Getting started

LocalAI is available as a container image and binary. It can be used with docker, podman, kubernetes and any container engine. You can check out all the available images with corresponding tags here.

See also our How to section for end-to-end guided examples curated by the community.

How to get started

The easiest way to run LocalAI is by using docker compose or with Docker (to build locally, see the build section).

# Prepare the models into the `model` directory
mkdir models

# copy your models to it
cp your-model.gguf models/

# run the LocalAI container
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
# 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 │
# └───────────────────────────────────────────────────┘

# Try the endpoint with curl
curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "your-model.gguf",
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

# Clone LocalAI
git clone https://github.com/go-skynet/LocalAI

cd LocalAI

# (optional) Checkout a specific LocalAI tag
# git checkout -b build <TAG>

# copy your models to models/
cp your-model.gguf 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.gguf","object":"model"}]}

curl http://localhost:8080/v1/completions -H "Content-Type: application/json" -d '{
     "model": "your-model.gguf",
     "prompt": "A long time ago in a galaxy far, far away",
     "temperature": 0.7
   }'

# Install the helm repository
helm repo add go-skynet https://go-skynet.github.io/helm-charts/
# Update the repositories
helm repo update
# Get the values
helm show values go-skynet/local-ai > values.yaml

# Edit the values value if needed
# vim values.yaml ...

# Install the helm chart
helm install local-ai go-skynet/local-ai -f values.yaml

Container images

LocalAI has a set of images to support CUDA, ffmpeg and ‘vanilla’ (CPU-only). The image list is on quay:

Example:

• Standard (GPT + stablediffusion): quay.io/go-skynet/local-ai:latest
• FFmpeg: quay.io/go-skynet/local-ai:v2.0.0-ffmpeg
• CUDA 11+FFmpeg: quay.io/go-skynet/local-ai:v2.0.0-cublas-cuda11-ffmpeg
• CUDA 12+FFmpeg: quay.io/go-skynet/local-ai:v2.0.0-cublas-cuda12-ffmpeg

Example: Use luna-ai-llama2 model with docker

mkdir models

# Download luna-ai-llama2 to models/
wget https://huggingface.co/TheBloke/Luna-AI-Llama2-Uncensored-GGUF/resolve/main/luna-ai-llama2-uncensored.Q4_0.gguf -O models/luna-ai-llama2

# Use a template from the examples
cp -rf prompt-templates/getting_started.tmpl models/luna-ai-llama2.tmpl

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

# Now API is accessible at localhost:8080
curl http://localhost:8080/v1/models
# {"object":"list","data":[{"id":"luna-ai-llama2","object":"model"}]}

curl http://localhost:8080/v1/chat/completions -H "Content-Type: application/json" -d '{
     "model": "luna-ai-llama2",
     "messages": [{"role": "user", "content": "How are you?"}],
     "temperature": 0.9
   }'

# {"model":"luna-ai-llama2","choices":[{"message":{"role":"assistant","content":"I'm doing well, thanks. How about you?"}}]}

To see other model configurations, see also the example section here.

From binaries

LocalAI binary releases are available in Github.

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

CLI parameters

Run LocalAI in Kubernetes

LocalAI can be installed inside Kubernetes with helm.

Requirements:

• SSD storage class, or disable mmap to load the whole model in memory

### values.yaml

replicaCount: 1

deployment:
  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 "v2.0.0-cublas-cuda12-core")
  env:
    threads: 4
    context_size: 512
  modelsPath: "/models"

resources:
  {}
  # We usually recommend not to specify default resources and to leave this as a conscious
  # choice for the user. This also increases chances charts run on environments with little
  # resources, such as Minikube. If you do want to specify resources, uncomment the following
  # lines, adjust them as necessary, and remove the curly braces after 'resources:'.
  # limits:
  #   cpu: 100m
  #   memory: 128Mi
  # requests:
  #   cpu: 100m
  #   memory: 128Mi

# Prompt templates to include
# Note: the keys of this map will be the names of the prompt template files
promptTemplates:
  {}
  # ggml-gpt4all-j.tmpl: |
  #   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.
  #   ### Prompt:
  #   {{.Input}}
  #   ### Response:

# Models to download at runtime
models:
  # Whether to force download models even if they already exist
  forceDownload: false

  # The list of URLs to download models from
  # Note: the name of the file will be the name of the loaded model
  list:
  - url: "https://gpt4all.io/models/ggml-gpt4all-j.bin"
      # basicAuth: base64EncodedCredentials

  # Persistent storage for models and prompt templates.
  # PVC and HostPath are mutually exclusive. If both are enabled,
  # PVC configuration takes precedence. If neither are enabled, ephemeral
  # storage is used.
  persistence:
    pvc:
      enabled: false
      size: 6Gi
      accessModes:
        - ReadWriteOnce

      annotations: {}

      # Optional
      storageClass: ~

    hostPath:
      enabled: false
      path: "/models"

service:
  type: ClusterIP
  port: 80
  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"

ingress:
  enabled: false
  className: ""
  annotations:
    {}
    # kubernetes.io/ingress.class: nginx
    # kubernetes.io/tls-acme: "true"
  hosts:
    - host: chart-example.local
      paths:
        - path: /
          pathType: ImplementationSpecific
  tls: []
  #  - secretName: chart-example-tls
  #    hosts:
  #      - chart-example.local

nodeSelector: {}

tolerations: []

affinity: {}

Build from source

See the build section.

