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 --pull always # or you can build the images with: # 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 --pull always # or you can build the images with: # 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?"}}]} ```

### Advanced: prepare models using the API Instead of installing models manually, you can use the LocalAI API endpoints and a model definition to install programmatically via API models in runtime.

A curated collection of model files is in the [model-gallery](https://github.com/go-skynet/model-gallery) (work in progress!). To install for example `gpt4all-j`, you can send a POST call to the `/models/apply` endpoint with the model definition url (`url`) and the name of the model should have in LocalAI (`name`, optional): ``` curl http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{ "url": "https://raw.githubusercontent.com/go-skynet/model-gallery/main/gpt4all-j.yaml", "name": "gpt4all-j" }' ```

### Other examples  To see other examples on how to integrate with other projects for instance for question answering or for using it with chatbot-ui, see: [examples](https://github.com/go-skynet/LocalAI/tree/master/examples/). ### 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. Consider the following `models` folder in the `example/chatbot-ui`: ``` base ❯ ls -liah examples/chatbot-ui/models 36487587 drwxr-xr-x 2 mudler mudler 4.0K May 3 12:27 . 36487586 drwxr-xr-x 3 mudler mudler 4.0K May 3 10:42 .. 36465214 -rw-r--r-- 1 mudler mudler 10 Apr 27 07:46 completion.tmpl 36464855 -rw-r--r-- 1 mudler mudler 3.6G Apr 27 00:08 ggml-gpt4all-j 36464537 -rw-r--r-- 1 mudler mudler 245 May 3 10:42 gpt-3.5-turbo.yaml 36467388 -rw-r--r-- 1 mudler mudler 180 Apr 27 07:46 gpt4all.tmpl ``` In the `gpt-3.5-turbo.yaml` file it is defined the `gpt-3.5-turbo` model which is an alias to use `gpt4all-j` with pre-defined options. For instance, consider the following that declares `gpt-3.5-turbo` backed by the `ggml-gpt4all-j` model: ```yaml name: gpt-3.5-turbo # Default model parameters parameters: # Relative to the models path model: ggml-gpt4all-j # temperature temperature: 0.3 # all the OpenAI request options here.. # Default context size context_size: 512 threads: 10 # Define a backend (optional). By default it will try to guess the backend the first time the model is interacted with. backend: gptj # available: llama, stablelm, gpt2, gptj rwkv # stopwords (if supported by the backend) stopwords: - "HUMAN:" - "### Response:" # define chat roles roles: user: "HUMAN:" system: "GPT:" template: # template file ".tmpl" with the prompt template to use by default on the endpoint call. Note there is no extension in the files 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. ### Full config model file reference ```yaml name: gpt-3.5-turbo # Default model parameters parameters: # Relative to the models path model: ggml-gpt4all-j # temperature temperature: 0.3 # all the OpenAI request options here.. top_k: top_p: max_tokens: batch: f16: true ignore_eos: true n_keep: 10 seed: mode: step: # Default context size context_size: 512 # Default number of threads threads: 10 # Define a backend (optional). By default it will try to guess the backend the first time the model is interacted with. backend: gptj # available: llama, stablelm, gpt2, gptj rwkv # stopwords (if supported by the backend) stopwords: - "HUMAN:" - "### Response:" # string to trim space to trimspace: - string # Strings to cut from the response cutstrings: - "string" # define chat roles roles: user: "HUMAN:" system: "GPT:" assistant: "ASSISTANT:" template: # template file ".tmpl" with the prompt template to use by default on the endpoint call. Note there is no extension in the files completion: completion chat: ggml-gpt4all-j edit: edit_template # Enable F16 if backend supports it f16: true # Enable debugging debug: true # Enable embeddings embeddings: true # Mirostat configuration (llama.cpp only) mirostat_eta: 0.8 mirostat_tau: 0.9 mirostat: 1 # GPU Layers (only used when built with cublas) gpu_layers: 22 # Directory used to store additional assets (used for stablediffusion) asset_dir: "" ```

### 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. For the edit endpoint, an example template for alpaca-based models can be: ```yaml Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request. ### Instruction: {{.Instruction}} ### Input: {{.Input}} ### Response: ```

