
OpenLLM
Run any open-source LLMs, such as Llama, Mistral, as OpenAI compatible API endpoint in the cloud.
Stars: 10446

OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.
README:
OpenLLM allows developers to run any open-source LLMs (Llama 3.3, Qwen2.5, Phi3 and more) or custom models as OpenAI-compatible APIs with a single command. It features a built-in chat UI, state-of-the-art inference backends, and a simplified workflow for creating enterprise-grade cloud deployment with Docker, Kubernetes, and BentoCloud.
Understand the design philosophy of OpenLLM.
Run the following commands to install OpenLLM and explore it interactively.
pip install openllm # or pip3 install openllm
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 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 |
...
For the full model list, see the OpenLLM models repository.
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.
- Create your Hugging Face token here.
- Request access to the gated model, such as meta-llama/Meta-Llama-3-8B.
- Set your token as an environment variable by running:
export HF_TOKEN=<your token>
openllm serve llama3:8b
The server will be accessible at 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.
- 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
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/Meta-Llama-3-8B-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
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")
...
OpenLLM provides a chat UI at the /chat
endpoint for the launched LLM server at http://localhost:3000/chat.
To start a chat conversation in the CLI, use the openllm run
command and specify the model version.
openllm run llama3:8b
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. To see all available models from the default and any added repository, use:
openllm model list
To ensure your local list of models is synchronized with the latest updates from all connected repositories, run:
openllm repo update
To review a model’s information, run:
openllm model get llama3:8b
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.
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 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. Check out the default model repository for an example and read the Developer Guide for details.
Then, register your custom model repository with OpenLLM:
openllm repo add <repo-name> <repo-url>
Note: Currently, OpenLLM only supports adding public repositories.
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 for free and log in. Then, run openllm deploy
to deploy a model to BentoCloud:
openllm deploy llama3:8b
[!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:
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!
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.
- Submit a pull request or help review other developers’ pull requests.
- Add an LLM to the OpenLLM default model repository so that other users can run your model. See the pull request template.
- Check out the Developer Guide to learn more.
This project uses the following open-source projects:
- bentoml/bentoml for production level model serving
- vllm-project/vllm for production level LLM backend
- blrchen/chatgpt-lite for a fancy Web Chat UI
- chujiezheng/chat_templates
- 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.
For Tasks:
Click tags to check more tools for each tasksFor Jobs:
Alternative AI tools for OpenLLM
Similar Open Source Tools

OpenLLM
OpenLLM is a platform that helps developers run any open-source Large Language Models (LLMs) as OpenAI-compatible API endpoints, locally and in the cloud. It supports a wide range of LLMs, provides state-of-the-art serving and inference performance, and simplifies cloud deployment via BentoML. Users can fine-tune, serve, deploy, and monitor any LLMs with ease using OpenLLM. The platform also supports various quantization techniques, serving fine-tuning layers, and multiple runtime implementations. OpenLLM seamlessly integrates with other tools like OpenAI Compatible Endpoints, LlamaIndex, LangChain, and Transformers Agents. It offers deployment options through Docker containers, BentoCloud, and provides a community for collaboration and contributions.

axoned
Axone is a public dPoS layer 1 designed for connecting, sharing, and monetizing resources in the AI stack. It is an open network for collaborative AI workflow management compatible with any data, model, or infrastructure, allowing sharing of data, algorithms, storage, compute, APIs, both on-chain and off-chain. The 'axoned' node of the AXONE network is built on Cosmos SDK & Tendermint consensus, enabling companies & individuals to define on-chain rules, share off-chain resources, and create new applications. Validators secure the network by maintaining uptime and staking $AXONE for rewards. The blockchain supports various platforms and follows Semantic Versioning 2.0.0. A docker image is available for quick start, with documentation on querying networks, creating wallets, starting nodes, and joining networks. Development involves Go and Cosmos SDK, with smart contracts deployed on the AXONE blockchain. The project provides a Makefile for building, installing, linting, and testing. Community involvement is encouraged through Discord, open issues, and pull requests.

AgentLab
AgentLab is an open, easy-to-use, and extensible framework designed to accelerate web agent research. It provides features for developing and evaluating agents on various benchmarks supported by BrowserGym. The framework allows for large-scale parallel agent experiments using ray, building blocks for creating agents over BrowserGym, and a unified LLM API for OpenRouter, OpenAI, Azure, or self-hosted using TGI. AgentLab also offers reproducibility features, a unified LeaderBoard, and supports multiple benchmarks like WebArena, WorkArena, WebLinx, VisualWebArena, AssistantBench, GAIA, Mind2Web-live, and MiniWoB.

weblinx
WebLINX is a Python library and dataset for real-world website navigation with multi-turn dialogue. The repository provides code for training models reported in the WebLINX paper, along with a comprehensive API to work with the dataset. It includes modules for data processing, model evaluation, and utility functions. The modeling directory contains code for processing, training, and evaluating models such as DMR, LLaMA, MindAct, Pix2Act, and Flan-T5. Users can install specific dependencies for HTML processing, video processing, model evaluation, and library development. The evaluation module provides metrics and functions for evaluating models, with ongoing work to improve documentation and functionality.