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.

Clients

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

Javascript

import { Configuration, OpenAIApi } from 'openai';

const configuration = new Configuration({
  basePath: `http://localhost:8080/v1`
});
const openai = new OpenAIApi(configuration);

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)

🆕 What's New

04-12-2023: v2.0.0

This release brings a major overhaul in some backends.

Breaking/important changes:

• Backend rename: llama-stable renamed to llama-ggml 1287
• Prompt template changes: 1254 (extra space in roles)
• Apple metal bugfixes: 1365

New:

• Added support for LLaVa and OpenAI Vision API support ( 1254 )
• Python based backends are now using conda to track env dependencies ( 1144 )
• Support for parallel requests ( 1290 )
• Support for transformers-embeddings ( 1308 )
• Watchdog for backends ( 1341 ). As https://github.com/ggerganov/llama.cpp/issues/3969 is hitting LocalAI’s llama-cpp implementation, we have now a watchdog that can be used to make sure backends are not stalling. This is a generic mechanism that can be enabled for all the backends now.
• Whisper.cpp updates ( 1302 )
• Petals backend ( 1350 )
• Full LLM fine-tuning example to use with LocalAI: https://localai.io/advanced/fine-tuning/

Due to the python dependencies size of images grew in size. If you still want to use smaller images without python dependencies, you can use the corresponding images tags ending with -core.

Full changelog: https://github.com/mudler/LocalAI/releases/tag/v2.0.0

30-10-2023: v1.40.0

This release is a preparation before v2 - the efforts now will be to refactor, polish and add new backends. Follow up on: https://github.com/mudler/LocalAI/issues/1126

Hot topics

This release now brings the llama-cpp backend which is a c++ backend tied to llama.cpp. It follows more closely and tracks recent versions of llama.cpp. It is not feature compatible with the current llama backend but plans are to sunset the current llama backend in favor of this one. This one will be probably be the latest release containing the older llama backend written in go and c++. The major improvement with this change is that there are less layers that could be expose to potential bugs - and as well it ease out maintenance as well.

Support for ROCm/HIPBLAS

This release bring support for AMD thanks to @65a . See more details in 1100

More CLI commands

Thanks to @jespino now the local-ai binary has more subcommands allowing to manage the gallery or try out directly inferencing, check it out!

Release notes

25-09-2023: v1.30.0

This is an exciting LocalAI release! Besides bug-fixes and enhancements this release brings the new backend to a whole new level by extending support to vllm and vall-e-x for audio generation!

Check out the documentation for vllm here and Vall-E-X here

Release notes

26-08-2023: v1.25.0

Hey everyone, Ettore here, I’m so happy to share this release out - while this summer is hot apparently doesn’t stop LocalAI development :)

This release brings a lot of new features, bugfixes and updates! Also a big shout out to the community, this was a great release!

Attention 🚨

From this release the llama backend supports only gguf files (see 943 ). LocalAI however still supports ggml files. We ship a version of llama.cpp before that change in a separate backend, named llama-stable to allow still loading ggml files. If you were specifying the llama backend manually to load ggml files from this release you should use llama-stable instead, or do not specify a backend at all (LocalAI will automatically handle this).

Image generation enhancements

The Diffusers backend got now various enhancements, including support to generate images from images, longer prompts, and support for more kernels schedulers. See the Diffusers documentation for more information.

Lora adapters

Now it’s possible to load lora adapters for llama.cpp. See 955 for more information.

Device management

It is now possible for single-devices with one GPU to specify --single-active-backend to allow only one backend active at the time 925 .

Community spotlight

Resources management

Thanks to the continous community efforts (another cool contribution from dave-gray101 ) now it’s possible to shutdown a backend programmatically via the API. There is an ongoing effort in the community to better handling of resources. See also the 🔥Roadmap.

New how-to section

Thanks to the community efforts now we have a new how-to section with various examples on how to use LocalAI. This is a great starting point for new users! We are currently working on improving it, a huge shout out to lunamidori5 from the community for the impressive efforts on this!

💡 More examples!

• Open source autopilot? See the new addition by gruberdev in our examples on how to use Continue with LocalAI!
• Want to try LocalAI with Insomnia? Check out the new Insomnia example by dave-gray101 !

LocalAGI in discord!

Did you know that we have now few cool bots in our Discord? come check them out! We also have an instance of LocalAGI ready to help you out!

Changelog summary

Breaking Changes 🛠

• feat: bump llama.cpp, add gguf support by mudler in 943

Exciting New Features 🎉

• feat(Makefile): allow to restrict backend builds by mudler in 890
• feat(diffusers): various enhancements by mudler in 895
• feat: make initializer accept gRPC delay times by mudler in 900
• feat(diffusers): add DPMSolverMultistepScheduler++, DPMSolverMultistepSchedulerSDE++, guidance_scale by mudler in 903
• feat(diffusers): overcome prompt limit by mudler in 904
• feat(diffusers): add img2img and clip_skip, support more kernels schedulers by mudler in 906
• Usage Features by dave-gray101 in 863
• feat(diffusers): be consistent with pipelines, support also depthimg2img by mudler in 926
• feat: add –single-active-backend to allow only one backend active at the time by mudler in 925
• feat: add llama-stable backend by mudler in 932
• feat: allow to customize rwkv tokenizer by dave-gray101 in 937
• feat: backend monitor shutdown endpoint, process based by dave-gray101 in 938
• feat: Allow to load lora adapters for llama.cpp by mudler in 955

Join our Discord community! our vibrant community is growing fast, and we are always happy to help! https://discord.gg/uJAeKSAGDy

The full changelog is available here.

