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"""miniVERL command line interface.
A thin layer: every command parses arguments, calls one library function, and
renders the result. No training logic lives here.
Only the heavy commands import torch, and they do it inside the command body,
so ``miniverl --help``, ``doctor``, ``validate``, ``inspect``, ``report`` and
``cache`` all work from a bare ``pip install miniverl``.
"""
from __future__ import annotations
import hashlib
import json
import sys
import time
from pathlib import Path
from typing import Any, Literal, Optional
import typer
from pydantic import ValidationError
from rich.console import Console
from rich.markup import escape
from rich.table import Table
from miniverl import __version__
from miniverl.commands.compat import compat_app, profiles_app
from miniverl.commands.evidence import hardware_app
from miniverl.commands.qualification import qualification_app
from miniverl.errors import ConfigError, MiniVerlError
from miniverl.utils.runs import make_run_id
app = typer.Typer(
name="miniverl",
help=(
"Compile supported verl experiment semantics for one consumer GPU.\n\n"
"Turn resolved YAML and Parquet prompts into actor rollout, reward/reference/teacher "
"phases and actor updates, then export inspectable PEFT artifacts."
),
add_completion=False,
no_args_is_help=True,
rich_markup_mode=None,
)
cache_app = typer.Typer(help="Inspect and validate a teacher-target cache.", no_args_is_help=True)
app.add_typer(cache_app, name="cache")
bridge_app = typer.Typer(
help="Inspect a pinned, exported verl scale-out bundle.", no_args_is_help=True
)
app.add_typer(bridge_app, name="bridge")
alignment_suite_app = typer.Typer(
help=(
"Prepare, validate and report the pinned external alignment suite. "
"Needs the alignment-benchmarks extra."
),
no_args_is_help=True,
)
app.add_typer(alignment_suite_app, name="alignment-suite")
evidence_app = typer.Typer(
help="Show and validate evidence packaged with the installed wheel.", no_args_is_help=True
)
app.add_typer(evidence_app, name="evidence")
data_app = typer.Typer(help="Create and inspect portable prompt data.", no_args_is_help=True)
app.add_typer(data_app, name="data")
app.add_typer(profiles_app, name="profiles")
app.add_typer(compat_app, name="compat")
app.add_typer(hardware_app, name="hardware")
app.add_typer(qualification_app, name="qualification")
console = Console()
err_console = Console(stderr=True)
_STATUS_STYLE = {"ok": "green", "warn": "yellow", "missing": "yellow", "fail": "red"}
def _emit_json(payload: Any) -> None:
try:
serialized = json.dumps(payload, default=str, allow_nan=False)
except (OverflowError, RecursionError, TypeError, ValueError) as exc:
from miniverl.errors import SerializationError
_fail(SerializationError(f"command result is not finite JSON: {exc}"))
return
console.print_json(serialized)
def _esc(value: object) -> str:
"""Escape dynamic text before it reaches Rich.
Rich treats square brackets as markup, so unescaped values silently lose
content: the hint ``pip install "miniverl[train]"`` would print as
``pip install "miniverl"`` -- the wrong command. Every dynamic string this
module prints goes through here.
"""
return escape(str(value))
def _require_training_stack(purpose: str) -> None:
"""Fail with an install command if the training extra is not present.
Called before any heavy import so a bare ``pip install miniverl`` produces
the exact command to run rather than a bare ``ModuleNotFoundError``.
"""
from miniverl.errors import MissingDependencyError
from miniverl.utils.lazy import have_module
for module in ("torch", "transformers", "peft"):
if not have_module(module):
raise MissingDependencyError(module, "train", purpose)
def _fail(exc: Exception, *, code: int = 1) -> None:
"""Print an actionable error and exit non-zero."""