### CLI You can control LocalAI with command line arguments, to specify a binding address, or the number of threads.

Usage: ``` local-ai --models-path [--address

] [--threads ] ``` | 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. | | upload_limit | UPLOAD_LIMIT | 5MB | Upload limit for whisper. | | image-path | IMAGE_PATH | empty | Image directory to store and serve processed images. |

## Setup Currently LocalAI comes as a container image and can be used with docker or a container engine of choice. You can check out all the available images with corresponding tags [here](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest). ### Docker

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 │ └───────────────────────────────────────────────────┘ ``` Note: the binary inside the image is rebuild at the start of the container to enable CPU optimizations for the execution environment, you can set the environment variable `REBUILD` to `false` to prevent this behavior.

### Build locally

In order to build the `LocalAI` container image locally you can use `docker`: ``` # build the image docker build -t LocalAI . docker run LocalAI ``` Or you can build the binary with `make`: ``` make build ```

### Build on mac Building on Mac (M1 or M2) works, but you may need to install some prerequisites using `brew`.

The below has been tested by one mac user and found to work. Note that this doesn't use docker to run the server: ``` # install build dependencies brew install cmake brew install go # clone the repo git clone https://github.com/go-skynet/LocalAI.git cd LocalAI # build the binary make 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/ # Run LocalAI ./local-ai --models-path ./models/ --debug # Now API is accessible at localhost:8080 curl http://localhost:8080/v1/models 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 }' ```

### Build with Image generation support

**Requirements**: OpenCV, Gomp Image generation is experimental and requires `GO_TAGS=stablediffusion` to be set during build: ``` make GO_TAGS=stablediffusion rebuild ```

### Accelleration #### OpenBLAS

Requirements: OpenBLAS ``` make BUILD_TYPE=openblas build ```

#### CuBLAS

Requirement: Nvidia CUDA toolkit Note: CuBLAS support is experimental, and has not been tested on real HW. please report any issues you find! ``` make BUILD_TYPE=cublas build ``` More informations available in the upstream PR: https://github.com/ggerganov/llama.cpp/pull/1412

### 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 ### Run LocalAI in Kubernetes LocalAI can be installed inside Kubernetes with helm.

1. Add the helm repo ```bash helm repo add go-skynet https://go-skynet.github.io/helm-charts/ ``` 1. Create a values files with your settings: ```bash cat < values.yaml deployment: image: quay.io/go-skynet/local-ai:latest env: threads: 4 contextSize: 1024 modelsPath: "/models" # Optionally create a PVC, mount the PV to the LocalAI Deployment, # and download a model to prepopulate the models directory modelsVolume: enabled: true url: "https://gpt4all.io/models/ggml-gpt4all-j.bin" pvc: size: 6Gi accessModes: - ReadWriteOnce auth: # Optional value for HTTP basic access authentication header basic: "" # 'username:password' base64 encoded service: type: ClusterIP annotations: {} # If using an AWS load balancer, you'll need to override the default 60s load balancer idle timeout # service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "1200" EOF ``` 3. Install the helm chart: ```bash helm repo update helm install local-ai go-skynet/local-ai -f values.yaml ``` Check out also the [helm chart repository on GitHub](https://github.com/go-skynet/helm-charts).

## 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`

### Edit completions

To generate an edit completion you can send a POST request to the `/v1/edits` endpoint with the instruction as the request body: ``` curl http://localhost:8080/v1/edits -H "Content-Type: application/json" -d '{ "model": "ggml-koala-7b-model-q4_0-r2.bin", "instruction": "rephrase", "input": "Black cat jumped out of the window", "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 ```

### Embeddings OpenAI docs: https://platform.openai.com/docs/api-reference/embeddings

The embedding endpoint is experimental and enabled only if the model is configured with `embeddings: true` in its `yaml` file, for example: ```yaml name: text-embedding-ada-002 parameters: model: bert embeddings: true backend: "bert-embeddings" ``` There is an example available [here](https://github.com/go-skynet/LocalAI/tree/master/examples/query_data/). Note: embeddings is supported only with `llama.cpp` compatible models and `bert` models. bert is more performant and available independently of the LLM model.