🔥🔥🔥🔥 12-08-2023: v1.24.0 🔥🔥🔥🔥

This is release brings four(!) new additional backends to LocalAI: 🐶 Bark, 🦙 AutoGPTQ, 🧨 Diffusers, 🦙 exllama and a lot of improvements!

Major improvements:

• feat: add bark and AutoGPTQ by mudler in 871
• feat: Add Diffusers by mudler in 874
• feat: add API_KEY list support by neboman11 and bnusunny in 877
• feat: Add exllama by mudler in 881
• feat: pre-configure LocalAI galleries by mudler in 886

🐶 Bark

Bark is a text-prompted generative audio model - it combines GPT techniques to generate Audio from text. It is a great addition to LocalAI, and it’s available in the container images by default.

It can also generate music, see the example: lion.webm

🦙 AutoGPTQ

AutoGPTQ is an easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.

It is targeted mainly for GPU usage only. Check out the AutoGPTQ documentation for usage.

🦙 Exllama

Exllama is a “A more memory-efficient rewrite of the HF transformers implementation of Llama for use with quantized weights”. It is a faster alternative to run LLaMA models on GPU.Check out the Exllama documentation for usage.

🧨 Diffusers

Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Currently it is experimental, and supports generation only of images so you might encounter some issues on models which weren’t tested yet. Check out the Diffusers documentation for usage.

🔑 API Keys

Thanks to the community contributions now it’s possible to specify a list of API keys that can be used to gate API requests.

API Keys can be specified with the API_KEY environment variable as a comma-separated list of keys.

🖼️ Galleries

Now by default the model-gallery repositories are configured in the container images

💡 New project

LocalAGI is a simple agent that uses LocalAI functions to have a full locally runnable assistant (with no API keys needed).

See it here in action planning a trip for San Francisco!

The full changelog is available here.

🔥🔥 29-07-2023: v1.23.0 🚀

This release focuses mostly on bugfixing and updates, with just a couple of new features:

• feat: add rope settings and negative prompt, drop grammar backend by mudler in 797
• Added CPU information to entrypoint.sh by @finger42 in 794
• feat: cancel stream generation if client disappears by @tmm1 in 792

Most notably, this release brings important fixes for CUDA (and not only):

• fix: add rope settings during model load, fix CUDA by mudler in 821
• fix: select function calls if ’name’ is set in the request by mudler in 827
• fix: symlink libphonemize in the container by mudler in 831

The full changelog is available here

🔥🔥🔥 23-07-2023: v1.22.0 🚀

• feat: add llama-master backend by mudler in 752
• [build] pass build type to cmake on libtransformers.a build by @TonDar0n in 741
• feat: resolve JSONSchema refs (planners) by mudler in 774
• feat: backends improvements by mudler in 778
• feat(llama2): add template for chat messages by dave-gray101 in 782

Huggingface embeddings

In this release is now possible to specify to LocalAI external gRPC backends that can be used for inferencing 778 . It is now possible to write internal backends in any language, and a huggingface-embeddings backend is now available in the container image to be used with https://github.com/UKPLab/sentence-transformers. See also Embeddings.

LLaMa 2 has been released!

Thanks to the community effort now LocalAI supports templating for LLaMa2! more at: 782 until we update the model gallery with LLaMa2 models!

Official langchain integration

Progress has been made to support LocalAI with langchain. See: https://github.com/langchain-ai/langchain/pull/8134

🔥🔥🔥 17-07-2023: v1.21.0 🚀

• [whisper] Partial support for verbose_json format in transcribe endpoint by @ldotlopez in 721
• LocalAI functions by @mudler in 726
• gRPC-based backends by @mudler in 743
• falcon support (7b and 40b) with ggllm.cpp by @mudler in 743

LocalAI functions

This allows to run OpenAI functions as described in the OpenAI blog post and documentation: https://openai.com/blog/function-calling-and-other-api-updates.

This is a video of running the same example, locally with LocalAI:

And here when it actually picks to reply to the user instead of using functions!

Note: functions are supported only with llama.cpp-compatible models.

A full example is available here: https://github.com/go-skynet/LocalAI/tree/master/examples/functions

gRPC backends

This is an internal refactor which is not user-facing, however, it allows to ease out maintenance and addition of new backends to LocalAI!

falcon support

Now Falcon 7b and 40b models compatible with https://github.com/cmp-nct/ggllm.cpp are supported as well.

The former, ggml-based backend has been renamed to falcon-ggml.

Default pre-compiled binaries

From this release the default behavior of images has changed. Compilation is not triggered on start automatically, to recompile local-ai from scratch on start and switch back to the old behavior, you can set REBUILD=true in the environment variables. Rebuilding can be necessary if your CPU and/or architecture is old and the pre-compiled binaries are not compatible with your platform. See the build section for more information.

Full release changelog

🔥🔥🔥 28-06-2023: v1.20.0 🚀

Exciting New Features 🎉

• Add Text-to-Audio generation with go-piper by mudler in 649 See API endpoints in our documentation.
• Add gallery repository by mudler in 663 . See models for documentation.

