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|
{ config, lib, pkgs, ... }:
let
inherit (lib) types;
cfg = config.services.tabby;
format = pkgs.formats.toml { };
tabbyPackage = cfg.package.override {
inherit (cfg) acceleration;
};
in
{
options = {
services.tabby = {
enable = lib.mkEnableOption "Self-hosted AI coding assistant using large language models";
package = lib.mkPackageOption pkgs "tabby" { };
port = lib.mkOption {
type = types.port;
default = 11029;
description = ''
Specifies the bind port on which the tabby server HTTP interface listens.
'';
};
model = lib.mkOption {
type = types.str;
default = "TabbyML/StarCoder-1B";
description = ''
Specify the model that tabby will use to generate completions.
This model will be downloaded automatically if it is not already present.
If you want to utilize an existing model that you've already
downloaded you'll need to move it into tabby's state directory which
lives in `/var/lib/tabby`. Because the tabby.service is configured to
use a DyanmicUser the service will need to have been started at least
once before you can move the locally existing model into
`/var/lib/tabby`. You can set the model to 'none' and tabby will
startup and fail to download a model, but will have created the
`/var/lib/tabby` directory. You can then copy over the model manually
into `/var/lib/tabby`, update the model option to the name you just
downloaded and copied over then `nixos-rebuild switch` to start using
it.
$ tabby download --model TabbyML/DeepseekCoder-6.7B
$ find ~/.tabby/ | tail -n1
/home/ghthor/.tabby/models/TabbyML/DeepseekCoder-6.7B/ggml/q8_0.v2.gguf
$ sudo rsync -r ~/.tabby/models/ /var/lib/tabby/models/
$ sudo chown -R tabby:tabby /var/lib/tabby/models/
See for Model Options:
> https://github.com/TabbyML/registry-tabby
'';
};
acceleration = lib.mkOption {
type = types.nullOr (types.enum [ "cpu" "rocm" "cuda" "metal" ]);
default = null;
example = "rocm";
description = ''
Specifies the device to use for hardware acceleration.
- `cpu`: no acceleration just use the CPU
- `rocm`: supported by modern AMD GPUs
- `cuda`: supported by modern NVIDIA GPUs
- `metal`: supported on darwin aarch64 machines
Tabby will try and determine what type of acceleration that is
already enabled in your configuration when `acceleration = null`.
- nixpkgs.config.cudaSupport
- nixpkgs.config.rocmSupport
- if stdenv.hostPlatform.isDarwin && stdenv.hostPlatform.isAarch64
IFF multiple acceleration methods are found to be enabled or if you
haven't set either `cudaSupport or rocmSupport` you will have to
specify the device type manually here otherwise it will default to
the first from the list above or to cpu.
'';
};
settings = lib.mkOption {
inherit (format) type;
default = { };
description = ''
Tabby scheduler configuration
See for more details:
> https://tabby.tabbyml.com/docs/configuration/#repository-context-for-code-completion
'';
example = lib.literalExpression ''
settings = {
repositories = [
{ name = "tabby"; git_url = "https://github.com/TabbyML/tabby.git"; }
{ name = "CTranslate2"; git_url = "git@github.com:OpenNMT/CTranslate2.git"; }
# local directory is also supported, but limited by systemd DynamicUser=1
# adding local repositories will need to be done manually
{ name = "repository_a"; git_url = "file:///var/lib/tabby/repository_a"; }
];
};
'';
};
usageCollection = lib.mkOption {
type = types.bool;
default = false;
description = ''
Enable sending anonymous usage data.
See for more details:
> https://tabby.tabbyml.com/docs/configuration#usage-collection
'';
};
indexInterval = lib.mkOption {
type = types.str;
default = "5hours";
example = "5hours";
description = ''
Run tabby scheduler to generate the index database at this interval.
Updates by default every 5 hours. This value applies to
`OnUnitInactiveSec`
The format is described in
{manpage}`systemd.time(7)`.
To disable running `tabby scheduler --now` updates, set to `"never"`
'';
};
};
};
# TODO(ghthor): firewall config
config = lib.mkIf cfg.enable {
environment = {
etc."tabby/config.toml".source = format.generate "config.toml" cfg.settings;
systemPackages = [ tabbyPackage ];
};
systemd = let
serviceUser = {
WorkingDirectory = "/var/lib/tabby";
StateDirectory = [ "tabby" ];
ConfigurationDirectory = [ "tabby" ];
DynamicUser = true;
User = "tabby";
Group = "tabby";
};
serviceEnv = lib.mkMerge [
{
TABBY_ROOT = "%S/tabby";
}
(lib.mkIf (!cfg.usageCollection) {
TABBY_DISABLE_USAGE_COLLECTION = "1";
})
];
in {
services.tabby = {
wantedBy = [ "multi-user.target" ];
description = "Self-hosted AI coding assistant using large language models";
after = [ "network.target" ];
environment = serviceEnv;
serviceConfig = lib.mkMerge [
serviceUser
{
ExecStart =
"${lib.getExe tabbyPackage} serve --model ${cfg.model} --port ${toString cfg.port} --device ${tabbyPackage.featureDevice}";
}
];
};
services.tabby-scheduler = lib.mkIf (cfg.indexInterval != "never") {
wantedBy = [ "multi-user.target" ];
description = "Tabby repository indexing service";
after = [ "network.target" ];
environment = serviceEnv;
preStart = "cp -f /etc/tabby/config.toml \${TABBY_ROOT}/config.toml";
serviceConfig = lib.mkMerge [
serviceUser
{
# Type = "oneshot";
ExecStart = "${lib.getExe tabbyPackage} scheduler --now";
}
];
};
timers.tabby-scheduler = lib.mkIf (cfg.indexInterval != "never") {
description = "Update timer for tabby-scheduler";
partOf = [ "tabby-scheduler.service" ];
wantedBy = [ "timers.target" ];
timerConfig.OnUnitInactiveSec = cfg.indexInterval;
};
};
};
meta.maintainers = with lib.maintainers; [ ghthor ];
}
|