llm-foundry
LLM Foundry is a codebase for training, finetuning, evaluating, and deploying LLMs for inference with Composer and the MosaicML platform. It is designed to be easy-to-use, efficient _and_ flexible, enabling rapid experimentation with the latest techniques. You'll find in this repo: * `llmfoundry/` - source code for models, datasets, callbacks, utilities, etc. * `scripts/` - scripts to run LLM workloads * `data_prep/` - convert text data from original sources to StreamingDataset format * `train/` - train or finetune HuggingFace and MPT models from 125M - 70B parameters * `train/benchmarking` - profile training throughput and MFU * `inference/` - convert models to HuggingFace or ONNX format, and generate responses * `inference/benchmarking` - profile inference latency and throughput * `eval/` - evaluate LLMs on academic (or custom) in-context-learning tasks * `mcli/` - launch any of these workloads using MCLI and the MosaicML platform * `TUTORIAL.md` - a deeper dive into the repo, example workflows, and FAQs

keras-llm-robot
The Keras-llm-robot Web UI project is an open-source tool designed for offline deployment and testing of various open-source models from the Hugging Face website. It allows users to combine multiple models through configuration to achieve functionalities like multimodal, RAG, Agent, and more. The project consists of three main interfaces: chat interface for language models, configuration interface for loading models, and tools & agent interface for auxiliary models. Users can interact with the language model through text, voice, and image inputs, and the tool supports features like model loading, quantization, fine-tuning, role-playing, code interpretation, speech recognition, image recognition, network search engine, and function calling.

kubeai
KubeAI is a highly scalable AI platform that runs on Kubernetes, serving as a drop-in replacement for OpenAI with API compatibility. It can operate OSS model servers like vLLM and Ollama, with zero dependencies and additional OSS addons included. Users can configure models via Kubernetes Custom Resources and interact with models through a chat UI. KubeAI supports serving various models like Llama v3.1, Gemma2, and Qwen2, and has plans for model caching, LoRA finetuning, and image generation.

open-assistant-api
Open Assistant API is an open-source, self-hosted AI intelligent assistant API compatible with the official OpenAI interface. It supports integration with more commercial and private models, R2R RAG engine, internet search, custom functions, built-in tools, code interpreter, multimodal support, LLM support, and message streaming output. Users can deploy the service locally and expand existing features. The API provides user isolation based on tokens for SaaS deployment requirements and allows integration of various tools to enhance its capability to connect with the external world.

LLM-Pruner
LLM-Pruner is a tool for structural pruning of large language models, allowing task-agnostic compression while retaining multi-task solving ability. It supports automatic structural pruning of various LLMs with minimal human effort. The tool is efficient, requiring only 3 minutes for pruning and 3 hours for post-training. Supported LLMs include Llama-3.1, Llama-3, Llama-2, LLaMA, BLOOM, Vicuna, and Baichuan. Updates include support for new LLMs like GQA and BLOOM, as well as fine-tuning results achieving high accuracy. The tool provides step-by-step instructions for pruning, post-training, and evaluation, along with a Gradio interface for text generation. Limitations include issues with generating repetitive or nonsensical tokens in compressed models and manual operations for certain models.

training-operator
Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, Tensorflow, XGBoost, MPI, Paddle and others. Training Operator allows you to use Kubernetes workloads to effectively train your large models via Kubernetes Custom Resources APIs or using Training Operator Python SDK. > Note: Before v1.2 release, Kubeflow Training Operator only supports TFJob on Kubernetes. * For a complete reference of the custom resource definitions, please refer to the API Definition. * TensorFlow API Definition * PyTorch API Definition * Apache MXNet API Definition * XGBoost API Definition * MPI API Definition * PaddlePaddle API Definition * For details of all-in-one operator design, please refer to the All-in-one Kubeflow Training Operator * For details on its observability, please refer to the monitoring design doc.

ludwig
Ludwig is a declarative deep learning framework designed for scale and efficiency. It is a low-code framework that allows users to build custom AI models like LLMs and other deep neural networks with ease. Ludwig offers features such as optimized scale and efficiency, expert level control, modularity, and extensibility. It is engineered for production with prebuilt Docker containers, support for running with Ray on Kubernetes, and the ability to export models to Torchscript and Triton. Ludwig is hosted by the Linux Foundation AI & Data.

exllamav2
ExLlamaV2 is an inference library for running local LLMs on modern consumer GPUs. It is a faster, better, and more versatile codebase than its predecessor, ExLlamaV1, with support for a new quant format called EXL2. EXL2 is based on the same optimization method as GPTQ and supports 2, 3, 4, 5, 6, and 8-bit quantization. It allows for mixing quantization levels within a model to achieve any average bitrate between 2 and 8 bits per weight. ExLlamaV2 can be installed from source, from a release with prebuilt extension, or from PyPI. It supports integration with TabbyAPI, ExUI, text-generation-webui, and lollms-webui. Key features of ExLlamaV2 include: - Faster and better kernels - Cleaner and more versatile codebase - Support for EXL2 quantization format - Integration with various web UIs and APIs - Community support on Discord

evalverse
Evalverse is an open-source project designed to support Large Language Model (LLM) evaluation needs. It provides a standardized and user-friendly solution for processing and managing LLM evaluations, catering to AI research engineers and scientists. Evalverse supports various evaluation methods, insightful reports, and no-code evaluation processes. Users can access unified evaluation with submodules, request evaluations without code via Slack bot, and obtain comprehensive reports with scores, rankings, and visuals. The tool allows for easy comparison of scores across different models and swift addition of new evaluation tools.