Container images

• Standard (GPT + stablediffusion): quay.io/go-skynet/local-ai:v1.20.0
• FFmpeg: quay.io/go-skynet/local-ai:v1.20.0-ffmpeg
• CUDA 11+FFmpeg: quay.io/go-skynet/local-ai:v1.20.0-cublas-cuda11-ffmpeg
• CUDA 12+FFmpeg: quay.io/go-skynet/local-ai:v1.20.0-cublas-cuda12-ffmpeg

Updates

Updates to llama.cpp, go-transformers, gpt4all.cpp and rwkv.cpp.

The NUMA option was enabled by mudler in 684 , along with many new parameters (mmap,mmlock, ..). See advanced for the full list of parameters.

Gallery repositories

In this release there is support for gallery repositories. These are repositories that contain models, and can be used to install models. The default gallery which contains only freely licensed models is in Github: https://github.com/go-skynet/model-gallery, but you can use your own gallery by setting the GALLERIES environment variable. An automatic index of huggingface models is available as well.

For example, now you can start LocalAI with the following environment variable to use both galleries:

GALLERIES=[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:ci-robbot/localai-huggingface-zoo/index.yaml","name":"huggingface"}]

And in runtime you can install a model from huggingface now with:

curl http://localhost:8000/models/apply -H "Content-Type: application/json" -d '{ "id": "huggingface@thebloke__open-llama-7b-open-instruct-ggml__open-llama-7b-open-instruct.ggmlv3.q4_0.bin" }'

or a tts voice with:

curl http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{ "id": "model-gallery@voice-en-us-kathleen-low" }'

See also models for a complete documentation.

Text to Audio

Now LocalAI uses piper and go-piper to generate audio from text. This is an experimental feature, and it requires GO_TAGS=tts to be set during build. It is enabled by default in the pre-built container images.

To setup audio models, you can use the new galleries, or setup the models manually as described in the API section of the documentation.

You can check the full changelog in Github

🔥🔥🔥 19-06-2023: v1.19.0 🚀

• Full CUDA GPU offload support ( PR by mudler. Thanks to chnyda for handing over the GPU access, and lu-zero to help in debugging )
• Full GPU Metal Support is now fully functional. Thanks to Soleblaze to iron out the Metal Apple silicon support!

Container images:

• Standard (GPT + stablediffusion): quay.io/go-skynet/local-ai:v1.19.2
• FFmpeg: quay.io/go-skynet/local-ai:v1.19.2-ffmpeg
• CUDA 11+FFmpeg: quay.io/go-skynet/local-ai:v1.19.2-cublas-cuda11-ffmpeg
• CUDA 12+FFmpeg: quay.io/go-skynet/local-ai:v1.19.2-cublas-cuda12-ffmpeg

🔥🔥🔥 06-06-2023: v1.18.0 🚀

This LocalAI release is plenty of new features, bugfixes and updates! Thanks to the community for the help, this was a great community release!

We now support a vast variety of models, while being backward compatible with prior quantization formats, this new release allows still to load older formats and new k-quants!

New features

• ✨ Added support for falcon-based model families (7b) ( mudler )
• ✨ Experimental support for Metal Apple Silicon GPU - ( mudler and thanks to Soleblaze for testing! ). See the build section.
• ✨ Support for token stream in the /v1/completions endpoint ( samm81 )
• ✨ Added huggingface backend ( Evilfreelancer )
• 📷 Stablediffusion now can output 2048x2048 images size with esrgan! ( mudler )

Container images

• 🐋 CUDA container images (arm64, x86_64) ( sebastien-prudhomme )
• 🐋 FFmpeg container images (arm64, x86_64) ( mudler )

Dependencies updates

• 🆙 Bloomz has been updated to the latest ggml changes, including new quantization format ( mudler )
• 🆙 RWKV has been updated to the new quantization format( mudler )
• 🆙 k-quants format support for the llama models ( mudler )
• 🆙 gpt4all has been updated, incorporating upstream changes allowing to load older models, and with different CPU instruction set (AVX only, AVX2) from the same binary! ( mudler )

Generic

• 🐧 Fully Linux static binary releases ( mudler )
• 📷 Stablediffusion has been enabled on container images by default ( mudler ) Note: You can disable container image rebuilds with REBUILD=false

Examples

• 💡 AutoGPT example ( mudler )
• 💡 PrivateGPT example ( mudler )
• 💡 Flowise example ( mudler )

Two new projects offer now direct integration with LocalAI!

• Flowise
• Mods

Full release changelog

29-05-2023: v1.17.0

Support for OpenCL has been added while building from sources.

You can now build LocalAI from source with BUILD_TYPE=clblas to have an OpenCL build. See also the build section.

For instructions on how to install OpenCL/CLBlast see here.

rwkv.cpp has been updated to the new ggml format commit.

27-05-2023: v1.16.0

Now it’s possible to automatically download pre-configured models before starting the API.

Start local-ai with the PRELOAD_MODELS containing a list of models from the gallery, for instance to install gpt4all-j as gpt-3.5-turbo:

PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/gpt4all-j.yaml", "name": "gpt-3.5-turbo"}]

llama.cpp models now can also automatically save the prompt cache state as well by specifying in the model YAML configuration file:

# Enable prompt caching

# This is a file that will be used to save/load the cache. relative to the models directory.
prompt_cache_path: "alpaca-cache"

# Always enable prompt cache
prompt_cache_all: true

See also the advanced section.

