Model Gallery

The model gallery is a curated collection of models configurations for LocalAI that enables one-click install of models directly from the LocalAI Web interface.

A list of the models available can also be browsed at the Public LocalAI Gallery.

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 or the Web interface to configure, download and verify the model assets for you.

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.

How it works

Navigate the WebUI interface in the “Models” section from the navbar at the top. Here you can find a list of models that can be installed, and you can install them by clicking the “Install” button.

VRAM and download size estimates

When browsing the gallery or importing a model by URI, LocalAI can show estimated download size and estimated VRAM for models.

• Where they appear: In the model gallery table (Size / VRAM column), in the model detail modal, and after starting an import from URI (in the success message).
• How they are computed: GGUF models use file size (HTTP HEAD or local stat) and optional GGUF metadata (HTTP Range) for KV cache and overhead; other formats use Hugging Face file sizes and optional config when available. If metadata is unavailable, a size-only heuristic is used.
• Hardware fit indicator: When your system reports GPU or RAM capacity, the gallery shows whether the estimated VRAM fits (green) or may not fit (red) using a 95% headroom rule.
• Estimates are best-effort and may be missing if the server does not support HEAD/Range or the request times out.

Add other galleries

You can add other galleries by:

1. Using the Web UI: Navigate to the Runtime Settings page and configure galleries through the interface.

2. Using Environment Variables: Set the GALLERIES environment variable. The GALLERIES environment variable is a list of JSON objects, where each object has a name and a url field. The name field is the name of the gallery, and the url field is the URL of the gallery’s index file, for example:

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

3. Using Configuration Files: Add galleries to runtime_settings.json in the LOCALAI_CONFIG_DIR directory.

The models in the gallery will be automatically indexed and available for installation.

API Reference

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.

By default LocalAI is configured with the localai repository.

To use additional repositories you need to start local-ai with the GALLERIES environment variable:

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

For example, to enable the default localai repository, you can start local-ai with:

GALLERIES=[{"name":"localai", "url":"github:mudler/localai/gallery/index.yaml"}]

where github:mudler/localai/gallery/index.yaml will be expanded automatically to https://raw.githubusercontent.com/mudler/LocalAI/main/index.yaml.

Note: the url are expanded automatically for github and huggingface, however https:// and http:// prefix works as well.

Using Local Gallery Files

You can also use local gallery index files by using the file:// prefix. For security reasons, local gallery files must be located within your models directory (the directory specified by MODELS_PATH or the default models/ directory).

Example:

GALLERIES=[{"name":"my-local-gallery", "url":"file:///path/to/models/my-gallery-index.yaml"}]

Important notes:

• The file:// prefix is required for local paths
• The file path must be absolute (starting with / on Unix systems)
• The resolved path must be within your models directory for security
• If you try to access files outside the models directory, LocalAI will block the request

Valid example (assuming MODELS_PATH=/opt/localai/models):

GALLERIES=[{"name":"local", "url":"file:///opt/localai/models/galleries/my-gallery.yaml"}]

Invalid example (file outside models directory):

GALLERIES=[{"name":"local", "url":"file:///home/user/my-gallery.yaml"}]

This will be rejected with a security error.

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:

curl http://localhost:8080/models/available | jq '.[] | select(.name | contains("replit"))'

curl http://localhost:8080/models/available | jq '.[] | .name | select(contains("localmodels"))'

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": "localai@bert-embeddings"
   }'  

where:

• localai 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).

Model variants

Some gallery entries offer several builds of the same model: different quantizations, or the same weights served by a different engine. Such an entry carries a variants list, and installing it normally lets LocalAI choose:

• variants whose backend cannot run on this machine are dropped;
• variants that do not fit the available memory are dropped. That budget is VRAM on a discrete-GPU host, and system RAM otherwise — including on unified-memory machines such as Apple Silicon, where the GPU shares system RAM and reports no separate VRAM pool;
• the entry’s own build is never dropped. It competes with whatever survived rather than waiting for everything else to fail, so an entry that is itself the largest build that fits keeps its own payload;
• among the remaining builds the engine this machine prefers wins first: a vLLM build on an NVIDIA or AMD host, an MLX build on Apple Silicon, llama.cpp otherwise. The native accelerated runtime is worth more than a bigger download, so preference is settled before size;
• the largest build on the preferred engine then wins, because a bigger footprint means a higher quality build of the same model. A machine with no preferred engine picks purely by size;
• a build whose size could not be measured ranks below the entry’s own build, so an unreadable size never quietly displaces the payload the entry ships;
• if nothing else survives, the entry’s own build is installed. The entry is always installable, on any machine.

Because the entry’s own build competes like every other candidate, the order of the list means nothing and a variants list may offer smaller builds, larger ones, or both.

