Enum Command 

pub enum Command {
    Upload {
        repo: String,
        token: String,
        root: PathBuf,
    },
    Convert {
        model: String,
        output: PathBuf,
        dtype: DtypeArg,
        model_dir: PathBuf,
        token: Option<String>,
        redownload: bool,
    },
    InspectTensors {
        path: PathBuf,
    },
    Infer {
        gguf: PathBuf,
    },
    Delete {
        model: String,
        model_dir: PathBuf,
        output: Option<PathBuf>,
    },
}

Variants

Upload

Upload source + GGUF files to HuggingFace Hub.

Fields

repo: String

token: String

root: PathBuf

Directory tree to upload. Defaults to the current directory.

Convert

Download MOMENT-1-large from HuggingFace and convert to GGUF.

The first conversion downloads the model and writes a canonical F32 GGUF to <model_dir>/<owner>__<name>/model-f32.gguf, deleting the (large) downloaded weight files afterward. Later conversions (any –dtype) recast from that cached F32 GGUF instead of re-downloading — pass –redownload to force a fresh download anyway (e.g. the repo was updated).

Fields

model: String

output: PathBuf

dtype: DtypeArg

model_dir: PathBuf

token: Option<String>

redownload: bool

Force a fresh download even if a cached F32 GGUF already exists.

InspectTensors

Print all tensor names in a local checkpoint file (safetensors, PyTorch pickle, GGUF, or npy/npz — format auto-detected).

Fields

path: PathBuf

Infer

Run MOMENT forecasting from a GGUF file. Univariate, point-forecast only. No –config flag: the architecture is fixed (MomentConfig::default()), unlike chronos/flowstate/toto/ttm, which vary by checkpoint and need config.json.

Reads a JSON request from stdin: {“context”: […], “horizon”: N} Outputs a JSON forecast in OpenAI-compatible format.

Fields

gguf: PathBuf

Delete

Delete cached model files for this model.

Removes the canonical F32 GGUF and config cache at <model_dir>/<owner>__<name>/ created by convert. By default only the cache is removed; pass --output <path> to also delete a converted GGUF file (e.g. gguf/...).

Fields

model: String

HuggingFace repo id (must match the convert –model you used).

model_dir: PathBuf

Directory where the cache was written (must match convert –model-dir).

output: Option<PathBuf>

Also delete this output GGUF file if it exists.

Trait Implementations

impl FromArgMatches for Command

fn from_arg_matches(__clap_arg_matches: &ArgMatches) -> Result<Self, Error>

Instantiate Self from [ArgMatches], parsing the arguments as needed.

fn from_arg_matches_mut( __clap_arg_matches: &mut ArgMatches, ) -> Result<Self, Error>

Instantiate Self from [ArgMatches], parsing the arguments as needed.

fn update_from_arg_matches( &mut self, __clap_arg_matches: &ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

fn update_from_arg_matches_mut<'b>( &mut self, __clap_arg_matches: &mut ArgMatches, ) -> Result<(), Error>

Assign values from ArgMatches to self.

impl Subcommand for Command

fn augment_subcommands<'b>(__clap_app: Command) -> Command

Append to [Command] so it can instantiate Self via [FromArgMatches::from_arg_matches_mut]

fn augment_subcommands_for_update<'b>(__clap_app: Command) -> Command

Append to [Command] so it can instantiate self via [FromArgMatches::update_from_arg_matches_mut]

fn has_subcommand(__clap_name: &str) -> bool

Test whether Self can parse a specific subcommand