Media, Blogs, Social

• Create a slackbot for teams and OSS projects that answer to documentation
• LocalAI meets k8sgpt - CNCF Webinar showcasing LocalAI and k8sgpt.
• Question Answering on Documents locally with LangChain, LocalAI, Chroma, and GPT4All by Ettore Di Giacinto
• Tutorial to use k8sgpt with LocalAI - excellent usecase for localAI, using AI to analyse Kubernetes clusters. by Tyller Gillson

Previous

• 23-05-2023: v1.15.0 released. go-gpt2.cpp backend got renamed to go-ggml-transformers.cpp updated including https://github.com/ggerganov/llama.cpp/pull/1508 which breaks compatibility with older models. This impacts RedPajama, GptNeoX, MPT(not gpt4all-mpt), Dolly, GPT2 and Starcoder based models. Binary releases available, various fixes, including 341 .
• 21-05-2023: v1.14.0 released. Minor updates to the /models/apply endpoint, llama.cpp backend updated including https://github.com/ggerganov/llama.cpp/pull/1508 which breaks compatibility with older models. gpt4all is still compatible with the old format.
• 19-05-2023: v1.13.0 released! 🔥🔥 updates to the gpt4all and llama backend, consolidated CUDA support ( 310 thanks to @bubthegreat and @Thireus ), preliminar support for installing models via API.
• 17-05-2023: v1.12.0 released! 🔥🔥 Minor fixes, plus CUDA ( 258 ) support for llama.cpp-compatible models and image generation ( 272 ).
• 16-05-2023: 🔥🔥🔥 Experimental support for CUDA ( 258 ) in the llama.cpp backend and Stable diffusion CPU image generation ( 272 ) in master.

Now LocalAI can generate images too:

mode=0	mode=1 (winograd/sgemm)

• 14-05-2023: v1.11.1 released! rwkv backend patch release
• 13-05-2023: v1.11.0 released! 🔥 Updated llama.cpp bindings: This update includes a breaking change in the model files ( https://github.com/ggerganov/llama.cpp/pull/1405 ) - old models should still work with the gpt4all-llama backend.
• 12-05-2023: v1.10.0 released! 🔥🔥 Updated gpt4all bindings. Added support for GPTNeox (experimental), RedPajama (experimental), Starcoder (experimental), Replit (experimental), MosaicML MPT. Also now embeddings endpoint supports tokens arrays. See the langchain-chroma example! Note - this update does NOT include https://github.com/ggerganov/llama.cpp/pull/1405 which makes models incompatible.
• 11-05-2023: v1.9.0 released! 🔥 Important whisper updates ( 233 229 ) and extended gpt4all model families support ( 232 ). Redpajama/dolly experimental ( 214 )
• 10-05-2023: v1.8.0 released! 🔥 Added support for fast and accurate embeddings with bert.cpp ( 222 )
• 09-05-2023: Added experimental support for transcriptions endpoint ( 211 )
• 08-05-2023: Support for embeddings with models using the llama.cpp backend ( 207 )
• 02-05-2023: Support for rwkv.cpp models ( 158 ) and for /edits endpoint
• 01-05-2023: Support for SSE stream of tokens in llama.cpp backends ( 152 )

Features

This section contains the documentation for the features supported by LocalAI.

• 📖 Text generation (GPT)
• 🗣 Text to Audio
• 🔈 Audio to text
• 🎨 Image generation
• 🧠 Embeddings
• 🔥 OpenAI functions
• 🆕 GPT Vision API
• ✍️ Constrained grammars

Model compatibility

LocalAI is compatible with the models supported by llama.cpp supports also GPT4ALL-J and cerebras-GPT with ggml.

Hardware requirements

Depending on the model you are attempting to run might need more RAM or CPU resources. Check out also here for gguf based backends. rwkv is less expensive on resources.

Model compatibility table

Besides llama based models, LocalAI is compatible also with other architectures. The table below lists all the compatible models families and the associated binding repository.

Note: any backend name listed above can be used in the backend field of the model configuration file (See the advanced section).

• * 7b ONLY
• ** doesn’t seem to be accurate
• *** 7b and 40b with the ggccv format, for instance: https://huggingface.co/TheBloke/WizardLM-Uncensored-Falcon-40B-GGML

Tested with:

• Automatically by CI with OpenLLAMA and GPT4ALL.
• LLaMA 🦙
• Vicuna
• Alpaca
• GPT4ALL (see also using GPT4All)
• GPT4ALL-J (no changes required)
• Koala 🐨
• Cerebras-GPT
• WizardLM
• RWKV models with rwkv.cpp
• bloom.cpp
• Chinese LLaMA / Alpaca
• Vigogne (French)
• OpenBuddy 🐶 (Multilingual)
• Pygmalion 7B / Metharme 7B
• HuggingFace Inference models available through API
• Falcon

Note: You might need to convert some models from older models to the new format, for indications, see the README in llama.cpp for instance to run gpt4all.