if isinstance(exc, ModuleNotFoundError):
from miniverl.errors import MissingDependencyError
exc = MissingDependencyError(exc.name or "a dependency", "train", "This command")
if isinstance(exc, MiniVerlError):
err_console.print(f"[red]error[/red] {_esc(exc.message)}")
if exc.hint:
err_console.print(f"[yellow]hint[/yellow] {_esc(exc.hint)}")
else:
err_console.print(f"[red]error[/red] {_esc(exc)}")
raise typer.Exit(code)
def _version_callback(value: bool) -> None:
if value:
console.print(f"miniverl {__version__}")
raise typer.Exit(0)
@app.callback()
def main(
version: bool = typer.Option(
False,
"--version",
"-V",
help="Print the miniVERL version and exit.",
callback=_version_callback,
is_eager=True,
),
log_level: str = typer.Option(
"INFO",
"--log-level",
help="Logging verbosity (DEBUG, INFO, WARNING, ERROR).",
envvar="MINIVERL_LOG_LEVEL",
),
) -> None:
"""miniVERL: verl experiment semantics compiled for one GPU."""
from miniverl.utils.logging import configure_logging
configure_logging(log_level)
@data_app.command("sample")
def data_sample_command(
out: Path = typer.Option(..., "--out", help="Output Parquet path."),
format_name: str = typer.Option(
"verl-parquet", "--format", help="Portable output format (verl-parquet only)."
),
rows: int = typer.Option(4, "--rows", min=1, max=1024, help="Number of sample prompts."),
task_rewards: bool = typer.Option(
False,
"--task-rewards",
help="Add deterministic exact-answer reward metadata for local RL examples.",
),
reward_profile: Optional[str] = typer.Option(
None,
"--reward-profile",
help="Educational reward: exact-answer or target-length.",
),
) -> None:
"""Create a small verl-style Parquet prompt dataset."""
if format_name != "verl-parquet":
_fail(ConfigError("--format must be verl-parquet"))
return
if reward_profile not in {None, "exact-answer", "target-length"} or (
task_rewards and reward_profile
):
_fail(ConfigError("choose --task-rewards or --reward-profile exact-answer/target-length"))
return
task_rewards = task_rewards or reward_profile is not None
try:
import pyarrow as pa
import pyarrow.parquet as pq
except ModuleNotFoundError as exc:
from miniverl.errors import MissingDependencyError
_fail(MissingDependencyError(exc.name or "pyarrow", "bridge", "Parquet sample data"))
return
prompts = [
"Explain why exact provenance matters in one concise sentence.",
"Give one safe way to recover from a CUDA out-of-memory error.",
"What does token-mean loss aggregation mean?",
"State one limitation of a single-GPU training runtime.",
]
rewarded = [
("Reply with exactly the word: provenance", "provenance"),
("Reply with exactly the word: checkpoint", "checkpoint"),
("Reply with exactly the word: adapter", "adapter"),
("Reply with exactly the word: reward", "reward"),
]
records = []
for index in range(rows):
prompt, ground_truth = (
rewarded[index % len(rewarded)]
if task_rewards
else (prompts[index % len(prompts)], None)
)
if index >= len(prompts) and not task_rewards:
prompt = f"Sample {index + 1}: {prompt}"
extra_info: dict[str, Any] = {"sample_index": index}
record: dict[str, Any] = {
"prompt": [
{
"role": "system",
"content": (
"Follow the exact response instruction."
if task_rewards
else "Answer clearly and briefly."
),
},
{"role": "user", "content": prompt},
],
"data_source": "miniverl_quickstart_rl" if task_rewards else "miniverl_quickstart",
"ability": "exact_answer" if task_rewards else "short_answer",
"extra_info": extra_info,
}
if ground_truth is not None:
record["reward_model"] = {"style": "exact", "ground_truth": ground_truth}
extra_info["ground_truth"] = ground_truth
if reward_profile == "target-length":
target = (12, 24, 48, 72)[index % 4]
record["prompt"] = [
{"role": "system", "content": "Answer briefly and directly. /no_think"},
{
"role": "user",
"content": f"Describe checkpoint item {index + 1} in about {target} characters. /no_think",
},
]
record["reward_model"] = {"style": "target_length", "characters": target}
record["ability"] = "target_length"
record["extra_info"] = {"sample_index": index, "target_characters": target}
records.append(record)
out.parent.mkdir(parents=True, exist_ok=True)
pq.write_table(pa.Table.from_pylist(records), out)
digest = hashlib.sha256(out.read_bytes()).hexdigest()
console.print(f"[green]wrote[/green] {_esc(out)} ({rows} rows, sha256 {digest})")
# ---------------------------------------------------------------- doctor
@app.command()
def doctor(
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
output_dir: Path = typer.Option(
Path("runs"), "--output", help="Directory to test for writability."
