LocalAI

# copy your models to models/ cp your-model.bin models/ # (optional) Edit the .env file to set things like context size and threads # vim .env # start with docker-compose docker-compose up -d --build # Now API is accessible at localhost:8080 curl http://localhost:8080/v1/models # {"object":"list","data":[{"id":"your-model.bin","object":"model"}]} curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{ "model": "your-model.bin", "prompt": "A long time ago in a galaxy far, far away", "temperature": 0.7 }' ``` ### Example: Use GPT4ALL-J model

```bash # Clone LocalAI git clone https://github.com/go-skynet/LocalAI cd LocalAI # (optional) Checkout a specific LocalAI tag # git checkout -b build # Download gpt4all-j to models/ wget https://gpt4all.io/models/ggml-gpt4all-j.bin -O models/ggml-gpt4all-j # Use a template from the examples cp -rf prompt-templates/ggml-gpt4all-j.tmpl models/ # (optional) Edit the .env file to set things like context size and threads # vim .env # start with docker-compose docker-compose up -d --build # Now API is accessible at localhost:8080 curl http://localhost:8080/v1/models # {"object":"list","data":[{"id":"ggml-gpt4all-j","object":"model"}]} curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "ggml-gpt4all-j", "messages": [{"role": "user", "content": "How are you?"}], "temperature": 0.9 }' # {"model":"ggml-gpt4all-j","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]} ```

To build locally, run `make build` (see below). ## Other examples  To see other examples on how to integrate with other projects for instance chatbot-ui, see: [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/). ## Prompt templates The API doesn't inject a default prompt for talking to the model. You have to use a prompt similar to what's described in the standford-alpaca docs: https://github.com/tatsu-lab/stanford_alpaca#data-release.

You can use a default template for every model present in your model path, by creating a corresponding file with the `.tmpl` suffix next to your model. For instance, if the model is called `foo.bin`, you can create a sibling file, `foo.bin.tmpl` which will be used as a default prompt and can be used with alpaca: ``` The below instruction describes a task. Write a response that appropriately completes the request. ### Instruction: {{.Input}} ### Response: ``` See the [prompt-templates](https://github.com/go-skynet/LocalAI/tree/master/prompt-templates) directory in this repository for templates for some of the most popular models.

## Installation Currently LocalAI comes as container images and can be used with docker or a containre engine of choice. ### Run LocalAI in Kubernetes LocalAI can be installed inside Kubernetes with helm.

The local-ai Helm chart supports two options for the LocalAI server's models directory: 1. Basic deployment with no persistent volume. You must manually update the Deployment to configure your own models directory. Install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == false`. 2. Advanced, two-phase deployment to provision the models directory using a DataVolume. Requires [Containerized Data Importer CDI](https://github.com/kubevirt/containerized-data-importer) to be pre-installed in your cluster. First, install the chart with `.Values.deployment.volumes.enabled == false` and `.Values.dataVolume.enabled == true`: ```bash helm install local-ai charts/local-ai -n local-ai --create-namespace ``` Wait for CDI to create an importer Pod for the DataVolume and for the importer pod to finish provisioning the model archive inside the PV. Once the PV is provisioned and the importer Pod removed, set `.Values.deployment.volumes.enabled == true` and `.Values.dataVolume.enabled == false` and upgrade the chart: ```bash helm upgrade local-ai -n local-ai charts/local-ai ``` This will update the local-ai Deployment to mount the PV that was provisioned by the DataVolume.

## API `LocalAI` provides an API for running text generation as a service, that follows the OpenAI reference and can be used as a drop-in. The models once loaded the first time will be kept in memory.

Example of starting the API with `docker`: ```bash docker run -p 8080:8080 -ti --rm quay.io/go-skynet/local-ai:latest --models-path /path/to/models --context-size 700 --threads 4 ``` You should see: ``` ┌───────────────────────────────────────────────────┐ │ Fiber v2.42.0 │ │ http://127.0.0.1:8080 │ │ (bound on host 0.0.0.0 and port 8080) │ │ │ │ Handlers ............. 1 Processes ........... 1 │ │ Prefork ....... Disabled PID ................. 1 │ └───────────────────────────────────────────────────┘ ``` You can control the API server options with command line arguments: ``` local-api --models-path [--address

] [--threads ] ``` The API takes takes the following parameters: | Parameter | Environment Variable | Default Value | Description | | ------------ | -------------------- | ------------- | -------------------------------------- | | models-path | MODELS_PATH | | The path where you have models (ending with `.bin`). | | threads | THREADS | Number of Physical cores | The number of threads to use for text generation. | | address | ADDRESS | :8080 | The address and port to listen on. | | context-size | CONTEXT_SIZE | 512 | Default token context size. | | debug | DEBUG | false | Enable debug mode. | | config-file | CONFIG_FILE | empty | Path to a LocalAI config file. | Once the server is running, you can start making requests to it using HTTP, using the OpenAI API.