Build

Build locally

Requirements:

Either Docker/podman, or

• Golang >= 1.21
• Cmake/make
• GCC

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 manually binary with make:

git clone https://github.com/go-skynet/LocalAI
cd LocalAI
make build

To run: ./local-ai

CMAKE_ARGS="-DLLAMA_F16C=OFF -DLLAMA_AVX512=OFF -DLLAMA_AVX2=OFF -DLLAMA_AVX=OFF -DLLAMA_FMA=OFF" make build

docker run  quay.io/go-skynet/localai
docker run --rm -ti -p 8080:8080 -e DEBUG=true -e MODELS_PATH=/models -e THREADS=1 -e REBUILD=true -e CMAKE_ARGS="-DLLAMA_F16C=OFF -DLLAMA_AVX512=OFF -DLLAMA_AVX2=OFF -DLLAMA_AVX=OFF -DLLAMA_FMA=OFF" -v $PWD/models:/models quay.io/go-skynet/local-ai:latest

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 abseil cmake go grpc protobuf wget

# 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=true

# 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 build

Build with Text to audio support

Requirements: piper-phonemize

Text to audio support is experimental and requires GO_TAGS=tts to be set during build:

make GO_TAGS=tts build

Acceleration

List of the variables available to customize the build:

OpenBLAS

Software acceleration.

Requirements: OpenBLAS

make BUILD_TYPE=openblas build

CuBLAS

Nvidia Acceleration.

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

Hipblas (AMD GPU with ROCm on Arch Linux)

Packages:

pacman -S base-devel git rocm-hip-sdk rocm-opencl-sdk opencv clblast grpc

Library links:

export CGO_CFLAGS="-I/usr/include/opencv4"
export CGO_CXXFLAGS="-I/usr/include/opencv4"
export CGO_LDFLAGS="-L/opt/rocm/hip/lib -lamdhip64 -L/opt/rocm/lib -lOpenCL -L/usr/lib -lclblast -lrocblas -lhipblas -lrocrand -lomp -O3 --rtlib=compiler-rt -unwindlib=libgcc -lhipblas -lrocblas --hip-link"

Build:

make BUILD_TYPE=hipblas GPU_TARGETS=gfx1030

ClBLAS

AMD/Intel GPU acceleration.

Requirement: OpenCL, CLBlast

make BUILD_TYPE=clblas build

To specify a clblast dir set: CLBLAST_DIR

Metal (Apple Silicon)

make BUILD_TYPE=metal build

# Set `gpu_layers: 1` to your YAML model config file and `f16: true`
# Note: only models quantized with q4_0 are supported!

Windows compatibility

Make sure to give enough resources to the running container. See https://github.com/go-skynet/LocalAI/issues/2

Advanced

Advanced configuration with YAML files

In order to define default prompts, model parameters (such as custom default top_p or top_k), 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   ?G Apr 27 00:08 luna-ai-llama2-uncensored.ggmlv3.q5_K_M.bin
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 chat.tmpl

In the gpt-3.5-turbo.yaml file it is defined the gpt-3.5-turbo model which is an alias to use luna-ai-llama2 with pre-defined options.

For instance, consider the following that declares gpt-3.5-turbo backed by the luna-ai-llama2 model:

name: gpt-3.5-turbo
# Default model parameters
parameters:
  # Relative to the models path
  model: luna-ai-llama2-uncensored.ggmlv3.q5_K_M.bin
  # 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: llama-stable # available: llama, stablelm, gpt2, gptj rwkv

# Enable prompt caching
prompt_cache_path: "alpaca-cache"
prompt_cache_all: true

# stopwords (if supported by the backend)
stopwords:
- "HUMAN:"
- "### Response:"
# define chat roles
roles:
  assistant: '### Response:'
  system: '### System Instruction:'
  user: '### Instruction:'
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: chat

Specifying a config-file via CLI allows to declare models in a single file as a list, for instance:

- name: list1
  parameters:
    model: testmodel
  context_size: 512
  threads: 10
  stopwords:
  - "HUMAN:"
  - "### Response:"
  roles:
    user: "HUMAN:"
    system: "GPT:"
  template:
    completion: completion
    chat: chat
- name: list2
  parameters:
    model: testmodel
  context_size: 512
  threads: 10
  stopwords:
  - "HUMAN:"
  - "### Response:"
  roles:
    user: "HUMAN:"
    system: "GPT:"
  template:
    completion: completion
   chat: chat

See also chatbot-ui as an example on how to use config files.

Full config model file reference

# Model name.
# The model name is used to identify the model in the API calls.
name: gpt-3.5-turbo

# Default model parameters.
# These options can also be specified in the API calls
parameters:
  # Relative to the models path
  model: luna-ai-llama2-uncensored.ggmlv3.q5_K_M.bin
  # temperature
  temperature: 0.3
  # all the OpenAI request options here..
  top_k: 
  top_p: 
  max_tokens:
  ignore_eos: true
  n_keep: 10
  seed: 
  mode: 
  step:
  negative_prompt:
  typical_p:
  tfz:
  frequency_penalty:
  mirostat_eta:
  mirostat_tau:
  mirostat: 
  rope_freq_base:
  rope_freq_scale:
  negative_prompt_scale:

# 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: llama-stable # 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"

# Directory used to store additional assets
asset_dir: ""

# 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: chat
  edit: edit_template
  function: function_template

function:
   disable_no_action: true
   no_action_function_name: "reply"
   no_action_description_name: "Reply to the AI assistant"

system_prompt:
rms_norm_eps:
# Set it to 8 for llama2 70b
ngqa: 1
## LLAMA specific options
# 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
# Enable memory lock
mmlock: true
# GPU setting to split the tensor in multiple parts and define a main GPU
# see llama.cpp for usage
tensor_split: ""
main_gpu: ""
# Define a prompt cache path (relative to the models)
prompt_cache_path: "prompt-cache"
# Cache all the prompts
prompt_cache_all: true
# Read only
prompt_cache_ro: false
# Enable mmap
mmap: true
# Enable low vram mode (GPU only)
low_vram: true
# Set NUMA mode (CPU only)
numa: true
# Lora settings
lora_adapter: "/path/to/lora/adapter"
lora_base: "/path/to/lora/base"
# Disable mulmatq (CUDA)
no_mulmatq: true

# Diffusers/transformers
cuda: true

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.

