Enum TrainingModality 

pub enum TrainingModality {
    Language,
    Timeseries,
    Vision,
    Multimodal,
}

What the training task is training. Modality drives which Python reference-trainer adapter handles inner loops; the protocol layer is modality-agnostic.

Variants

Language

Decoder-only or encoder-decoder language models (Llama, Mistral, T5).

Timeseries

Foundation timeseries forecasting (TimesFM, Chronos, Moirai, TFT, N-BEATS, Mamba). Phase 1’s lead modality.

Vision

Vision encoders/decoders (ViT, ConvNeXt, diffusion U-Nets).

Multimodal

Multimodal towers (CLIP, audio, video).

Implementations

impl TrainingModality

pub fn from_str_lossy(s: &str) -> Option<Self>

Parse from a string label (case-insensitive).

Trait Implementations

impl Clone for TrainingModality

fn clone(&self) -> TrainingModality

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Copy for TrainingModality

impl Debug for TrainingModality

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for TrainingModality

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl Display for TrainingModality

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Eq for TrainingModality

impl Hash for TrainingModality

fn hash<__H: Hasher>(&self, state: &mut __H)

Feeds this value into the given Hasher.

fn hash_slice<H>(data: &[Self], state: &mut H)

where H: Hasher, Self: Sized,

Feeds a slice of this type into the given Hasher.

impl PartialEq for TrainingModality

fn eq(&self, other: &TrainingModality) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Serialize for TrainingModality

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for TrainingModality