Advanced usage

Advanced configuration with YAML files link

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.

In order to configure a model, 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.

It is possible to specify a full URL or a short-hand URL to a YAML model configuration file and use it on start with local-ai, for example to use phi-2:

  local-ai github://mudler/LocalAI/examples/configurations/phi-2.yaml@master
  

Full config model file reference link

  # 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:

  rope_freq_base:
  rope_freq_scale:
  negative_prompt_scale:

mirostat_eta:
mirostat_tau:
mirostat: 
# 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 link

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 link

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 link

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 link

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 link

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 link

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"
  

For example, to start vllm manually after compiling LocalAI (also assuming running the command from the root of the repository):

  ./local-ai --external-grpc-backends "vllm:$PWD/backend/python/vllm/run.sh"
  

Note that first is is necessary to create the environment with:

  make -C backend/python/vllm
  

Environment variables link

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
  

CLI parameters link

You can control LocalAI with command line arguments, to specify a binding address, or the number of threads. Any command line parameter can be specified via an environment variable.

In the help text below, BASEPATH is the location that local-ai is being executed from

Global Flags link

Storage Flags link

Models Flags link

Performance Flags link

API Flags link

Backend Flags link

.env files link

Any settings being provided by an Environment Variable can also be provided from within .env files. There are several locations that will be checked for relevant .env files. In order of precedence they are:

• .env within the current directory
• localai.env within the current directory
• localai.env within the home directory
• .config/localai.env within the home directory
• /etc/localai.env

Environment variables within files earlier in the list will take precedence over environment variables defined in files later in the list.

An example .env file is:

  LOCALAI_THREADS=10
LOCALAI_MODELS_PATH=/mnt/storage/localai/models
LOCALAI_F16=true
  

Extra backends link

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 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 make -C backend/python/diffusers

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

In runtime link

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
  

Concurrent requests link

LocalAI supports parallel requests for the backends that supports it. For instance, vLLM and llama.cpp supports parallel requests, and thus LocalAI allows to run multiple requests in parallel.

In order to enable parallel requests, you have to pass --parallel-requests or set the PARALLEL_REQUEST to true as environment variable.

A list of the environment variable that tweaks parallelism is the following:

  ### Python backends GRPC max workers
### Default number of workers for GRPC Python backends.
### This actually controls wether a backend can process multiple requests or not.
# PYTHON_GRPC_MAX_WORKERS=1

### Define the number of parallel LLAMA.cpp workers (Defaults to 1)
# LLAMACPP_PARALLEL=1

### Enable to run parallel requests
# LOCALAI_PARALLEL_REQUESTS=true
  

Note that, for llama.cpp you need to set accordingly LLAMACPP_PARALLEL to the number of parallel processes your GPU/CPU can handle. For python-based backends (like vLLM) you can set PYTHON_GRPC_MAX_WORKERS to the number of parallel requests.

Disable CPU flagset auto detection in llama.cpp link

LocalAI will automatically discover the CPU flagset available in your host and will use the most optimized version of the backends.

If you want to disable this behavior, you can set DISABLE_AUTODETECT to true in the environment variables.