Enum ModelOpsCommands 

pub enum ModelOpsCommands {
    Finetune {
        file: Option<PathBuf>,
        method: String,
        rank: Option<u32>,
        vram: f64,
        plan: bool,
        data: Option<PathBuf>,
        output: Option<PathBuf>,
        adapter: Option<PathBuf>,
        merge: bool,
        epochs: u32,
        learning_rate: f64,
        model_size: Option<String>,
    },
    Prune {
        file: PathBuf,
        method: String,
        target_ratio: f32,
        sparsity: f32,
        output: Option<PathBuf>,
        remove_layers: Option<String>,
        analyze: bool,
        plan: bool,
        calibration: Option<PathBuf>,
    },
    Distill {
        teacher: PathBuf,
        student: Option<PathBuf>,
        data: Option<PathBuf>,
        output: Option<PathBuf>,
        strategy: String,
        temperature: f64,
        alpha: f64,
        epochs: u32,
        plan: bool,
    },
}

Variants

Finetune

Fine-tune model with LoRA/QLoRA (GH-244)

Fields

file: Option<PathBuf>

Input model file

method: String

Fine-tuning method: auto, full, lora, qlora

rank: Option<u32>

LoRA rank (default: auto-selected)

vram: f64

Available VRAM in GB

plan: bool

Plan mode (estimate only)

data: Option<PathBuf>

Training data file (JSONL format)

output: Option<PathBuf>

Output path (adapter dir or merged model)

adapter: Option<PathBuf>

Adapter path for merge mode

merge: bool

Merge adapter into base model

epochs: u32

Training epochs

learning_rate: f64

Learning rate

model_size: Option<String>

Model size for planning (e.g., “7B”, “1.5B”)

Prune

Prune model (structured/unstructured pruning) (GH-247)

Fields

file: PathBuf

Input model file

method: String

Pruning method: magnitude, structured, depth, width, wanda, sparsegpt

target_ratio: f32

Target pruning ratio (0-1)

sparsity: f32

Sparsity level (0-1)

output: Option<PathBuf>

Output file path

remove_layers: Option<String>

Layers to remove for depth pruning (e.g., “20-24”)

analyze: bool

Analyze mode (identify pruning opportunities)

plan: bool

Plan mode (estimate only)

calibration: Option<PathBuf>

Calibration data file

Distill

Knowledge distillation (teacher -> student) (GH-247)

Fields

teacher: PathBuf

Teacher model file

student: Option<PathBuf>

Student model file

data: Option<PathBuf>

Training data file

output: Option<PathBuf>

Output file path

strategy: String

Distillation strategy: standard, progressive, ensemble

temperature: f64

Temperature for softmax scaling

alpha: f64

Alpha weight for KL vs task loss

epochs: u32

Training epochs

plan: bool

Plan mode (estimate only)

Trait Implementations

impl Debug for ModelOpsCommands

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

Formats the value using the given formatter.

impl FromArgMatches for ModelOpsCommands

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 ModelOpsCommands

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