Sizes are measured from the model’s weights rather than downloaded, and cached.

The gallery listing only flags which entries offer variants, with a has_variants field. It deliberately does not describe them: measuring a variant is a network round trip per referenced build, so describing every entry inline would make one listing request cost as many round trips as the whole page has variants.

curl http://localhost:8080/api/models | jq '.models[] | select(.has_variants) | .name'

Collapsing the listing to one row per model

By default the listing returns every entry, including the individual builds a parent entry offers as variants, so one model can occupy several rows. Pass collapse_variants=true for the deduplicated view: every entry that is installable in its own right, with nothing shown twice.

curl 'http://localhost:8080/api/models?collapse_variants=true'

An entry is hidden only when another entry already offers it as a variant, so it stays reachable by installing that entry. Entries that declare variants are always kept, and so is any entry nobody references. The filter is applied before pagination, so page counts stay correct.

Searching respects the collapse. term is matched against every entry the gallery holds, builds another entry offers included, so nothing becomes unfindable. The collapse then decides how a match is reported: a hit on a build another entry offers comes back as that entry, since that is the row installable in its own right. Looking a model up by name must not answer “not found” for an entry the gallery holds, and must not answer with a row the requested view has no place for either. A term that is empty or only whitespace does not count as a search:

# Collapsed: the parent stands in for the build it offers.
curl 'http://localhost:8080/api/models?collapse_variants=true'
# Collapsed, searching a build the parent offers: the parent comes back.
curl 'http://localhost:8080/api/models?collapse_variants=true&term=nanbeige4.1-3b-q8'
# Uncollapsed, same term: the build itself comes back.
curl 'http://localhost:8080/api/models?term=nanbeige4.1-3b-q8'

A parent appears once however many of its builds match, and once when it matches in its own right as well. The substitution runs before the count and the page math, so both describe the rows actually handed out.

term, tag and backend are all applied before the substitution, so each is judged against the build that really carries the name, tag or backend rather than against a parent that merely offers it. The consequence is worth knowing: filtering by a backend only one variant declares returns that variant’s parent, whose own backend field may say something else. The alternative would be to claim the gallery holds no such build.

The web UI requests the collapsed view by default and has a toggle for the other one. The parameter stays on the API, off by default, for clients that want either view.

Ask for the description one entry at a time, as the web UI does when you open a model’s variant menu:

curl http://localhost:8080/api/models/variants/localai@nanbeige4.1-3b-q4

{
  "auto_selected": "nanbeige4.1-3b-q8",
  "variants": [
    { "model": "nanbeige4.1-3b-q8", "backend": "llama-cpp", "memory_bytes": 4187593113, "fits": true, "is_base": false },
    { "model": "nanbeige4.1-3b-q4", "backend": "llama-cpp", "fits": true, "is_base": true }
  ]
}

auto_selected is what installing without a choice would pick right now. fits is whether auto-selection would consider that variant on this machine, and is_base marks the entry’s own build. memory_bytes is omitted entirely, as on the second entry above, when the size could not be measured; read a missing memory_bytes as unknown rather than as a free build.

An entry that declares no variants carries no has_variants field and answers this endpoint with an empty list, so a client never has to ask about it.

To install a specific one, pass its name as variant:

curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "localai@nanbeige4.1-3b-q4",
     "variant": "nanbeige4.1-3b-q8"
   }'

An explicit choice is honored even when the machine looks too small for it, so you can deliberately install a build LocalAI would not have picked. A variant the entry does not declare fails the install and names what was requested; it never quietly falls back to auto-selection. The choice is recorded, so a later reinstall or upgrade of the same model stays on the variant you picked.

The same option exists on the CLI:

local-ai models install nanbeige4.1-3b-q4 --variant nanbeige4.1-3b-q8

The install_model MCP tool takes the same variant argument, so an assistant managing installs conversationally can pick a build too.

Entries without a variants list are unaffected by any of this and install exactly as they always have.

Artifact-backed models

Gallery models with an artifacts declaration are fully materialized during installation. Their operation progresses through these phases:

resolving -> downloading -> verifying -> committing -> persisting

The admin Operations Bar and GET /api/operations expose currentBytes and totalBytes as raw transport bytes. Cancelling an active download leaves its partial files in place so a retry can resume. A verification failure never exposes a completed snapshot, while a retry or another installation reuses an already verified content-addressed snapshot.

Deleting a model configuration does not delete its content-addressed snapshot bytes. This allows another configuration or a later reinstall to reuse the cache; safe cache garbage collection is deferred.

