mirror of
https://github.com/mudler/LocalAI.git
synced 2025-01-05 12:24:10 +00:00
422 lines
17 KiB
Markdown
422 lines
17 KiB
Markdown
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+++
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disableToc = false
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title = "Getting started"
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weight = 1
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url = '/basics/getting_started/'
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+++
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`LocalAI` is available as a container image and binary. You can check out all the available images with corresponding tags [here](https://quay.io/repository/go-skynet/local-ai?tab=tags&tag=latest).
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### How to get started
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For a always up to date step by step how to of setting up LocalAI, Please see our [How to]({{%relref "howtos" %}}) page.
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### Fast Setup
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The easiest way to run LocalAI is by using [`docker compose`](https://docs.docker.com/compose/install/) or with [Docker](https://docs.docker.com/engine/install/) (to build locally, see the [build section]({{%relref "build" %}})). The following example uses `docker compose`:
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```bash
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI
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# (optional) Checkout a specific LocalAI tag
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# git checkout -b build <TAG>
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# copy your models to models/
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cp your-model.bin models/
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# (optional) Edit the .env file to set things like context size and threads
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# vim .env
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# start with docker compose
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docker compose up -d --pull always
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# or you can build the images with:
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# docker compose up -d --build
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"your-model.bin","object":"model"}]}
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curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
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"model": "your-model.bin",
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"prompt": "A long time ago in a galaxy far, far away",
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"temperature": 0.7
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}'
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```
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### Example: Use luna-ai-llama2 model with `docker compose`
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```bash
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# Clone LocalAI
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git clone https://github.com/go-skynet/LocalAI
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cd LocalAI
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# (optional) Checkout a specific LocalAI tag
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# git checkout -b build <TAG>
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# Download luna-ai-llama2 to models/
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wget https://huggingface.co/TheBloke/Luna-AI-Llama2-Uncensored-GGUF/resolve/main/luna-ai-llama2-uncensored.Q4_0.gguf -O models/luna-ai-llama2
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# Use a template from the examples
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cp -rf prompt-templates/getting_started.tmpl models/luna-ai-llama2.tmpl
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# (optional) Edit the .env file to set things like context size and threads
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# vim .env
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# start with docker compose
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docker compose up -d --pull always
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# or you can build the images with:
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# docker compose up -d --build
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# Now API is accessible at localhost:8080
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curl http://localhost:8080/v1/models
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# {"object":"list","data":[{"id":"luna-ai-llama2","object":"model"}]}
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "luna-ai-llama2",
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"messages": [{"role": "user", "content": "How are you?"}],
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"temperature": 0.9
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}'
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# {"model":"luna-ai-llama2","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}
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```
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{{% notice note %}}
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- If running on Apple Silicon (ARM) it is **not** suggested to run on Docker due to emulation. Follow the [build instructions]({{%relref "build" %}}) to use Metal acceleration for full GPU support.
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- If you are running Apple x86_64 you can use `docker`, there is no additional gain into building it from source.
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- If you are on Windows, please run ``docker-compose`` not ``docker compose`` and make sure the project is in the Linux Filesystem, otherwise loading models might be slow. For more Info: [Microsoft Docs](https://learn.microsoft.com/en-us/windows/wsl/filesystems)
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{{% /notice %}}
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### From binaries
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LocalAI binary releases are available in [Github](https://github.com/go-skynet/LocalAI/releases).
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You can control LocalAI with command line arguments, to specify a binding address, or the number of threads.