),
) -> None:
"""Report what this machine can run, and what to install for the rest."""
from miniverl.doctor import run_doctor
try:
report = run_doctor(output_dir)
except Exception as exc:
_fail(exc)
return
if as_json:
_emit_json(report.to_dict())
return
table = Table(title=f"miniVERL {report.miniverl_version} environment", show_lines=False)
table.add_column("check", style="bold")
table.add_column("status")
table.add_column("detail")
for check in report.checks:
table.add_row(
_esc(check.name),
f"[{_STATUS_STYLE.get(check.status, 'white')}]{check.status}[/]",
_esc(check.detail),
)
console.print(table)
verdict = report.to_dict()["verdict"]
console.print()
for label, key, command in (
("core commands (doctor/validate/inspect/report/cache)", "core_commands", None),
(
"CPU + toy training (demo, recipes/toy_cpu.yaml)",
"cpu_training",
'pip install "miniverl[train]"',
),
(
"single-GPU CUDA training (native recipes)",
"gpu_training",
"install a CUDA build of torch",
),
("4-bit QLoRA", "qlora_4bit", 'pip install "miniverl[train,cuda]"'),
):
ready = verdict[key]
# Pad to the width of the longer word so the labels line up in a column.
mark = "[green]yes[/green]" if ready else "[yellow]no [/yellow]"
suffix = "" if ready or not command else f" -> {command}"
console.print(f" {mark} {_esc(label)}{_esc(suffix)}")
hints = [c for c in report.checks if c.hint and c.status in {"fail", "missing", "warn"}]
if hints:
console.print()
console.print("[bold]suggestions[/bold]")
for check in hints:
console.print(f" - {_esc(check.name)}: {_esc(check.hint)}")
# -------------------------------------------------------------- validate
@app.command()
def validate(
recipe: Path = typer.Argument(..., help="Path to a recipe YAML file."),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Validate a recipe without downloading models or allocating memory."""
from miniverl.config import RunConfig
from miniverl.environments.registry import make_environment
try:
config = RunConfig.from_yaml(recipe)
except ValidationError as exc:
if as_json:
_emit_json({"valid": False, "path": str(recipe), "errors": exc.errors()})
raise typer.Exit(1) from None
err_console.print(f"[red]invalid recipe[/red] {_esc(recipe)}")
for error in exc.errors():
location = ".".join(str(p) for p in error["loc"])
err_console.print(f" [red]{_esc(location or '<root>')}[/red]: {_esc(error['msg'])}")
err_console.print(
"\n[yellow]hint[/yellow] compare against recipes/toy_cpu.yaml, which is "
"validated in CI"
)
raise typer.Exit(1) from None
except MiniVerlError as exc:
if as_json:
_emit_json({"valid": False, "path": str(recipe), "errors": [exc.message]})
raise typer.Exit(1) from None
_fail(exc)
return
warnings: list[str] = []
environment_config = config.environment
if environment_config is not None:
try:
environment = make_environment(environment_config.name, **environment_config.params)
if (
config.models.teacher is not None
and config.models.teacher.mode.value == "privileged_context"
and not hasattr(environment, "privileged_context")
):
warnings.append("environment provides no privileged context")
except MiniVerlError as exc:
if as_json:
_emit_json({"valid": False, "path": str(recipe), "errors": [exc.message]})
raise typer.Exit(1) from None
_fail(exc)
return
steps_per_cycle = max(
1,
(config.train.rollouts_per_cycle + config.train.gradient_accumulation_steps - 1)
// config.train.gradient_accumulation_steps,
)
if config.run.mode.value == "opd" and config.train.opd_freshness.value == "replay":
warnings.append(
f"opd_freshness=replay permits {steps_per_cycle} optimizer step(s) per "
"rollout batch; this is online distillation with replay, not genuine OPD"
)
if config.models.backend.value == "hf" and not config.models.student.revision:
warnings.append("models.student.revision is unpinned; the manifest will record 'unpinned'")
if (
