llmaz 0.0.1
☸️ Easy, advanced inference platform for large language models on Kubernetes
License: Apache-2.0
Requires Python: <4.0,>=3.10

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Easy, advanced inference platform for large language models on Kubernetes
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**llmaz** (pronounced `/lima:z/`), aims to provide a **Production-Ready** inference platform for large language models on Kubernetes. It closely integrates with the state-of-the-art inference backends to bring the leading-edge researches to cloud.

> 🌱 llmaz is alpha now, so API may change before graduating to Beta.

## Architecture

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## Features Overview

- **Easy of Use**: People can quick deploy a LLM service with minimal configurations.
- **Broad Backends Support**: llmaz supports a wide range of advanced inference backends for different scenarios, like [vLLM](https://github.com/vllm-project/vllm), [Text-Generation-Inference](https://github.com/huggingface/text-generation-inference), [SGLang](https://github.com/sgl-project/sglang), [llama.cpp](https://github.com/ggerganov/llama.cpp). Find the full list of supported backends [here](./docs/support-backends.md).
- **Model Distribution**: Out-of-the-box model cache system with [Manta](https://github.com/InftyAI/Manta).
- **Accelerator Fungibility**: llmaz supports serving the same LLM with various accelerators to optimize cost and performance.
- **SOTA Inference**: llmaz supports the latest cutting-edge researches like [Speculative Decoding](https://arxiv.org/abs/2211.17192) or [Splitwise](https://arxiv.org/abs/2311.18677)(WIP) to run on Kubernetes.
- **Various Model Providers**: llmaz supports a wide range of model providers, such as [HuggingFace](https://huggingface.co/), [ModelScope](https://www.modelscope.cn), ObjectStores. llmaz will automatically handle the model loading, requiring no effort from users.
- **Multi-hosts Support**: llmaz supports both single-host and multi-hosts scenarios with [LWS](https://github.com/kubernetes-sigs/lws) from day 0.
- **Scaling Efficiency (WIP)**: llmaz works smoothly with autoscaling components like [Cluster-Autoscaler](https://github.com/kubernetes/autoscaler/tree/master/cluster-autoscaler) or [Karpenter](https://github.com/kubernetes-sigs/karpenter) to meet elastic demands.

## Quick Start

### Installation

Read the [Installation](./docs/installation.md) for guidance.

### Deploy

Here's a toy example for deploying `facebook/opt-125m`, all you need to do
is to apply a `Model` and a `Playground`.

Please refer to **[examples](/docs/examples/README.md)** to learn more.

> Note: if your model needs Huggingface token for weight downloads, please run `kubectl create secret generic modelhub-secret --from-literal=HF_TOKEN=<your token>` ahead.

#### Model

```yaml
apiVersion: llmaz.io/v1alpha1
kind: OpenModel
metadata:
  name: opt-125m
spec:
  familyName: opt
  source:
    modelHub:
      modelID: facebook/opt-125m
  inferenceFlavors:
  - name: t4 # GPU type
    requests:
      nvidia.com/gpu: 1
```

#### Inference Playground

```yaml
apiVersion: inference.llmaz.io/v1alpha1
kind: Playground
metadata:
  name: opt-125m
spec:
  replicas: 1
  modelClaim:
    modelName: opt-125m
```

### Test

#### Expose the service

```cmd
kubectl port-forward pod/opt-125m-0 8080:8080
```

#### Get registered models

```cmd
curl http://localhost:8080/v1/models
```

#### Request a query

```cmd
curl http://localhost:8080/v1/completions \
-H "Content-Type: application/json" \
-d '{
    "model": "opt-125m",
    "prompt": "San Francisco is a",
    "max_tokens": 10,
    "temperature": 0
}'
```

### More than quick-start

If you want to learn more about this project, please refer to [develop.md](./docs/develop.md).

## Roadmap

- Gateway support for traffic routing
- Metrics support
- Serverless support for cloud-agnostic users
- CLI tool support
- Model training, fine tuning in the long-term


## Community

Join us for more discussions:

* **Slack Channel**: [#llmaz](https://inftyai.slack.com/archives/C06D0BGEQ1G)

## Contributions

All kinds of contributions are welcomed ! Please following [CONTRIBUTING.md](./CONTRIBUTING.md).