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<details>
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Usage:
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```
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local-ai --models-path <model_path> [--address <address>] [--threads <num_threads>]
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```
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| Parameter | Environmental Variable | Default Variable | Description |
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| ------------------------------ | ------------------------------- | -------------------------------------------------- | ------------------------------------------------------------------- |
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| --f16 | $F16 | false | Enable f16 mode |
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| --debug | $DEBUG | false | Enable debug mode |
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| --cors | $CORS | false | Enable CORS support |
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| --cors-allow-origins value | $CORS_ALLOW_ORIGINS | | Specify origins allowed for CORS |
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| --threads value | $THREADS | 4 | Number of threads to use for parallel computation |
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| --models-path value | $MODELS_PATH | ./models | Path to the directory containing models used for inferencing |
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| --preload-models value | $PRELOAD_MODELS | | List of models to preload in JSON format at startup |
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| --preload-models-config value | $PRELOAD_MODELS_CONFIG | | A config with a list of models to apply at startup. Specify the path to a YAML config file |
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| --config-file value | $CONFIG_FILE | | Path to the config file |
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| --address value | $ADDRESS | :8080 | Specify the bind address for the API server |
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| --image-path value | $IMAGE_PATH | | Path to the directory used to store generated images |
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| --context-size value | $CONTEXT_SIZE | 512 | Default context size of the model |
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| --upload-limit value | $UPLOAD_LIMIT | 15 | Default upload limit in megabytes (audio file upload) |
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| --galleries | $GALLERIES | | Allows to set galleries from command line |
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</details>
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### Docker
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LocalAI has a set of images to support CUDA, ffmpeg and 'vanilla' (CPU-only). The image list is on [quay](https://quay.io/repository/go-skynet/local-ai?tab=tags):
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- Vanilla images tags: `master`, `v1.40.0`, `latest`, ...
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- FFmpeg images tags: `master-ffmpeg`, `v1.40.0-ffmpeg`, ...
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- CUDA `11` tags: `master-cublas-cuda11`, `v1.40.0-cublas-cuda11`, ...
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- CUDA `12` tags: `master-cublas-cuda12`, `v1.40.0-cublas-cuda12`, ...
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- CUDA `11` + FFmpeg tags: `master-cublas-cuda11-ffmpeg`, `v1.40.0-cublas-cuda11-ffmpeg`, ...
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- CUDA `12` + FFmpeg tags: `master-cublas-cuda12-ffmpeg`, `v1.40.0-cublas-cuda12-ffmpeg`, ...
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Example:
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- Standard (GPT + `stablediffusion`): `quay.io/go-skynet/local-ai:latest`
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- FFmpeg: `quay.io/go-skynet/local-ai:v1.40.0-ffmpeg`
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- CUDA 11+FFmpeg: `quay.io/go-skynet/local-ai:v1.40.0-cublas-cuda11-ffmpeg`
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- CUDA 12+FFmpeg: `quay.io/go-skynet/local-ai:v1.40.0-cublas-cuda12-ffmpeg`
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Example of starting the API with `docker`:
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```bash
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docker run -p 8080:8080 -v $PWD/models:/models -ti --rm quay.io/go-skynet/local-ai:latest --models-path /models --context-size 700 --threads 4
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```
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You should see:
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```
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┌───────────────────────────────────────────────────┐
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│ Fiber v2.42.0 │
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│ http://127.0.0.1:8080 │
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│ (bound on host 0.0.0.0 and port 8080) │
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│ │
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│ Handlers ............. 1 Processes ........... 1 │
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│ Prefork ....... Disabled PID ................. 1 │
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└───────────────────────────────────────────────────┘
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```
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{{% notice note %}}
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Note: the binary inside the image is pre-compiled, and might not suite all CPUs.
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To enable CPU optimizations for the execution environment,
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the default behavior is to rebuild when starting the container.
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To disable this auto-rebuild behavior,
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set the environment variable `REBUILD` to `false`.
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See [docs on all environment variables]({{%relref "advanced#environment-variables" %}})
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for more info.