config.models.backend.value == "hf"
and config.models.teacher is not None
and not config.models.teacher.revision
):
warnings.append("models.teacher.revision is unpinned")
payload = {
"valid": True,
"path": str(recipe),
"run_name": config.run.name,
"mode": config.run.mode.value,
"is_on_policy": config.is_on_policy,
"opd_freshness": (
config.train.opd_freshness.value if config.run.mode.value == "opd" else None
),
"backend": config.models.backend.value,
"student": config.models.student.model_id,
"teacher": config.models.teacher.model_id if config.models.teacher is not None else None,
"source_kind": config.source.kind.value,
"environment": environment_config.name if environment_config is not None else None,
"difficulty": environment_config.difficulty if environment_config is not None else None,
"objective": (
"sft_cross_entropy"
if config.run.mode.value == "sft"
else (
"online_distillation_with_replay"
if config.run.mode.value == "opd" and not config.is_on_policy
else config.run.mode.value
)
),
"loss_mode": config.loss.mode.value if config.run.mode.value != "sft" else None,
"divergence": config.loss.divergence.value if config.run.mode.value != "sft" else None,
"top_k": config.loss.top_k if config.run.mode.value != "sft" else None,
"selector": config.selection.selector.value,
"memory_strategy": config.memory.strategy.value,
"cycles": config.train.cycles,
"sft_warmup_cycles": config.train.sft_warmup_cycles,
"optimizer_steps_per_cycle": steps_per_cycle,
"planned_optimizer_steps": steps_per_cycle
* (config.train.cycles + config.train.sft_warmup_cycles),
"eval_tasks": (
config.effective_eval_tasks if environment_config is not None else config.eval.tasks
),
"seed": config.run.seed,
"warnings": warnings,
}
if as_json:
_emit_json(payload)
return
console.print(f"[green]valid[/green] {_esc(recipe)}")
table = Table(show_header=False, box=None, pad_edge=False)
for key, value in payload.items():
if key in {"valid", "path", "warnings"}:
continue
table.add_row(f"[dim]{key}[/dim]", _esc(value))
console.print(table)
for warning in warnings:
console.print(f"[yellow]warning[/yellow] {_esc(warning)}")
# ------------------------------------------------------------------ demo
@app.command()
def demo(
output: Path = typer.Option(Path("runs/demo"), "--output", help="Run directory to create."),
fast: bool = typer.Option(False, "--fast", help="Shrink every budget (CI smoke test)."),
overwrite: bool = typer.Option(
False,
"--overwrite",
help="Explicitly replace the whole existing demo run directory.",
),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
report_html: bool = typer.Option(True, "--report/--no-report", help="Also render report.html."),
) -> None:
"""Run the embedded no-network toy pipeline end to end."""
try:
_require_training_stack("miniverl demo")
from miniverl.demo import demo_config
from miniverl.trainer import OPDTrainer
except (MiniVerlError, ModuleNotFoundError) as exc:
_fail(exc)
return
target = Path(output)
try:
config = demo_config(fast=fast, output_dir=target.parent)
if overwrite and target.exists() and not as_json:
console.print(
f"[yellow]overwrite[/yellow] replacing whole run directory {_esc(target)}"
)
with OPDTrainer.from_config(
config,
output_dir=target.parent,
run_id=target.name,
overwrite=overwrite,
) as trainer:
result = trainer.train()
paths = trainer.paths
report_path: Path | None = None
if report_html:
from miniverl.reporting import ReportData, write_markdown, write_report
report_path = write_report(paths.root, paths.report_html)
write_markdown(ReportData.from_run(paths.root), paths.summary_md)
artifacts = _artifact_listing(paths.root)
except MiniVerlError as exc:
_fail(exc)
return
payload = {
**result.to_dict(),
"artifacts": artifacts,
"report": str(report_path) if report_path else None,
}
if as_json:
_emit_json(payload)
return
console.print()
