -102,9 +198,10 @@ for chunk in chat_completion: ```python from llama_index.llms.openai import OpenAI -llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Meta-Llama-3-8B-Instruct", api_key="dummy") +llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Llama-3.2-1B-Instruct", api_key="dummy") ... ``` +
## Chat UI @@ -138,7 +235,7 @@ openllm repo update To review a model’s information, run: ```bash -openllm model get llama3:8b +openllm model get openllm serve llama3.2:1b-instruct-f041 ``` ### Add a model to the default model repository @@ -166,7 +263,7 @@ OpenLLM supports LLM cloud deployment via BentoML, the unified model serving fra [Sign up for BentoCloud](https://www.bentoml.com/) for free and [log in](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). Then, run `openllm deploy` to deploy a model to BentoCloud: ```bash -openllm deploy llama3:8b +openllm deploy openllm serve llama3.2:1b-instruct-f041 ``` > [!NOTE] @@ -196,7 +293,6 @@ This project uses the following open-source projects: - [bentoml/bentoml](https://github.com/bentoml/bentoml) for production level model serving - [vllm-project/vllm](https://github.com/vllm-project/vllm) for production level LLM backend - [blrchen/chatgpt-lite](https://github.com/blrchen/chatgpt-lite) for a fancy Web Chat UI -- [chujiezheng/chat_templates](https://github.com/chujiezheng/chat_templates) - [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing We are grateful to the developers and contributors of these projects for their hard work and dedication. diff --git a/README.md.tpl b/README.md.tpl new file mode 100644 index 00000000..2bb38fc0 --- /dev/null +++ b/README.md.tpl @@ -0,0 +1,210 @@ +
+

🦾 OpenLLM: Self-Hosting LLMs Made Easy

+
+ +[![License: Apache-2.0](https://img.shields.io/badge/License-Apache%202-green.svg)](https://github.com/bentoml/OpenLLM/blob/main/LICENSE) +[![Releases](https://img.shields.io/pypi/v/openllm.svg?logo=pypi&label=PyPI&logoColor=gold)](https://pypi.org/project/openllm) +[![CI](https://results.pre-commit.ci/badge/github/bentoml/OpenLLM/main.svg)](https://results.pre-commit.ci/latest/github/bentoml/OpenLLM/main) +[![X](https://badgen.net/badge/icon/@bentomlai/000000?icon=twitter&label=Follow)](https://twitter.com/bentomlai) +[![Community](https://badgen.net/badge/icon/Community/562f5d?icon=slack&label=Join)](https://l.bentoml.com/join-slack) + +OpenLLM allows developers to run **any open-source LLMs** (Llama 3.3, Qwen2.5, Phi3 and [more](#supported-models)) or **custom models** as **OpenAI-compatible APIs** with a single command. It features a [built-in chat UI](#chat-ui), state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and [BentoCloud](#deploy-to-bentocloud). + +Understand the [design philosophy of OpenLLM](https://www.bentoml.com/blog/from-ollama-to-openllm-running-llms-in-the-cloud). + +## Get Started + +Run the following commands to install OpenLLM and explore it interactively. + +```bash +pip install openllm # or pip3 install openllm +openllm hello +``` + +![hello](https://github.com/user-attachments/assets/5af19f23-1b34-4c45-b1e0-a6798b4586d1) + +## Supported models + +OpenLLM supports a wide range of state-of-the-art open-source LLMs. You can also add a [model repository to run custom models](#set-up-a-custom-repository) with OpenLLM. + + + + + + + + + {%- for key, value in model_dict|items %} + + + + + + + {%- endfor %} +
ModelParametersRequired GPUStart a Server
{{key}}{{value['version'] | upper}}{{value['pretty_gpu']}}{{value['command']}}