### Supported OpenAI API endpoints You can check out the [OpenAI API reference](https://platform.openai.com/docs/api-reference/chat/create). Following the list of endpoints/parameters supported. Note: - You can also specify the model as part of the OpenAI token. - If only one model is available, the API will use it for all the requests. #### Chat completions

For example, to generate a chat completion, you can send a POST request to the `/v1/chat/completions` endpoint with the instruction as the request body: ``` curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{ "model": "ggml-koala-7b-model-q4_0-r2.bin", "messages": [{"role": "user", "content": "Say this is a test!"}], "temperature": 0.7 }' ``` Available additional parameters: `top_p`, `top_k`, `max_tokens`

#### Completions

To generate a completion, you can send a POST request to the `/v1/completions` endpoint with the instruction as per the request body: ``` curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{ "model": "ggml-koala-7b-model-q4_0-r2.bin", "prompt": "A long time ago in a galaxy far, far away", "temperature": 0.7 }' ``` Available additional parameters: `top_p`, `top_k`, `max_tokens`

#### List models

You can list all the models available with: ``` curl http://localhost:8080/v1/models ```

## Advanced configuration LocalAI can be configured to serve user-defined models with a set of default parameters and templates.

You can create multiple `yaml` files in the models path or either specify a single YAML configuration file. For instance, a configuration file (`gpt-3.5-turbo.yaml`) can be declaring the "gpt-3.5-turbo" model but backed by the "testmodel" model file: ```yaml name: gpt-3.5-turbo parameters: model: testmodel context_size: 512 threads: 10 stopwords: - "HUMAN:" - "### Response:" roles: user: "HUMAN:" system: "GPT:" template: completion: completion chat: ggml-gpt4all-j ``` Specifying a `config-file` via CLI allows to declare models in a single file as a list, for instance: ```yaml - name: list1 parameters: model: testmodel context_size: 512 threads: 10 stopwords: - "HUMAN:" - "### Response:" roles: user: "HUMAN:" system: "GPT:" template: completion: completion chat: ggml-gpt4all-j - name: list2 parameters: model: testmodel context_size: 512 threads: 10 stopwords: - "HUMAN:" - "### Response:" roles: user: "HUMAN:" system: "GPT:" template: completion: completion chat: ggml-gpt4all-j ``` See also [chatbot-ui](https://github.com/go-skynet/LocalAI/tree/master/examples/chatbot-ui) as an example on how to use config files.

## Blog posts and other articles - https://medium.com/@tyler_97636/k8sgpt-localai-unlock-kubernetes-superpowers-for-free-584790de9b65 - https://kairos.io/docs/examples/localai/ ## Windows compatibility It should work, however you need to make sure you give enough resources to the container. See https://github.com/go-skynet/LocalAI/issues/2 ## Build locally Pre-built images might fit well for most of the modern hardware, however you can and might need to build the images manually. In order to build the `LocalAI` container image locally you can use `docker`: ``` # build the image docker build -t LocalAI . docker run LocalAI ``` Or build the binary with `make`: ``` make build ``` ## Frequently asked questions Here are answers to some of the most common questions. ### How do I get models?

Most ggml-based models should work, but newer models may require additions to the API. If a model doesn't work, please feel free to open up issues. However, be cautious about downloading models from the internet and directly onto your machine, as there may be security vulnerabilities in lama.cpp or ggml that could be maliciously exploited. Some models can be found on Hugging Face: https://huggingface.co/models?search=ggml, or models from gpt4all should also work: https://github.com/nomic-ai/gpt4all.

### What's the difference with Serge, or XXX?

LocalAI is a multi-model solution that doesn't focus on a specific model type (e.g., llama.cpp or alpaca.cpp), and it handles all of these internally for faster inference, easy to set up locally and deploy to Kubernetes.

### Can I use it with a Discord bot, or XXX?

Yes! If the client uses OpenAI and supports setting a different base URL to send requests to, you can use the LocalAI endpoint. This allows to use this with every application that was supposed to work with OpenAI, but without changing the application!

### Can this leverage GPUs?

Not currently, as ggml doesn't support GPUs yet: https://github.com/ggerganov/llama.cpp/discussions/915.

### Where is the webUI?

We are working on to have a good out of the box experience - however as LocalAI is an API you can already plug it into existing projects that provides are UI interfaces to OpenAI's APIs. There are several already on github, and should be compatible with LocalAI already (as it mimics the OpenAI API)

### Does it work with AutoGPT?

AutoGPT currently doesn't allow to set a different API URL, but there is a PR open for it, so this should be possible soon!

## Projects already using LocalAI to run local models Feel free to open up a PR to get your project listed! - [Kairos](https://github.com/kairos-io/kairos) - [k8sgpt](https://github.com/k8sgpt-ai/k8sgpt#running-local-models) ## Short-term roadmap - [x] Mimic OpenAI API (https://github.com/go-skynet/LocalAI/issues/10) - [ ] Binary releases (https://github.com/go-skynet/LocalAI/issues/6) - [ ] Upstream our golang bindings to llama.cpp (https://github.com/ggerganov/llama.cpp/issues/351) and [gpt4all](https://github.com/go-skynet/LocalAI/issues/85) - [x] Multi-model support - [x] Have a webUI! - [x] Allow configuration of defaults for models. - [ ] Enable automatic downloading of models from a curated gallery, with only free-licensed models, directly from the webui. ## Star history [](https://star-history.com/#go-skynet/LocalAI&Date) ## License LocalAI is a community-driven project. It was initially created by [mudler](https://github.com/mudler/) at the [SpectroCloud OSS Office](https://github.com/spectrocloud). MIT ## Acknowledgements - [llama.cpp](https://github.com/ggerganov/llama.cpp) - https://github.com/tatsu-lab/stanford_alpaca - https://github.com/cornelk/llama-go for the initial ideas - https://github.com/antimatter15/alpaca.cpp for the light model version (this is compatible and tested only with that checkpoint model!) ## Contributors