console.print(f"[bold green]demo complete[/bold green] {_esc(paths.root)}")
table = Table(show_header=False, box=None)
baseline = (result.baseline_eval or {}).get("success_rate")
final = (result.eval or {}).get("success_rate")
table.add_row("mode", f"{result.mode} (genuine on-policy distillation)")
table.add_row("optimizer steps", str(result.global_step))
table.add_row("parameter version", str(result.parameter_version))
table.add_row("rollout iterations", str(result.cycles_completed))
table.add_row("wall clock", f"{result.duration_seconds:.1f} s")
provenance = _provenance_summary(paths.trajectories)
if provenance:
table.add_row("token provenance", provenance)
compression = _cache_summary(paths.teacher_cache)
if compression:
table.add_row("teacher cache", compression)
table.add_row(
"task success",
f"{_fmt_pct(baseline)} -> {_fmt_pct(final)} (greedy, held-out eval split)",
)
console.print(table)
console.print()
for line in (
"This demo proves the [bold]machinery[/bold], not capability.",
"At this size the toy student learns the tool-call format and not the",
"arithmetic, so 0% here is the expected outcome, not a failure.",
"For a CPU run that does learn (measured 0.0% -> 91.7% in 192 s):",
" [bold]miniverl train recipes/toy_cpu.yaml[/bold]",
):
console.print(line)
console.print()
console.print("[bold]artifacts[/bold]")
for name, size in artifacts.items():
console.print(f" {_esc(name)} [dim]{_esc(size)}[/dim]")
console.print()
console.print("[bold]next[/bold]")
console.print(f" miniverl inspect {_esc(paths.trajectories)}")
console.print(f" miniverl cache stats {_esc(paths.teacher_cache)}")
console.print(f" miniverl report {_esc(paths.root)} --out {_esc(paths.report_html)}")
def _provenance_summary(path: Path) -> str:
"""One line summarizing which tokens could enter the loss."""
try:
from miniverl.inspection import summarize_file
summary = summarize_file(path, limit=0)
except (MiniVerlError, OSError):
# A cosmetic summary line must never turn a completed run into a failure.
return ""
if not summary.tokens:
return ""
return (
f"{summary.model_tokens} of {summary.tokens} tokens trainable "
f"({summary.model_token_fraction * 100:.0f}%); "
f"{summary.context_tokens} are context and can never be a target"
)
def _cache_summary(path: Path) -> str:
"""One line summarizing the teacher-target cache."""
try:
from miniverl.cache.stats import compute_stats
stats = compute_stats(path, verify_checksums=False)
except (MiniVerlError, OSError):
return ""
if not stats.get("selected_positions"):
return ""
return (
f"{stats['selected_positions']} scored positions, "
f"{stats['actual_bytes'] / 1024:.1f} KiB on disk, "
f"{stats['compression_ratio']:.1f}x smaller than a dense fp16 dump"
)
def _fmt_pct(value: Any) -> str:
try:
return f"{float(value) * 100:.1f}%"
except (TypeError, ValueError):
return "n/a"
def _artifact_listing(root: Path) -> dict[str, str]:
out: dict[str, str] = {}
for path in sorted(root.rglob("*")):
if path.is_file():
rel = path.relative_to(root).as_posix()
out[rel] = f"{path.stat().st_size} B"
return out
# ----------------------------------------------------- prepare-offline-kd
@app.command("prepare-offline-kd")
def prepare_offline_kd_command(
recipe: Path = typer.Option(..., "--recipe", help="Frozen-student offline-KD recipe."),
checkpoint: Path = typer.Option(
..., "--checkpoint", help="Exact shared cold-start checkpoint directory."
),
out: Path = typer.Option(..., "--out", help="New immutable dataset bundle directory."),
offline: bool = typer.Option(
False, "--offline", help="Refuse network access; use only cached model files."
),
dry_run: bool = typer.Option(
False,
"--dry-run",
help="Validate and print the collection plan without loading models or writing files.",
),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Collect one frozen-student trajectory/teacher-target dataset."""