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{{% /notice %}}
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#### CUDA:
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Requirement: nvidia-container-toolkit (installation instructions [1](https://www.server-world.info/en/note?os=Ubuntu_22.04&p=nvidia&f=2) [2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html))
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You need to run the image with `--gpus all`, and
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```
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docker run --rm -ti --gpus all -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e PRELOAD_MODELS='[{"url": "github:go-skynet/model-gallery/openllama_7b.yaml", "name": "gpt-3.5-turbo", "overrides": { "f16":true, "gpu_layers": 35, "mmap": true, "batch": 512 } } ]' -e THREADS=1 -v $PWD/models:/models quay.io/go-skynet/local-ai:v1.40.0-cublas-cuda12
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```
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In the terminal where LocalAI was started, you should see:
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```
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5:13PM DBG Config overrides map[gpu_layers:10]
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5:13PM DBG Checking "open-llama-7b-q4_0.bin" exists and matches SHA
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5:13PM DBG Downloading "https://huggingface.co/SlyEcho/open_llama_7b_ggml/resolve/main/open-llama-7b-q4_0.bin"
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5:13PM DBG Downloading open-llama-7b-q4_0.bin: 393.4 MiB/3.5 GiB (10.88%) ETA: 40.965550709s
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5:13PM DBG Downloading open-llama-7b-q4_0.bin: 870.8 MiB/3.5 GiB (24.08%) ETA: 31.526866642s
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5:13PM DBG Downloading open-llama-7b-q4_0.bin: 1.3 GiB/3.5 GiB (36.26%) ETA: 26.37351405s
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5:13PM DBG Downloading open-llama-7b-q4_0.bin: 1.7 GiB/3.5 GiB (48.64%) ETA: 21.11682624s
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5:13PM DBG Downloading open-llama-7b-q4_0.bin: 2.2 GiB/3.5 GiB (61.49%) ETA: 15.656029361s
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5:14PM DBG Downloading open-llama-7b-q4_0.bin: 2.6 GiB/3.5 GiB (74.33%) ETA: 10.360950226s
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5:14PM DBG Downloading open-llama-7b-q4_0.bin: 3.1 GiB/3.5 GiB (87.05%) ETA: 5.205663978s
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5:14PM DBG Downloading open-llama-7b-q4_0.bin: 3.5 GiB/3.5 GiB (99.85%) ETA: 61.269714ms
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5:14PM DBG File "open-llama-7b-q4_0.bin" downloaded and verified
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5:14PM DBG Prompt template "openllama-completion" written
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5:14PM DBG Prompt template "openllama-chat" written
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5:14PM DBG Written config file /models/gpt-3.5-turbo.yaml
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```
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LocalAI will download automatically the OpenLLaMa model and run with GPU. Wait for the download to complete. You can also avoid automatic download of the model by not specifying a `PRELOAD_MODELS` variable. For compatible models with GPU support see the [model compatibility table]({{%relref "model-compatibility" %}}).
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To test that the API is working run in another terminal:
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```
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curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "What is an alpaca?"}],
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"temperature": 0.1
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}'
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```
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And if the GPU inferencing is working, you should be able to see something like:
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```
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5:22PM DBG Loading model in memory from file: /models/open-llama-7b-q4_0.bin
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ggml_init_cublas: found 1 CUDA devices:
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Device 0: Tesla T4
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llama.cpp: loading model from /models/open-llama-7b-q4_0.bin
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llama_model_load_internal: format = ggjt v3 (latest)
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llama_model_load_internal: n_vocab = 32000
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llama_model_load_internal: n_ctx = 1024
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llama_model_load_internal: n_embd = 4096
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llama_model_load_internal: n_mult = 256
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llama_model_load_internal: n_head = 32
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llama_model_load_internal: n_layer = 32
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llama_model_load_internal: n_rot = 128
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llama_model_load_internal: ftype = 2 (mostly Q4_0)
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llama_model_load_internal: n_ff = 11008
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llama_model_load_internal: n_parts = 1
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llama_model_load_internal: model size = 7B
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llama_model_load_internal: ggml ctx size = 0.07 MB
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llama_model_load_internal: using CUDA for GPU acceleration
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llama_model_load_internal: mem required = 4321.77 MB (+ 1026.00 MB per state)
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llama_model_load_internal: allocating batch_size x 1 MB = 512 MB VRAM for the scratch buffer
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llama_model_load_internal: offloading 10 repeating layers to GPU
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llama_model_load_internal: offloaded 10/35 layers to GPU
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llama_model_load_internal: total VRAM used: 1598 MB
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...................................................................................................
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llama_init_from_file: kv self size = 512.00 MB
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```
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{{% notice note %}}
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When enabling GPU inferencing, set the number of GPU layers to offload with: `gpu_layers: 1` to your YAML model config file and `f16: true`. You might also need to set `low_vram: true` if the device has low VRAM.
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{{% /notice %}}
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### Run LocalAI in Kubernetes
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LocalAI can be installed inside Kubernetes with helm.
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Requirements:
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- SSD storage class, or disable `mmap` to load the whole model in memory
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<details>
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By default, the helm chart will install LocalAI instance using the ggml-gpt4all-j model without persistent storage.