AgentBench
AgentBench is a benchmark designed to evaluate Large Language Models (LLMs) as autonomous agents in various environments. It includes 8 distinct environments such as Operating System, Database, Knowledge Graph, Digital Card Game, and Lateral Thinking Puzzles. The tool provides a comprehensive evaluation of LLMs' ability to operate as agents by offering Dev and Test sets for each environment. Users can quickly start using the tool by following the provided steps, configuring the agent, starting task servers, and assigning tasks. AgentBench aims to bridge the gap between LLMs' proficiency as agents and their practical usability.

OmAgent
OmAgent is an open-source agent framework designed to streamline the development of on-device multimodal agents. It enables agents to empower various hardware devices, integrates speed-optimized SOTA multimodal models, provides SOTA multimodal agent algorithms, and focuses on optimizing the end-to-end computing pipeline for real-time user interaction experience. Key features include easy connection to diverse devices, scalability, flexibility, and workflow orchestration. The architecture emphasizes graph-based workflow orchestration, native multimodality, and device-centricity, allowing developers to create bespoke intelligent agent programs.

SillyTavern
SillyTavern is a user interface you can install on your computer (and Android phones) that allows you to interact with text generation AIs and chat/roleplay with characters you or the community create. SillyTavern is a fork of TavernAI 1.2.8 which is under more active development and has added many major features. At this point, they can be thought of as completely independent programs.
For similar tasks

ai-on-gke
This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

ray
Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

labelbox-python
Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

djl
Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

mlflow
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are:
* `MLflow Tracking

tt-metal
TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

awsome-distributed-training
This repository contains reference architectures and test cases for distributed model training with Amazon SageMaker Hyperpod, AWS ParallelCluster, AWS Batch, and Amazon EKS. The test cases cover different types and sizes of models as well as different frameworks and parallel optimizations (Pytorch DDP/FSDP, MegatronLM, NemoMegatron...).
For similar jobs

weave
Weave is a toolkit for developing Generative AI applications, built by Weights & Biases. With Weave, you can log and debug language model inputs, outputs, and traces; build rigorous, apples-to-apples evaluations for language model use cases; and organize all the information generated across the LLM workflow, from experimentation to evaluations to production. Weave aims to bring rigor, best-practices, and composability to the inherently experimental process of developing Generative AI software, without introducing cognitive overhead.

LLMStack
LLMStack is a no-code platform for building generative AI agents, workflows, and chatbots. It allows users to connect their own data, internal tools, and GPT-powered models without any coding experience. LLMStack can be deployed to the cloud or on-premise and can be accessed via HTTP API or triggered from Slack or Discord.

VisionCraft
The VisionCraft API is a free API for using over 100 different AI models. From images to sound.

kaito
Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

PyRIT
PyRIT is an open access automation framework designed to empower security professionals and ML engineers to red team foundation models and their applications. It automates AI Red Teaming tasks to allow operators to focus on more complicated and time-consuming tasks and can also identify security harms such as misuse (e.g., malware generation, jailbreaking), and privacy harms (e.g., identity theft). The goal is to allow researchers to have a baseline of how well their model and entire inference pipeline is doing against different harm categories and to be able to compare that baseline to future iterations of their model. This allows them to have empirical data on how well their model is doing today, and detect any degradation of performance based on future improvements.

tabby
Tabby is a self-hosted AI coding assistant, offering an open-source and on-premises alternative to GitHub Copilot. It boasts several key features: * Self-contained, with no need for a DBMS or cloud service. * OpenAPI interface, easy to integrate with existing infrastructure (e.g Cloud IDE). * Supports consumer-grade GPUs.

spear
SPEAR (Simulator for Photorealistic Embodied AI Research) is a powerful tool for training embodied agents. It features 300 unique virtual indoor environments with 2,566 unique rooms and 17,234 unique objects that can be manipulated individually. Each environment is designed by a professional artist and features detailed geometry, photorealistic materials, and a unique floor plan and object layout. SPEAR is implemented as Unreal Engine assets and provides an OpenAI Gym interface for interacting with the environments via Python.

Magick
Magick is a groundbreaking visual AIDE (Artificial Intelligence Development Environment) for no-code data pipelines and multimodal agents. Magick can connect to other services and comes with nodes and templates well-suited for intelligent agents, chatbots, complex reasoning systems and realistic characters.