from miniverl.config import OfflineKDTrajectorySource, RunConfig, TrainingMode
try:
config = RunConfig.from_yaml(recipe)
if config.run.mode is not TrainingMode.OFFLINE_KD:
raise ConfigError("prepare-offline-kd requires run.mode=offline_kd")
if config.offline_kd.trajectory_source is not OfflineKDTrajectorySource.FROZEN_STUDENT:
raise ConfigError(
"prepare-offline-kd requires offline_kd.trajectory_source=frozen_student"
)
if not checkpoint.is_dir():
raise ConfigError(f"checkpoint directory not found: {checkpoint}")
if out.exists():
raise ConfigError(
f"output already exists: {out}",
hint="choose a new directory; frozen datasets are never overwritten",
)
except (ValidationError, MiniVerlError) as exc:
if isinstance(exc, MiniVerlError):
_fail(exc)
err_console.print(f"[red]invalid recipe[/red] {_esc(recipe)}\n{_esc(exc)}")
raise typer.Exit(1) from None
plan = {
"dry_run": dry_run,
"recipe": str(recipe),
"checkpoint": str(checkpoint),
"out": str(out),
"offline": offline,
"trajectory_source": config.offline_kd.trajectory_source.value,
"collection_seed": config.offline_kd.collection_seed,
"collection_tasks": (config.offline_kd.collection_tasks or config.train.rollouts_per_cycle),
"student": config.models.student.model_id,
"student_revision": config.models.student.revision,
"teacher": config.models.teacher.model_id if config.models.teacher is not None else None,
"teacher_revision": (
config.models.teacher.revision if config.models.teacher is not None else None
),
}
if dry_run:
if as_json:
_emit_json(plan)
else:
console.print(f"[green]dry run ok[/green] {_esc(recipe)}")
for key, value in plan.items():
console.print(f" [dim]{key}[/dim] {_esc(value)}")
console.print("\nNo models were loaded, files written, or downloads attempted.")
return
try:
_require_training_stack("miniverl prepare-offline-kd")
from miniverl.trainer import OPDTrainer
from miniverl.training.checkpoint import load_checkpoint, validate_checkpoint
validated = validate_checkpoint(checkpoint)
with OPDTrainer.from_config(
config,
output_dir=out.parent,
run_id=out.name,
local_files_only=offline,
) as trainer:
load_checkpoint(
checkpoint,
backend=trainer.student,
optimizer=None,
device=trainer.student.device,
include_optimizer=False,
include_rng=False,
expected_identity=(trainer._checkpoint_identity() if validated.identity else None),
)
trainer.set_offline_collection_checkpoint_digest(validated.content_digest)
summary = trainer.prepare_offline_dataset()
destination = trainer.paths.root
except (MiniVerlError, ModuleNotFoundError) as exc:
_fail(exc)
return
payload = {**plan, "written": str(destination), **summary}
if as_json:
_emit_json(payload)
return
console.print(f"[green]offline dataset prepared[/green] {_esc(destination)}")
console.print(f" digest {_esc(summary['dataset_digest'])}")
console.print(f" trajectories {_esc(summary['trajectories'])}")
# --------------------------------------------------------- qualify-teacher
@app.command("qualify-teacher")
def qualify_teacher_command(
recipe: Path = typer.Option(..., "--recipe", help="Recipe defining the frozen teacher."),
candidate: str = typer.Option(..., "--candidate", help="Preregistered candidate id."),
out: Path = typer.Option(..., "--out", help="New immutable gate-result directory."),
tasks: Optional[int] = typer.Option(None, "--tasks", help="Eval-task count override."),
offline: bool = typer.Option(
False, "--offline", help="Refuse network access; use only cached model files."
),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Evaluate one RecoveryBench teacher candidate on eval only."""
from miniverl.config import RunConfig
try:
config = RunConfig.from_yaml(recipe)
if config.require_environment("qualify-teacher").name != "sqlite_recovery":
raise ConfigError("qualify-teacher requires environment.name=sqlite_recovery")
_require_training_stack("miniverl qualify-teacher")
from miniverl.evaluation.teacher_gate import evaluate_teacher_candidate
result = evaluate_teacher_candidate(
config,
candidate_id=candidate,
out=out,
tasks=tasks,
split="eval",
local_files_only=offline,
)
except (ValidationError, MiniVerlError, ModuleNotFoundError) as exc:
if isinstance(exc, ValidationError):
err_console.print(f"[red]invalid recipe[/red] {_esc(recipe)}\n{_esc(exc)}")
raise typer.Exit(1) from None
_fail(exc)
return
if as_json:
_emit_json(result)
return
status = "passed" if result["gate"]["passed"] else "failed"
style = "green" if result["gate"]["passed"] else "yellow"
console.print(f"[{style}]teacher gate {status}[/{style}] {_esc(candidate)}")
console.print(f" result {_esc(out / 'result.json')}")
# ------------------------------------------------------------- evidence
@evidence_app.command("show")
def evidence_show(
study_id: str = typer.Argument(..., help="Packaged study identifier."),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Show a packaged, typed study result without a repository checkout."""