+ +... + +For the full model list, see the [OpenLLM models repository](https://github.com/bentoml/openllm-models). + +## Start an LLM server + +To start an LLM server locally, use the `openllm serve` command and specify the model version. + +> [!NOTE] +> OpenLLM does not store model weights. A Hugging Face token (HF_TOKEN) is required for gated models. +> +> 1. Create your Hugging Face token [here](https://huggingface.co/settings/tokens). +> 2. Request access to the gated model, such as [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct). +> 3. Set your token as an environment variable by running: +> ```bash +> export HF_TOKEN= +> ``` + +```bash +openllm serve {{model_dict.get("llama3.2")["command"]}} +``` + +The server will be accessible at [http://localhost:3000](http://localhost:3000/), providing OpenAI-compatible APIs for interaction. You can call the endpoints with different frameworks and tools that support OpenAI-compatible APIs. Typically, you may need to specify the following: + +- **The API host address**: By default, the LLM is hosted at [http://localhost:3000](http://localhost:3000/). +- **The model name:** The name can be different depending on the tool you use. +- **The API key**: The API key used for client authentication. This is optional. + +Here are some examples: + +
+ +OpenAI Python client + +```python +from openai import OpenAI + +client = OpenAI(base_url='http://localhost:3000/v1', api_key='na') + +# Use the following func to get the available models +# model_list = client.models.list() +# print(model_list) + +chat_completion = client.chat.completions.create( + model="meta-llama/Llama-3.2-1B-Instruct", + messages=[ + { + "role": "user", + "content": "Explain superconductors like I'm five years old" + } + ], + stream=True, +) +for chunk in chat_completion: + print(chunk.choices[0].delta.content or "", end="") +``` + +
+ +
+ +LlamaIndex + +```python +from llama_index.llms.openai import OpenAI + +llm = OpenAI(api_bese="http://localhost:3000/v1", model="meta-llama/Llama-3.2-1B-Instruct", api_key="dummy") +... +``` + +
+ +## Chat UI + +OpenLLM provides a chat UI at the `/chat` endpoint for the launched LLM server at http://localhost:3000/chat. + +openllm_ui + +## Chat with a model in the CLI + +To start a chat conversation in the CLI, use the `openllm run` command and specify the model version. + +```bash +openllm run llama3:8b +``` + +## Model repository + +A model repository in OpenLLM represents a catalog of available LLMs that you can run. OpenLLM provides a default model repository that includes the latest open-source LLMs like Llama 3, Mistral, and Qwen2, hosted at [this GitHub repository](https://github.com/bentoml/openllm-models). To see all available models from the default and any added repository, use: + +```bash +openllm model list +``` + +To ensure your local list of models is synchronized with the latest updates from all connected repositories, run: + +```bash +openllm repo update +``` + +To review a model’s information, run: + +```bash +openllm model get {{model_dict.get("llama3.2")["command"]}} +``` + +### Add a model to the default model repository + +You can contribute to the default model repository by adding new models that others can use. This involves creating and submitting a Bento of the LLM. For more information, check out this [example pull request](https://github.com/bentoml/openllm-models/pull/1). + +### Set up a custom repository + +You can add your own repository to OpenLLM with custom models. To do so, follow the format in the default OpenLLM model repository with a `bentos` directory to store custom LLMs. You need to [build your Bentos with BentoML](https://docs.bentoml.com/en/latest/guides/build-options.html) and submit them to your model repository. + +First, prepare your custom models in a `bentos` directory following the guidelines provided by [BentoML to build Bentos](https://docs.bentoml.com/en/latest/guides/build-options.html). Check out the [default model repository](https://github.com/bentoml/openllm-repo) for an example and read the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) for details. + +Then, register