How to install a model not part of 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 '{
     "config_url": "<MODEL_CONFIG_FILE_URL>"
   }' 
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "<GALLERY>@<MODEL_NAME>"
   }' 
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_CONFIG_FILE_URL>"
   }' 

An example that installs hermes-2-pro-mistral can be:

LOCALAI=http://localhost:8080
curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "config_url": "https://raw.githubusercontent.com/mudler/LocalAI/v2.25.0/embedded/models/hermes-2-pro-mistral.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:mudler/LocalAI/gallery/stablediffusion.yaml@master"}]

local-ai --preload-models '[{"url": "github:mudler/LocalAI/gallery/stablediffusion.yaml@master"}]'

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

- url: github:mudler/LocalAI/gallery/stablediffusion.yaml@master

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

Override a model 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:mudler/LocalAI/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,
        ...
     }
   }'  

Examples

Embeddings: Bert

curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{
     "id": "bert-embeddings",
     "name": "text-embedding-ada-002"
   }'  

LOCALAI=http://localhost:8080
curl $LOCALAI/v1/embeddings -H "Content-Type: application/json" -d '{
    "input": "Test",
    "model": "text-embedding-ada-002"
  }'

Image generation: Stable diffusion

URL: https://github.com/EdVince/Stable-Diffusion-NCNN

curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{         
     "url": "github:mudler/LocalAI/gallery/stablediffusion.yaml@master"
   }'

PRELOAD_MODELS=[{"url": "github:mudler/LocalAI/gallery/stablediffusion.yaml@master"}]

local-ai --preload-models '[{"url": "github:mudler/LocalAI/gallery/stablediffusion.yaml@master"}]'

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

- url: github:mudler/LocalAI/gallery/stablediffusion.yaml@master

Test it:

curl $LOCALAI/v1/images/generations -H "Content-Type: application/json" -d '{
            "prompt": "floating hair, portrait, ((loli)), ((one girl)), cute face, hidden hands, asymmetrical bangs, beautiful detailed eyes, eye shadow, hair ornament, ribbons, bowties, buttons, pleated skirt, (((masterpiece))), ((best quality)), colorful|((part of the head)), ((((mutated hands and fingers)))), deformed, blurry, bad anatomy, disfigured, poorly drawn face, mutation, mutated, extra limb, ugly, poorly drawn hands, missing limb, blurry, floating limbs, disconnected limbs, malformed hands, blur, out of focus, long neck, long body, Octane renderer, lowres, bad anatomy, bad hands, text",
            "mode": 2,  "seed":9000,
            "size": "256x256", "n":2
}'

Audio transcription: Whisper

URL: https://github.com/ggerganov/whisper.cpp

curl $LOCALAI/models/apply -H "Content-Type: application/json" -d '{         
     "url": "github:mudler/LocalAI/gallery/whisper-base.yaml@master",
     "name": "whisper-1"
   }'

PRELOAD_MODELS=[{"url": "github:mudler/LocalAI/gallery/whisper-base.yaml@master", "name": "whisper-1"}]

local-ai --preload-models '[{"url": "github:mudler/LocalAI/gallery/whisper-base.yaml@master", "name": "whisper-1"}]'

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

- url: github:mudler/LocalAI/gallery/whisper-base.yaml@master
  name: whisper-1

Note

LocalAI will create a batch process that downloads the required files from a model definition and automatically reload itself to include the new model.

Input: url or id (required), name (optional), files (optional)

curl http://localhost:8080/models/apply -H "Content-Type: application/json" -d '{
     "url": "<MODEL_DEFINITION_URL>",
     "id": "<GALLERY>@<MODEL_NAME>",
     "name": "<INSTALLED_MODEL_NAME>",
     "files": [
        {
            "uri": "<additional_file>",
            "sha256": "<additional_file_hash>",
            "filename": "<additional_file_name>"
        },
      "overrides": { "backend": "...", "f16": true }
     ]
   }

An optional, list of additional files can be specified to be downloaded within files. The name allows to override the model name. Finally it is possible to override the model config file with override.

The url is a full URL, or a github url (github:org/repo/file.yaml), or a local file (file:///path/to/file.yaml).

The id is a string in the form <GALLERY>@<MODEL_NAME>, where <GALLERY> is the name of the gallery, and <MODEL_NAME> is the name of the model in the gallery. Galleries can be specified during startup with the GALLERIES environment variable.

Returns an uuid and an url to follow up the state of the process:

{ "uuid":"251475c9-f666-11ed-95e0-9a8a4480ac58", "status":"http://localhost:8080/models/jobs/251475c9-f666-11ed-95e0-9a8a4480ac58"}

To see a collection example of curated models definition files, see the LocalAI repository.

Get model job state /models/jobs/<uid>

This endpoint returns the state of the batch job associated to a model installation.

curl http://localhost:8080/models/jobs/<JOB_ID>

Returns a json containing the error, and if the job is being processed:

{"error":null,"processed":true,"message":"completed"}