### Transcriptions endpoint

Note: requires ffmpeg in the container image, which is currently not shipped due to licensing issues. We will prepare separated images with ffmpeg. (stay tuned!) Download one of the models from https://huggingface.co/ggerganov/whisper.cpp/tree/main in the `models` folder, and create a YAML file for your model: ```yaml name: whisper-1 backend: whisper parameters: model: whisper-en ``` The transcriptions endpoint then can be tested like so: ``` wget --quiet --show-progress -O gb1.ogg https://upload.wikimedia.org/wikipedia/commons/1/1f/George_W_Bush_Columbia_FINAL.ogg curl http://localhost:8080/v1/audio/transcriptions -H "Content-Type: multipart/form-data" -F file="@$PWD/gb1.ogg" -F model="whisper-1" {"text":"My fellow Americans, this day has brought terrible news and great sadness to our country.At nine o'clock this morning, Mission Control in Houston lost contact with our Space ShuttleColumbia.A short time later, debris was seen falling from the skies above Texas.The Columbia's lost.There are no survivors.One board was a crew of seven.Colonel Rick Husband, Lieutenant Colonel Michael Anderson, Commander Laurel Clark, Captain DavidBrown, Commander William McCool, Dr. Kultna Shavla, and Elon Ramon, a colonel in the IsraeliAir Force.These men and women assumed great risk in the service to all humanity.In an age when spaceflight has come to seem almost routine, it is easy to overlook thedangers of travel by rocket and the difficulties of navigating the fierce outer atmosphere ofthe Earth.These astronauts knew the dangers, and they faced them willingly, knowing they had a highand noble purpose in life.Because of their courage and daring and idealism, we will miss them all the more.All Americans today are thinking as well of the families of these men and women who havebeen given this sudden shock and grief.You're not alone.Our entire nation agrees with you, and those you loved will always have the respect andgratitude of this country.The cause in which they died will continue.Mankind has led into the darkness beyond our world by the inspiration of discovery andthe longing to understand.Our journey into space will go on.In the skies today, we saw destruction and tragedy.As farther than we can see, there is comfort and hope.In the words of the prophet Isaiah, \"Lift your eyes and look to the heavens who createdall these, he who brings out the starry hosts one by one and calls them each by name.\"Because of his great power and mighty strength, not one of them is missing.The same creator who names the stars also knows the names of the seven souls we mourntoday.The crew of the shuttle Columbia did not return safely to Earth yet we can pray that all aresafely home.May God bless the grieving families and may God continue to bless America.[BLANK_AUDIO]"} ```

### Image generation OpenAI docs: https://platform.openai.com/docs/api-reference/images/create LocalAI supports generating images with Stable diffusion, running on CPU. | mode=0 | mode=1 (winograd/sgemm) | |------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------| |  |  | |  |  | |  |  |

To generate an image you can send a POST request to the `/v1/images/generations` endpoint with the instruction as the request body: ```bash # 512x512 is supported too curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{ "prompt": "A cute baby sea otter", "size": "256x256" }' ``` Available additional parameters: `mode`, `step`. Note: To set a negative prompt, you can split the prompt with `|`, for instance: `a cute baby sea otter|malformed`. ```bash curl http://localhost:8080/v1/images/generations -H "Content-Type: application/json" -d '{ "prompt": "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text", "size": "256x256" }' ``` Note: image generator supports images up to 512x512. You can use other tools however to upscale the image, for instance: https://github.com/upscayl/upscayl. #### Setup Note: In order to use the `images/generation` endpoint, you need to build LocalAI with `GO_TAGS=stablediffusion`. 1. Create a model file `stablediffusion.yaml` in the models folder: ```yaml name: stablediffusion backend: stablediffusion asset_dir: stablediffusion_assets ``` 2. Create a `stablediffusion_assets` directory inside your `models` directory 3. Download the ncnn assets from https://github.com/EdVince/Stable-Diffusion-NCNN#out-of-box and place them in `stablediffusion_assets`. The models directory should look like the following: ``` models ├── stablediffusion_assets │   ├── AutoencoderKL-256-256-fp16-opt.param │   ├── AutoencoderKL-512-512-fp16-opt.param │   ├── AutoencoderKL-base-fp16.param │   ├── AutoencoderKL-encoder-512-512-fp16.bin │   ├── AutoencoderKL-fp16.bin │   ├── FrozenCLIPEmbedder-fp16.bin │   ├── FrozenCLIPEmbedder-fp16.param │   ├── log_sigmas.bin │   ├── tmp-AutoencoderKL-encoder-256-256-fp16.param │   ├── UNetModel-256-256-MHA-fp16-opt.param │   ├── UNetModel-512-512-MHA-fp16-opt.param │   ├── UNetModel-base-MHA-fp16.param │   ├── UNetModel-MHA-fp16.bin │   └── vocab.txt └── stablediffusion.yaml ```

