harn-stdlib 0.9.7

Embedded Harn standard library source catalog
Documentation
/**
 * `harn models lora plan` renderer.
 *
 * The dispatch shim resolves model aliases, provider capabilities, and catalog
 * launch facts in Rust. This Harn source owns the portable training/eval
 * recipe and the human/JSON presentation.
 *
 * Inputs (from the dispatch shim):
 *   HARN_MODELS_LORA_PLAN_PAYLOAD_JSON   - compact report JSON.
 *   HARN_MODELS_LORA_PLAN_PAYLOAD_PRETTY - pretty report JSON.
 *   HARN_OUTPUT_JSON                     - "1" for JSON, else human text.
 */
import {
  cli_json_envelope,
  nonempty_string,
  print_list,
  render_command,
  safe_bool,
  safe_dict,
  safe_int_string,
  safe_list,
  safe_number_string,
  safe_string,
} from "std/cli/render"

fn __render_human(harness: Harness, report: dict) {
  let base = safe_dict(report["base"])
  let request = safe_dict(report["request"])
  let tool_calling = safe_dict(report["tool_calling"])
  let training = safe_dict(report["training"])
  let training_contract = safe_dict(training["contract"])
  let template = safe_dict(report["template"])
  let data = safe_dict(report["data"])
  let corpus_refresh = safe_dict(report["corpus_refresh"])
  let evaluation = safe_dict(report["evaluation"])
  let serving = safe_dict(report["serving"])
  let launch = safe_dict(report["launch"])
  let warnings = safe_list(report["warnings"])
  let base_id = safe_string(base["id"], "")
  let provider = safe_string(base["provider"], "")
  harness.stdio.println("LoRA plan for " + base_id + " via " + provider)
  harness.stdio.println("  base selector: " + safe_string(base["selector"], ""))
  harness.stdio
    .println(
    "  tool format: "
      + safe_string(request["effective_tool_format"], "")
      + " (requested "
      + safe_string(request["requested_tool_format"], "auto")
      + ")",
  )
  let correction = safe_string(request["tool_format_correction"], "")
  if correction != "" {
    harness.stdio.println("  tool format correction: " + correction)
  }
  let native_tools = if safe_bool(tool_calling["native_tools"], false) {
    "yes"
  } else {
    "no"
  }
  harness.stdio
    .println(
    "  native tools: "
      + native_tools
      + ", preferred: "
      + nonempty_string(tool_calling["preferred_tool_format"], "unset"),
  )
  harness.stdio
    .println(
    "  training: "
      + safe_string(request["method"], "")
      + " + "
      + safe_string(training["adapter_type"], ""),
  )
  harness.stdio.println("  trainer: " + safe_string(training["trainer"], ""))
  harness.stdio
    .println(
    "  LoRA hparams: rank="
      + safe_int_string(training["rank"], "")
      + " alpha="
      + safe_int_string(training["alpha"], "")
      + " dropout="
      + safe_number_string(training["dropout"], ""),
  )
  harness.stdio.println("  quantization: " + safe_string(training["quantization"], ""))
  harness.stdio.println("  loss scope: " + safe_string(training["loss_scope"], ""))
  harness.stdio.println("  packing: " + safe_string(training["packing"], ""))
  harness.stdio
    .println("  assistant mask: " + safe_string(training_contract["assistant_mask_policy"], ""))
  harness.stdio
    .println("  parser owner: " + safe_string(training_contract["tool_parser_owner"], ""))
  harness.stdio
    .println("  split policy: " + safe_string(training_contract["dataset_split_policy"], ""))
  harness.stdio.println("  template: " + safe_string(template["name"], ""))
  harness.stdio.println("  template source: " + safe_string(template["source"], ""))
  harness.stdio.println("  supervised target: " + safe_string(template["supervised_target"], ""))
  let corpus = safe_string(request["corpus"], "")
  if corpus != "" {
    harness.stdio.println("  corpus: " + corpus)
  }
  harness.stdio
    .println(
    "  corpus strategy: "
      + safe_string(request["effective_corpus_strategy"], "")
      + " (requested "
      + safe_string(request["requested_corpus_strategy"], "auto")
      + ")",
  )
  let teacher = safe_dict(request["teacher"])
  let teacher_id = safe_string(teacher["id"], "")
  if teacher_id != "" {
    harness.stdio
      .println(
      "  teacher: "
        + teacher_id
        + " via "
        + safe_string(teacher["provider"], ""),
    )
  }
  harness.stdio.println("  dataset format: " + safe_string(data["dataset_format"], ""))
  print_list(harness, "template requirements", safe_list(template["requirements"]))
  print_list(harness, "stop sequences", safe_list(template["stop_sequences"]))
  print_list(harness, "template notes", safe_list(template["notes"]))
  print_list(harness, "required columns", safe_list(data["required_columns"]))
  print_list(harness, "data validation", safe_list(data["validation"]))
  print_list(harness, "corpus refresh", safe_list(corpus_refresh["generation_notes"]))
  print_list(harness, "provenance manifest", safe_list(corpus_refresh["provenance_manifest_fields"]))
  print_list(harness, "hard negatives", safe_list(corpus_refresh["hard_negative_slices"]))
  print_list(harness, "corpus gates", safe_list(corpus_refresh["acceptance_gates"]))
  print_list(harness, "trainer contract", safe_list(training["trainer_contract"]))
  print_list(harness, "training notes", safe_list(training["notes"]))
  harness.stdio
    .println("  holdout policy: " + safe_string(evaluation["holdout_policy"], ""))
  harness.stdio
    .println("  minimum trials: " + safe_int_string(evaluation["minimum_trials"], "0"))
  harness.stdio
    .println("  comparison baseline: " + safe_string(evaluation["comparison_baseline"], ""))
  print_list(harness, "required metrics", safe_list(evaluation["required_metrics"]))
  print_list(harness, "eval gates", safe_list(evaluation["gates"]))
  harness.stdio.println("  adapter binding: " + safe_string(serving["adapter_binding"], ""))
  harness.stdio
    .println("  LoRA module format: " + safe_string(serving["lora_module_value_format"], ""))
  harness.stdio.println("  request model: " + safe_string(serving["request_model"], ""))
  print_list(harness, "serving notes", safe_list(serving["runtime_notes"]))
  print_list(harness, "promotion gates", safe_list(serving["promotion_gates"]))
  render_command(harness, "export dataset", safe_list(launch["export_command"]))
  render_command(harness, "eval", safe_list(evaluation["eval_command"]))
  render_command(harness, "inspect adapter", safe_list(launch["inspect_command"]))
  render_command(harness, "local launch", safe_list(launch["local_launch_command"]))
  for warning in warnings {
    let text = safe_string(warning, "")
    if text != "" {
      harness.stdio.println("  warning: " + text)
    }
  }
}