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1. Add the helm repo
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```bash
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helm repo add go-skynet https://go-skynet.github.io/helm-charts/
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```
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2. Install the helm chart:
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```bash
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helm repo update
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helm install local-ai go-skynet/local-ai -f values.yaml
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```
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> **Note:** For further configuration options, see the [helm chart repository on GitHub](https://github.com/go-skynet/helm-charts).
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### Example values
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Deploy a single LocalAI pod with 6GB of persistent storage serving up a `ggml-gpt4all-j` model with custom prompt.
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```yaml
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### values.yaml
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replicaCount: 1
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deployment:
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image: quay.io/go-skynet/local-ai:latest ##(This is for CPU only, to use GPU change it to a image that supports GPU IE "v1.40.0-cublas-cuda12")
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env:
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threads: 4
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context_size: 512
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modelsPath: "/models"
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resources:
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{}
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# We usually recommend not to specify default resources and to leave this as a conscious
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# choice for the user. This also increases chances charts run on environments with little
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# resources, such as Minikube. If you do want to specify resources, uncomment the following
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# lines, adjust them as necessary, and remove the curly braces after 'resources:'.
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# limits:
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# cpu: 100m
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# memory: 128Mi
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# requests:
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# cpu: 100m
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# memory: 128Mi
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# Prompt templates to include
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# Note: the keys of this map will be the names of the prompt template files
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promptTemplates:
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{}
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# ggml-gpt4all-j.tmpl: |
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# The prompt below is a question to answer, a task to complete, or a conversation to respond to; decide which and write an appropriate response.
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# ### Prompt:
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# {{.Input}}
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# ### Response:
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# Models to download at runtime
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models:
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# Whether to force download models even if they already exist
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forceDownload: false
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# The list of URLs to download models from
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# Note: the name of the file will be the name of the loaded model
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list:
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- url: "https://gpt4all.io/models/ggml-gpt4all-j.bin"
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# basicAuth: base64EncodedCredentials
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# Persistent storage for models and prompt templates.
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# PVC and HostPath are mutually exclusive. If both are enabled,
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# PVC configuration takes precedence. If neither are enabled, ephemeral
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# storage is used.
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persistence:
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pvc:
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enabled: false
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size: 6Gi
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accessModes:
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- ReadWriteOnce
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annotations: {}
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# Optional
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storageClass: ~
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hostPath:
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enabled: false
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path: "/models"
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service:
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type: ClusterIP
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port: 80
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annotations: {}
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# If using an AWS load balancer, you'll need to override the default 60s load balancer idle timeout
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# service.beta.kubernetes.io/aws-load-balancer-connection-idle-timeout: "1200"
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ingress:
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enabled: false
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className: ""
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annotations:
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{}
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# kubernetes.io/ingress.class: nginx
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# kubernetes.io/tls-acme: "true"
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hosts:
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- host: chart-example.local
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paths:
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- path: /
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pathType: ImplementationSpecific
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tls: []
|
||
|
# - secretName: chart-example-tls
|
||
|
# hosts:
|
||
|
# - chart-example.local
|
||
|
|
||
|
nodeSelector: {}
|
||
|
|
||
|
tolerations: []
|
||
|
|
||
|
affinity: {}
|
||
|
```
|
||
|
</details>
|
||
|
|
||
|
|
||
|
### Build from source
|
||
|
|
||
|
See the [build section]({{%relref "build" %}}).
|
||
|
|
||
|
### Other examples
|
||
|
|
||
|
![Screenshot from 2023-04-26 23-59-55](https://user-images.githubusercontent.com/2420543/234715439-98d12e03-d3ce-4f94-ab54-2b256808e05e.png)
|
||
|
|
||
|
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/).
|
||
|
|
||
|
|
||
|
### Clients
|
||
|
|
||
|
OpenAI clients are already compatible with LocalAI by overriding the basePath, or the target URL.
|
||
|
|
||
|
## Javascript
|
||
|
|
||
|
<details>
|
||
|
|
||
|
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);
|
||
|
```
|
||
|
|
||
|
</details>
|
||
|
|
||
|
## Python
|
||
|
|
||
|
<details>
|
||
|
|
||
|
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)
|
||
|
```
|
||
|
|
||
|
</details>
|