from miniverl.evidence import show_builtin_study
try:
payload = show_builtin_study(study_id)
except (MiniVerlError, OSError, ValidationError) as exc:
_fail(exc)
return
if as_json:
_emit_json(payload)
return
console.print(f"[bold]{_esc(study_id)}[/bold]")
console.print_json(json.dumps(payload["result"], allow_nan=False))
@evidence_app.command("validate")
def evidence_validate(
study_id: str = typer.Argument(..., help="Packaged study identifier."),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Validate packaged result, schema, preregistration and task evidence."""
from miniverl.evidence import validate_builtin_study
try:
payload = validate_builtin_study(study_id)
except (MiniVerlError, OSError, ValidationError) as exc:
_fail(exc)
return
if as_json:
_emit_json(payload)
elif payload["valid"]:
console.print(
f"[green]valid[/green] {_esc(study_id)} · {_esc(payload['task_rows'])} task rows"
)
else:
for problem in payload["problems"]:
err_console.print(f"[red]invalid[/red] {_esc(problem)}")
raise typer.Exit(1)
# ----------------------------------------------------------------- pilot
@app.command()
def pilot(
recipe: Optional[Path] = typer.Argument(
None, help="Alignment recipe containing bounded pilot evidence."
),
study_result: Optional[Path] = typer.Option(
None,
"--study-result",
help="Schema-validated external-study result; does not load a model.",
),
builtin_study: Optional[str] = typer.Option(
None,
"--builtin-study",
help="Packaged external-study result; works from an installed wheel.",
),
out: Optional[Path] = typer.Option(None, "--out", help="Optional JSON output path."),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Recommend a method from explicit pilot evidence without loading a model."""
from miniverl.utils.runs import write_json_atomic
payload: dict[str, Any]
try:
selected = sum(value is not None for value in (recipe, study_result, builtin_study))
if selected > 1:
raise ConfigError(
"miniverl pilot accepts exactly one of a recipe, --study-result, or --builtin-study"
)
builtin = None
if builtin_study is not None:
from miniverl.evidence import get_builtin_study
builtin = get_builtin_study(builtin_study)
study_result = builtin.result_path
if study_result is not None:
from miniverl.alignment_external.result import load_alignment_external_result
result = load_alignment_external_result(study_result)
if result.study_status != "terminated_at_checkpoint_selection":
raise ConfigError(
"this pilot evidence path currently requires "
"study_status=terminated_at_checkpoint_selection"
)
payload = {
"study_status": result.study_status,
"recommendation": "do_not_continue",
"recommendation_scope": "do_not_continue_this_study",
"method_recommendation": "insufficient_evidence",
"reasons": [
"no candidate satisfied the retained-utility gate",
"no starting checkpoint was selected",
"teacher qualification was not run",
"no continuation method was authorized",
"the reserved final test was not accessed",
],
"evidence": {
"path": str(study_result),
"builtin_study": builtin.study_id if builtin is not None else None,
"sha256": hashlib.sha256(study_result.read_bytes()).hexdigest(),
"preregistration": result.preregistration.model_dump(mode="json"),
"task_evidence": result.checkpoint_selection.task_evidence.model_dump(
mode="json"
),
},
"universal_claim": False,
}
else:
if recipe is None:
raise ConfigError(
"miniverl pilot requires a recipe, --study-result or --builtin-study"
)
from miniverl.alignment import PilotEvidence, recommend_alignment_method
from miniverl.config import RunConfig
config = RunConfig.from_yaml(recipe)
if config.alignment is None:
raise ConfigError("miniverl pilot requires a recipe with an alignment section")
evidence = config.alignment.pilot or PilotEvidence()
recommendation = recommend_alignment_method(evidence)
payload = recommendation.model_dump(mode="json")
if out is not None:
write_json_atomic(out, payload)
except (ValidationError, MiniVerlError) as exc:
if isinstance(exc, MiniVerlError):
_fail(exc)
source = recipe if recipe is not None else study_result or builtin_study
err_console.print(f"[red]invalid pilot evidence[/red] {_esc(source)}\n{_esc(exc)}")
raise typer.Exit(1) from None
if as_json:
_emit_json(payload)
return
recommendation_text = payload.get("method_recommendation", payload["recommendation"])
console.print(f"[bold]recommendation[/bold] {_esc(recommendation_text)}")
for reason in payload["reasons"]:
console.print(f" - {_esc(reason)}")
if out is not None:
console.print(f" evidence {_esc(out)}")
@app.command()
def align(
recipe: Path = typer.Argument(..., help="Path to a post-SFT alignment recipe YAML file."),
output: Optional[Path] = typer.Option(
None, "--output", help="Parent directory for the run (default: run.output_dir)."