fn __render_json(report: dict) -> string {
  let ok = safe_bool(report["ok"], false)
  let envelope = if ok {
    cli_json_envelope({schema_version: 1, ok: true, data: report})
  } else {
    cli_json_envelope(
      {
        schema_version: 1,
        ok: false,
        error: {
          code: "lora_plan_failed",
          message: "LoRA plan did not produce a usable training contract.",
          details: report,
        },
      },
    )
  }
  return json_stringify_pretty(envelope)
}

fn main(harness: Harness) -> int {
  let raw = harness.env.get_or("HARN_MODELS_LORA_PLAN_PAYLOAD_JSON", "")
  if raw == "" {
    harness.stdio.eprintln("internal error: HARN_MODELS_LORA_PLAN_PAYLOAD_JSON not set")
    return 70
  }
  let report = try {
    json_parse(raw)
  } catch (e) {
    harness.stdio.eprintln("internal error: failed to parse LoRA plan payload: " + to_string(e))
    return 70
  }
  let ok = safe_bool(report["ok"], false)
  let json_mode = harness.env.get_or("HARN_OUTPUT_JSON", "0") == "1"
  if json_mode {
    harness.stdio.println(__render_json(report))
  } else {
    __render_human(harness, report)
  }
  if ok {
    return 0
  }
  return 1
}