your custom model repository with OpenLLM: + +```bash +openllm repo add +``` + +**Note**: Currently, OpenLLM only supports adding public repositories. + +## Deploy to BentoCloud + +OpenLLM supports LLM cloud deployment via BentoML, the unified model serving framework, and BentoCloud, an AI inference platform for enterprise AI teams. BentoCloud provides fully-managed infrastructure optimized for LLM inference with autoscaling, model orchestration, observability, and many more, allowing you to run any AI model in the cloud. + +[Sign up for BentoCloud](https://www.bentoml.com/) for free and [log in](https://docs.bentoml.com/en/latest/bentocloud/how-tos/manage-access-token.html). Then, run `openllm deploy` to deploy a model to BentoCloud: + +```bash +openllm deploy {{model_dict.get("llama3.2")["command"]}} +``` + +> [!NOTE] +> If you are deploying a gated model, make sure to set HF_TOKEN in enviroment variables. + +Once the deployment is complete, you can run model inference on the BentoCloud console: + +bentocloud_ui + +## Community + +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 + +As an open-source project, we welcome contributions of all kinds, such as new features, bug fixes, and documentation. Here are some of the ways to contribute: + +- Repost a bug by [creating a GitHub issue](https://github.com/bentoml/OpenLLM/issues/new/choose). +- [Submit a pull request](https://github.com/bentoml/OpenLLM/compare) or help review other developers’ [pull requests](https://github.com/bentoml/OpenLLM/pulls). +- Add an LLM to the OpenLLM default model repository so that other users can run your model. See the [pull request template](https://github.com/bentoml/openllm-models/pull/1). +- Check out the [Developer Guide](https://github.com/bentoml/OpenLLM/blob/main/DEVELOPMENT.md) to learn more. + +## Acknowledgements + +This project uses the following open-source projects: + +- [bentoml/bentoml](https://github.com/bentoml/bentoml) for production level model serving +- [vllm-project/vllm](https://github.com/vllm-project/vllm) for production level LLM backend +- [blrchen/chatgpt-lite](https://github.com/blrchen/chatgpt-lite) for a fancy Web Chat UI +- [astral-sh/uv](https://github.com/astral-sh/uv) for blazing fast model requirements installing + +We are grateful to the developers and contributors of these projects for their hard work and dedication. diff --git a/gen_readme.py b/gen_readme.py new file mode 100644 index 00000000..8e360078 --- /dev/null +++ b/gen_readme.py @@ -0,0 +1,22 @@ +# /// script +# requires-python = ">=3.11" +# dependencies = [ +# "jinja2", +# "uv", +# ] +# /// + +import subprocess, sys, pathlib, json + +from jinja2 import Environment, FileSystemLoader + +wd = pathlib.Path('.').parent +model_dict = subprocess.run( + [sys.executable, '-m', 'uv', 'run', '--with-editable', '.', 'openllm', 'model', 'list', '--output', 'readme'], + capture_output=True, + text=True, + check=True, +) +E = Environment(loader=FileSystemLoader('.')) +with (wd / 'README.md').open('w') as f: + f.write(E.get_template('README.md.tpl').render(model_dict=json.loads(model_dict.stdout.strip()))) diff --git a/src/openllm/__main__.py b/src/openllm/__main__.py index 1d50b51c..97c28980 100644 --- a/src/openllm/__main__.py +++ b/src/openllm/__main__.py @@ -230,7 +230,7 @@ def run( local_run(bento, port=port, timeout=timeout) -@app.command(help='deploy an production-ready OpenAI API compatible chat server to bentocloud ($100 free credit)') +@app.command(help='deploy production-ready OpenAI API-compatible server to BentoCloud') def deploy( model: Annotated[str, typer.Argument()] = '', instance_type: Optional[str] = None, diff --git a/src/openllm/model.py b/src/openllm/model.py index 89ddc057..e8c731e3 100644 --- a/src/openllm/model.py +++ b/src/openllm/model.py @@ -1,29 +1,33 @@ -import re -import typing -from typing import Optional +from __future__ import annotations -import tabulate -import typer +import re, typing, json + +import tabulate, questionary, typer from openllm.accelerator_spec import DeploymentTarget, can_run from openllm.analytic import OpenLLMTyper -from openllm.common import VERBOSE_LEVEL, BentoInfo, output +from openllm.common import VERBOSE_LEVEL, BentoInfo, output as output_ from openllm.repo import ensure_repo_updated, list_repo app = OpenLLMTyper(help='manage models') @app.command(help='get model') -def get(tag: str, repo: Optional[str] = None, verbose: bool = False): +def get(tag: str, repo: typing.Optional[str] = None, verbose: bool = False): if verbose: VERBOSE_LEVEL.set(20) bento_info = ensure_bento(tag, repo_name=repo) if bento_info: - output(bento_info) + output_(bento_info) @app.command(name='list', help='list available models') -def list_model(tag: Optional[str] = None, repo: Optional[str] = None, verbose: bool = False): +def list_model( + tag: typing.Optional[str] = None, + repo: typing.Optional[str] = None, + verbose: bool = False, + output: typing.Optional[str] = typer.Option(None, hidden=True), +): if verbose: VERBOSE_LEVEL.set(20) @@ -38,6 +42,23 @@ def list_model(tag: Optional[str] = None, repo: Optional[str] = None, verbose: b seen.add(value) return False + if output == 'readme': + # Parse parameters from bento.tag (e.g. "model:671b-it" -> "671b", 'model:something-long-78b' -> '78b') + version_pattern = re.compile(r'(\d+b|-[a-z]+b)') + questionary.print( + json.dumps({ + f'{bento.name}': dict( + tag=bento.tag, + version=version_pattern.search(bento.tag).group(1), + pretty_gpu=bento.pretty_gpu, + command=f'openllm serve {bento.tag}', + ) + for bento in bentos + if not is_seen(bento.name) and version_pattern.search(bento.tag) + }) + ) + return + table = tabulate.tabulate( [ [ @@ -51,19 +72,21 @@ def list_model(tag: Optional[str] = None, repo: Optional[str] = None, verbose: b ], headers=['model', 'version', 'repo', 'required GPU RAM', 'platforms'], ) - output(table) + output_(table) -def ensure_bento(model: str, target: Optional[DeploymentTarget] = None, repo_name: Optional[str] = None) -> BentoInfo: +def ensure_bento( + model: str, target: typing.Optional[DeploymentTarget] = None, repo_name: typing.Optional[str] = None +) -> BentoInfo: bentos = list_bento(model, repo_name=repo_name) if len(bentos) == 0: - output(f'No model found for {model}', style='red') + output_(f'No model found for {model}', style='red') raise typer.Exit(1) if len(bentos) == 1: - output(f'Found model {bentos[0]}', style='green') + output_(f'Found model {bentos[0]}', style='green') if target is not None and can_run(bentos[0], target) <= 0: - output( + output_( f'The machine({target.name}) with {target.accelerators_repr} does not appear to have sufficient ' f'resources to run model {bentos[0]}\n', style='yellow', @@ -71,7 +94,7 @@ def ensure_bento(model: str, target: Optional[DeploymentTarget] = None, repo_nam return bentos[0] # multiple models, pick one according to target - output(f'Multiple models match {model}, did you mean one of these?', style='red') + output_(f'Multiple models match {model}, did you mean one of these?', style='red') list_model(model, repo=repo_name) raise typer.Exit(1) @@ -99,9 +122,9 @@ def list_bento( if repo_name is not None: repo_map = {repo.name: repo for repo in repo_list} if repo_name not in repo_map: - output(f'Repo `{repo_name}` not found, did you mean one of these?') + output_(f'Repo `{repo_name}` not found, did you mean one of these?') for repo_name in repo_map: - output(f' {repo_name}') + output_(f' {repo_name}') raise typer.Exit(1) if not tag: diff --git a/uv.lock b/uv.lock new file mode 100644 index 00000000..7dd74c40 --- /dev/null +++ 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