The below instruction describes a task. Write a response that appropriately completes the request.

### Instruction:
{{.Input}}

### Response:

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:

Install 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 (work in progress!). The files of the model gallery are different from the model files used to configure LocalAI models. The model gallery files contains information about the model setup, and the files necessary to run the model locally.

To install for example lunademo, 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 --location 'http://localhost:8080/models/apply' \
--header 'Content-Type: application/json' \
--data-raw '{
    "id": "TheBloke/Luna-AI-Llama2-Uncensored-GGML/luna-ai-llama2-uncensored.ggmlv3.q5_K_M.bin",
    "name": "lunademo"
}'

Preloading models during startup

In order to allow the API to start-up with all the needed model on the first-start, the model gallery files can be used during startup.

PRELOAD_MODELS='[{"url": "https://raw.githubusercontent.com/go-skynet/model-gallery/main/gpt4all-j.yaml","name": "gpt4all-j"}]' local-ai

PRELOAD_MODELS (or --preload-models) takes a list in JSON with the same parameter of the API calls of the /models/apply endpoint.

Similarly it can be specified a path to a YAML configuration file containing a list of models with PRELOAD_MODELS_CONFIG ( or --preload-models-config ):

- url: https://raw.githubusercontent.com/go-skynet/model-gallery/main/gpt4all-j.yaml
  name: gpt4all-j
# ...

Automatic prompt caching

LocalAI can automatically cache prompts for faster loading of the prompt. This can be useful if your model need a prompt template with prefixed text in the prompt before the input.

To enable prompt caching, you can control the settings in the model config YAML file:

# Enable prompt caching
prompt_cache_path: "cache"
prompt_cache_all: true

prompt_cache_path is relative to the models folder. you can enter here a name for the file that will be automatically create during the first load if prompt_cache_all is set to true.

Configuring a specific backend for the model

By default LocalAI will try to autoload the model by trying all the backends. This might work for most of models, but some of the backends are NOT configured to autoload.

The available backends are listed in the model compatibility table.

In order to specify a backend for your models, create a model config file in your models directory specifying the backend:

name: gpt-3.5-turbo

# Default model parameters
parameters:
  # Relative to the models path
  model: ...

backend: llama-stable
# ...

Connect external backends

LocalAI backends are internally implemented using gRPC services. This also allows LocalAI to connect to external gRPC services on start and extend LocalAI functionalities via third-party binaries.

The --external-grpc-backends parameter in the CLI can be used either to specify a local backend (a file) or a remote URL. The syntax is <BACKEND_NAME>:<BACKEND_URI>. Once LocalAI is started with it, the new backend name will be available for all the API endpoints.

So for instance, to register a new backend which is a local file:

./local-ai --debug --external-grpc-backends "my-awesome-backend:/path/to/my/backend.py"

Or a remote URI:

./local-ai --debug --external-grpc-backends "my-awesome-backend:host:port"

Environment variables

When LocalAI runs in a container, there are additional environment variables available that modify the behavior of LocalAI on startup:

Here is how to configure these variables:

# Option 1: command line
docker run --env REBUILD=true localai
# Option 2: set within an env file
docker run --env-file .env localai

Build only a single backend

You can control the backends that are built by setting the GRPC_BACKENDS environment variable. For instance, to build only the llama-cpp backend only:

make GRPC_BACKENDS=backend-assets/grpc/llama-cpp build

By default, all the backends are built.

Extra backends

LocalAI can be extended with extra backends. The backends are implemented as gRPC services and can be written in any language. The container images that are built and published on quay.io contain a set of images split in core and extra. By default Images bring all the dependencies and backends supported by LocalAI (we call those extra images). The -core images instead bring only the strictly necessary dependencies to run LocalAI without only a core set of backends.

If you wish to build a custom container image with extra backends, you can use the core images and build only the backends you are interested into or prepare the environment on startup by using the EXTRA_BACKENDS environment variable. For instance, to use the diffusers backend:

FROM quay.io/go-skynet/local-ai:master-ffmpeg-core

RUN PATH=$PATH:/opt/conda/bin make -C backend/python/diffusers

Remember also to set the EXTERNAL_GRPC_BACKENDS environment variable (or --external-grpc-backends as CLI flag) to point to the backends you are using (EXTERNAL_GRPC_BACKENDS="backend_name:/path/to/backend"), for example with diffusers:

FROM quay.io/go-skynet/local-ai:master-ffmpeg-core

RUN PATH=$PATH:/opt/conda/bin make -C backend/python/diffusers

ENV EXTERNAL_GRPC_BACKENDS="diffusers:/build/backend/python/diffusers/run.sh"

In runtime

When using the -core container image it is possible to prepare the python backends you are interested into by using the EXTRA_BACKENDS variable, for instance:

docker run --env EXTRA_BACKENDS="backend/python/diffusers" quay.io/go-skynet/local-ai:master-ffmpeg-core

🖼️ Model gallery

The model gallery is a (experimental!) collection of models configurations for LocalAI.