## LocalAI API endpoints Besides the OpenAI endpoints, there are additional LocalAI-only API endpoints. ### Applying a model - `/models/apply` This endpoint can be used to install a model in runtime.

LocalAI will create a batch process that downloads the required files from a model definition and automatically reload itself to include the new model. Input: `url`, `name` (optional), `files` (optional) ```bash curl http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{ "url": "", "name": "", "files": [ { "uri": "", "sha256": "", "name": "" } ] } ``` An optional, list of additional files can be specified to be downloaded. The `name` allows to override the model name. Returns an `uuid` and an `url` to follow up the state of the process: ```json { "uid":"251475c9-f666-11ed-95e0-9a8a4480ac58", "status":"http://localhost:8080/models/jobs/251475c9-f666-11ed-95e0-9a8a4480ac58"} ``` To see a collection example of curated models definition files, see the [model-gallery](https://github.com/go-skynet/model-gallery).

### Inquiry model job state `/models/jobs/` This endpoint returns the state of the batch job associated to a model

This endpoint can be used with the uuid returned by `/models/apply` to check a job state: ```bash curl http://localhost:8080/models/jobs/251475c9-f666-11ed-95e0-9a8a4480ac58 ``` Returns a json containing the error, and if the job is being processed: ```json {"error":null,"processed":true,"message":"completed"} ```

## Clients OpenAI clients are already compatible with LocalAI by overriding the basePath, or the target URL. ## Javascript

https://github.com/openai/openai-node/ ```javascript import { Configuration, OpenAIApi } from 'openai'; const configuration = new Configuration({ basePath: `http://localhost:8080/v1` }); const openai = new OpenAIApi(configuration); ```

## Python

https://github.com/openai/openai-python Set the `OPENAI_API_BASE` environment variable, or by code: ```python import openai openai.api_base = "http://localhost:8080/v1" # create a chat completion chat_completion = openai.ChatCompletion.create(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hello world"}]) # print the completion print(completion.choices[0].message.content) ```

## 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?

There is partial GPU support, see build instructions above.

### Where is the webUI?

There is the availability of localai-webui and chatbot-ui in the examples section and can be setup as per the instructions. 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) - [Spark](https://github.com/cedriking/spark) ## Blog posts and other articles - https://medium.com/@tyler_97636/k8sgpt-localai-unlock-kubernetes-superpowers-for-free-584790de9b65 - https://kairos.io/docs/examples/localai/ ## 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. - [x] Support for embeddings - [x] Support for audio transcription with https://github.com/ggerganov/whisper.cpp - [ ] GPU/CUDA support ( https://github.com/go-skynet/LocalAI/issues/69 ) - [ ] 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 [Ettore Di Giacinto](https://github.com/mudler/) at the [SpectroCloud OSS Office](https://github.com/spectrocloud). MIT ## Golang bindings used - [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp) - [go-skynet/go-gpt4all-j.cpp](https://github.com/go-skynet/go-gpt4all-j.cpp) - [go-skynet/go-gpt2.cpp](https://github.com/go-skynet/go-gpt2.cpp) - [go-skynet/go-bert.cpp](https://github.com/go-skynet/go-bert.cpp) - [donomii/go-rwkv.cpp](https://github.com/donomii/go-rwkv.cpp) ## Acknowledgements LocalAI couldn't have been built without the help of great software already available from the community. Thank you! - [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 - https://github.com/EdVince/Stable-Diffusion-NCNN - https://github.com/ggerganov/whisper.cpp - https://github.com/saharNooby/rwkv.cpp ## Contributors