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use clap::{Args, Subcommand};
use super::util::llm_model_completion_parser;
#[derive(Debug, Args)]
pub(crate) struct ModelsArgs {
#[command(subcommand)]
pub command: ModelsCommand,
}
#[derive(Debug, Subcommand)]
pub(crate) enum ModelsCommand {
/// Print resolved metadata for a model alias or model id as JSON.
Info(ModelInfoArgs),
/// Inspect LoRA adapter metadata and compatibility with a Harn model route.
Lora(ModelsLoraArgs),
/// List models grouped by provider.
List(ModelsListArgs),
/// Pull an Ollama model or print setup steps for a known local runtime.
Install(ModelsInstallArgs),
/// Recommend a starter model for the current machine and credentials.
Recommend(ModelRecommendArgs),
/// Round-trip a small prompt through a model and report timing, tokens, and cost.
Test(ModelsTestArgs),
}
#[derive(Debug, Args)]
pub(crate) struct ModelsLoraArgs {
#[command(subcommand)]
pub command: ModelsLoraCommand,
}
#[derive(Debug, Subcommand)]
pub(crate) enum ModelsLoraCommand {
/// Export a tool-calling corpus into a trainer-ready LoRA dataset.
Export(ModelsLoraExportArgs),
/// Inspect a PEFT LoRA adapter directory or repo id.
Inspect(ModelsLoraInspectArgs),
/// Plan a portable LoRA/QLoRA tool-calling fine-tune for a Harn model route.
Plan(ModelsLoraPlanArgs),
}
#[derive(Debug, Args)]
pub(crate) struct ModelsLoraExportArgs {
/// Base model alias or provider-native id the dataset targets.
#[arg(long = "base", value_parser = llm_model_completion_parser(), hide_possible_values = true)]
pub base_model: String,
/// Provider/runtime to plan against instead of inferring from the base model.
#[arg(long)]
pub provider: Option<String>,
/// Tool-call format to export for (`auto`, `native`, `text`, or `json`).
#[arg(long = "tool-format", default_value = "auto")]
pub tool_format: String,
/// Corpus JSONL file, or a directory containing a conventional corpus JSONL.
#[arg(long, value_name = "PATH")]
pub corpus: String,
/// Write exported JSONL rows to this path. Required unless --check is set.
#[arg(long, value_name = "PATH")]
pub out: Option<std::path::PathBuf>,
/// Write a provenance manifest with input/output hashes and export stats.
#[arg(long, value_name = "PATH")]
pub manifest: Option<std::path::PathBuf>,
/// Validate conversion and print a report without writing dataset rows.
#[arg(long)]
pub check: bool,
/// Served LoRA adapter/model name to include in row metadata.
#[arg(long = "adapter-name")]
pub adapter_name: Option<String>,
/// Chat template identifier to include in row metadata.
#[arg(long = "chat-template")]
pub chat_template: Option<String>,
/// Extra target provenance copied into row metadata, as KEY=VALUE.
#[arg(long = "target-metadata", value_name = "KEY=VALUE")]
pub target_metadata: Vec<String>,
/// Emit structured JSON report.
#[arg(long)]
pub json: bool,
}
#[derive(Debug, Args)]
pub(crate) struct ModelsLoraInspectArgs {
/// Base model alias or provider-native id the adapter will attach to.
#[arg(long = "base", value_parser = llm_model_completion_parser(), hide_possible_values = true)]
pub base_model: String,
/// Adapter directory or Hugging Face repo id.
pub adapter: String,
/// Request model name to expose for the adapter. Defaults to the adapter directory/repo basename.
#[arg(long)]
pub name: Option<String>,
/// Provider/runtime to check against instead of inferring from the base model.
#[arg(long)]
pub provider: Option<String>,
/// Emit structured JSON.
#[arg(long)]
pub json: bool,
}
#[derive(Debug, Args)]
pub(crate) struct ModelsLoraPlanArgs {
/// Base model alias or provider-native id to fine-tune.
#[arg(long = "base", value_parser = llm_model_completion_parser(), hide_possible_values = true)]
pub base_model: String,
/// Provider/runtime to plan against instead of inferring from the base model.
#[arg(long)]
pub provider: Option<String>,
/// Tool-call format to train for (`auto`, `native`, `text`, or `json`).
#[arg(long = "tool-format", default_value = "auto")]
pub tool_format: String,
/// Corpus path or dataset id to include in the generated eval/training recipe.
#[arg(long, value_name = "PATH_OR_DATASET")]
pub corpus: Option<String>,
/// Optional teacher model route for synthetic corpus refresh or distillation.
#[arg(long, value_parser = llm_model_completion_parser(), hide_possible_values = true)]
pub teacher: Option<String>,
/// Corpus strategy (`auto`, `audit-only`, `refresh`, or `distill`).
#[arg(long = "corpus-strategy", default_value = "auto")]
pub corpus_strategy: String,
/// Adapter training method (`qlora` or `lora`).
#[arg(long, default_value = "qlora")]
pub method: String,
/// Emit structured JSON.
#[arg(long)]
pub json: bool,
}
#[derive(Debug, Args)]
pub(crate) struct ModelInfoArgs {
/// Verify provider-local readiness for the resolved model when supported.
#[arg(long)]
pub verify: bool,
/// Warm/preload the resolved model when supported. Implies --verify.
#[arg(long)]
pub warm: bool,
/// Ollama keep_alive value to use with --warm (for example 30m, forever, or -1).
#[arg(long = "keep-alive", value_name = "VALUE")]
pub keep_alive: Option<String>,
/// Model alias or provider-native model id.
#[arg(
value_parser = llm_model_completion_parser(),
hide_possible_values = true
)]
pub model: String,
}
#[derive(Debug, Args)]
pub(crate) struct ModelsListArgs {
/// Restrict to a single provider.
#[arg(long)]
pub provider: Option<String>,
/// Emit JSON instead of a human table.
#[arg(long)]
pub json: bool,
/// Only show locally-installed (Ollama) models.
#[arg(long = "installed-only")]
pub installed_only: bool,
}
#[derive(Debug, Args)]
pub(crate) struct ModelsInstallArgs {
/// Model alias or provider-native id to install or set up.
pub model: String,
/// Skip the size-confirmation prompt.
#[arg(long)]
pub yes: bool,
/// Optional Ollama keep-alive hint (e.g. `5m`, `1h`).
#[arg(long = "keep-alive", value_name = "VALUE")]
pub keep_alive: Option<String>,
}
#[derive(Debug, Args)]
pub(crate) struct ModelRecommendArgs {
/// Emit the recommendation and hardware snapshot as JSON.
#[arg(long)]
pub json: bool,
}
#[derive(Debug, Args)]
pub(crate) struct ModelsTestArgs {
/// Model alias or provider-native model id.
pub model: String,
/// Prompt text to send to the model.
#[arg(long, default_value = "Reply with the word pong.")]
pub prompt: String,
/// Provider id to use instead of inferring one from the model selector.
#[arg(long)]
pub provider: Option<String>,
/// Emit a structured JSON result.
#[arg(long, default_value_t = false)]
pub json: bool,
}