LocalAI to ease out installations of models provide a way to preload models on start and downloading and installing them in runtime. You can install models manually by copying them over the models directory, or use the API to configure, download and verify the model assets for you. As the UI is still a work in progress, you will find here the documentation about the API Endpoints.

Useful Links and resources

• Open LLM Leaderboard - here you can find a list of the most performing models on the Open LLM benchmark. Keep in mind models compatible with LocalAI must be quantized in the gguf format.

Model repositories

You can install a model in runtime, while the API is running and it is started already, or before starting the API by preloading the models.

To install a model in runtime you will need to use the /models/apply LocalAI API endpoint.

To enable the model-gallery repository you need to start local-ai with the GALLERIES environment variable:

GALLERIES=[{"name":"<GALLERY_NAME>", "url":"<GALLERY_URL"}]

For example, to enable the model-gallery repository, start local-ai with:

GALLERIES=[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}]

where github:go-skynet/model-gallery/index.yaml will be expanded automatically to https://raw.githubusercontent.com/go-skynet/model-gallery/main/index.yaml.

GALLERIES=[{"name":"model-gallery", "url":"github:go-skynet/model-gallery/index.yaml"}, {"url": "github:go-skynet/model-gallery/huggingface.yaml","name":"huggingface"}]

List Models

To list all the available models, use the /models/available endpoint:

curl http://localhost:8080/models/available

To search for a model, you can use jq:

# Get all information about models with a name that contains "replit"
curl http://localhost:8080/models/available | jq '.[] | select(.name | contains("replit"))'

# Get the binary name of all local models (not hosted on Hugging Face)
curl http://localhost:8080/models/available | jq '.[] | .name | select(contains("localmodels"))'

# Get all of the model URLs that contains "orca"
curl http://localhost:8080/models/available | jq '.[] | .urls | select(. != null) | add | select(contains("orca"))'

How to install a model from the repositories

Models can be installed by passing the full URL of the YAML config file, or either an identifier of the model in the gallery. The gallery is a repository of models that can be installed by passing the model name.

To install a model from the gallery repository, you can pass the model name in the id field. For instance, to install the bert-embeddings model, you can use the following command:

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "model-gallery@bert-embeddings"
   }'  

where:

• model-gallery is the repository. It is optional and can be omitted. If the repository is omitted LocalAI will search the model by name in all the repositories. In the case the same model name is present in both galleries the first match wins.
• bert-embeddings is the model name in the gallery (read its config here).

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "huggingface@TheBloke/WizardLM-13B-V1-0-Uncensored-SuperHOT-8K-GGML/wizardlm-13b-v1.0-superhot-8k.ggmlv3.q4_K_M.bin"
   }'  

How to install a model (without a gallery)

If you don’t want to set any gallery repository, you can still install models by loading a model configuration file.

In the body of the request you must specify the model configuration file URL (url), optionally a name to install the model (name), extra files to install (files), and configuration overrides (overrides). When calling the API endpoint, LocalAI will download the models files and write the configuration to the folder used to store models.

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_CONFIG_FILE>"
   }' 
# or if from a repository
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "<GALLERY>@<MODEL_NAME>"
   }' 

An example that installs openllama can be:

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "https://github.com/go-skynet/model-gallery/blob/main/openllama_3b.yaml"
   }'  

The API will return a job uuid that you can use to track the job progress:

{"uuid":"1059474d-f4f9-11ed-8d99-c4cbe106d571","status":"http://localhost:8080/models/jobs/1059474d-f4f9-11ed-8d99-c4cbe106d571"}

For instance, a small example bash script that waits a job to complete can be (requires jq):

response=$(curl -s http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{"url": "$model_url"}')

job_id=$(echo "$response" | jq -r '.uuid')

while [ "$(curl -s http://localhost:8080/models/jobs/"$job_id" | jq -r '.processed')" != "true" ]; do 
  sleep 1
done

echo "Job completed"

To preload models on start instead you can use the PRELOAD_MODELS environment variable.

PRELOAD_MODELS='[{"url": "<MODEL_URL>"}]'

PRELOAD_MODELS=[{"url": "github:go-skynet/model-gallery/stablediffusion.yaml"}]

local-ai --preload-models '[{"url": "github:go-skynet/model-gallery/stablediffusion.yaml"}]'

local-ai --preload-models-config "/path/to/yaml"

- url: github:go-skynet/model-gallery/stablediffusion.yaml

curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "github:go-skynet/model-gallery/base.yaml",
     "name": "model-name",
     "files": [
        {
            "uri": "<URL>",
            "sha256": "<SHA>",
            "filename": "model"
        }
     ]
   }'

Installing a model with a different name

To install a model with a different name, specify a name parameter in the request body.

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_CONFIG_FILE>",
     "name": "<MODEL_NAME>"
   }'  

For example, to install a model as gpt-3.5-turbo:

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
      "url": "github:go-skynet/model-gallery/gpt4all-j.yaml",
      "name": "gpt-3.5-turbo"
   }'  

Additional Files

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_CONFIG_FILE>",
     "name": "<MODEL_NAME>",
     "files": [
        {
            "uri": "<additional_file_url>",
            "sha256": "<additional_file_hash>",
            "filename": "<additional_file_name>"
        }
     ]
   }'  

Overriding configuration files

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_CONFIG_FILE>",
     "name": "<MODEL_NAME>",
     "overrides": {
        "backend": "llama",
        "f16": true,
        ...
     }
   }'