),
run_id: Optional[str] = typer.Option(None, "--run-id", help="Explicit run id."),
overwrite: bool = typer.Option(
False,
"--overwrite",
help="Explicitly replace the whole target run directory.",
),
dry_run: bool = typer.Option(
False,
"--dry-run",
help="Validate and print all alignment stages without loading models.",
),
offline: bool = typer.Option(
False, "--offline", help="Refuse network access; use only cached model files."
),
as_json: bool = typer.Option(False, "--json", help="Emit machine-readable JSON."),
) -> None:
"""Run base -> SFT checkpoint -> teacher -> alignment -> eval -> card."""
from miniverl.alignment import build_alignment_stage_plan
from miniverl.config import RunConfig
try:
config = RunConfig.from_yaml(recipe)
if config.alignment is None:
raise ConfigError("miniverl align requires a recipe with an alignment section")
except (ValidationError, MiniVerlError) as exc:
if isinstance(exc, MiniVerlError):
_fail(exc)
err_console.print(f"[red]invalid recipe[/red] {_esc(recipe)}\n{_esc(exc)}")
raise typer.Exit(1) from None
workflow = build_alignment_stage_plan(
config.alignment,
sft_warmup_cycles=config.train.sft_warmup_cycles,
)
if dry_run:
payload = {
"dry_run": True,
"recipe": str(recipe),
"method": config.alignment.method.value,
"workflow": workflow,
"backend": config.models.backend.value,
"downloads_required": config.models.backend.value == "hf",
"output_dir": str(output or config.run.output_dir),
}
if as_json:
_emit_json(payload)
else:
console.print(f"[green]alignment dry run ok[/green] {_esc(recipe)}")
for stage in workflow["stages"]:
console.print(f" - {_esc(stage['name'])}")
console.print("\nNo models were loaded and nothing was downloaded.")
return
try:
_require_training_stack("miniverl align")
from miniverl.alignment import run_alignment
payload = run_alignment(
config,
output_dir=output,
run_id=run_id,
local_files_only=offline,
overwrite=overwrite,
)
except (MiniVerlError, ModuleNotFoundError) as exc:
_fail(exc)
return
if as_json:
_emit_json(payload)
return
console.print(f"[bold green]alignment complete[/bold green] {_esc(payload['run_dir'])}")
console.print(f" card {_esc(Path(str(payload['run_dir'])) / 'alignment-card.md')}")
# ---------------------------------------------------------- verl-shaped plan/run
@app.command(
"plan",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def plan_command(
ctx: typer.Context,
config: str = typer.Option(..., "--config", help="Resolved YAML path or builtin profile."),
profile: str = typer.Option(
"verl-opd-v0.8-single-gpu-v1", "--profile", help="Pinned compatibility profile."
),
rollout_backend: Optional[str] = typer.Option(
None,
"--rollout-backend",
help="Local execution backend: hf_reference, hf_cached, or vllm.",
),
overrides: list[str] = typer.Option([], "--set", help="Repeatable dotted key=value override."),
override_files: list[Path] = typer.Option(
[],
"--overrides-file",
help="Repeatable plain key=value file or JSON argv array.",
),
accept_local_reinterpretations: bool = typer.Option(
False,
"--accept-local-reinterpretations",