mirror of
https://github.com/open-jarvis/OpenJarvis.git
synced 2026-07-30 19:02:16 +00:00
Jon Saad-FalconandClaude Opus 4.6
2aebcd7d77
Trace-driven learning pipeline: - TrainingDataMiner: extract SFT/routing/agent pairs from traces - LoRATrainer: fine-tune local models from trace-derived data - AgentConfigEvolver: rewrite agent configs from trace analysis - LearningOrchestrator: coordinate mine→train→evolve cycle, wired into SystemBuilder Eval framework (15 real IPW benchmarks): - Datasets: SuperGPQA, GPQA, MMLU-Pro, MATH-500, Natural Reasoning, HLE, SimpleQA, WildChat, IPW, GAIA, FRAMES, SWE-bench, SWEfficiency, TerminalBench, TerminalBench Native - Scorers: MCQ extraction, LLM-judge, exact match, structural validation - CLI: jarvis eval list|run|compare|report Composable abstractions: - Recipe system: TOML composition of all 5 pillars (3 built-in recipes) - Agent templates: 15 pre-configured TOML manifests with system prompts - Bundled skills: 20 ready-to-use TOML skill manifests - Operator recipes: researcher (4h), correspondent (5min), sentinel (2h) 102 files changed, ~11,500 lines added. 3241 tests pass (44 skipped). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
125 lines
3.3 KiB
Python
125 lines
3.3 KiB
Python
"""TerminalBench dataset (terminal-bench/terminal-bench).
|
|
|
|
Agentic benchmark for terminal / command-line tasks.
|
|
"""
|
|
|
|
from __future__ import annotations
|
|
|
|
import random
|
|
from typing import Iterable, List, MutableMapping, Optional, Sequence
|
|
|
|
from evals.core.dataset import DatasetProvider
|
|
from evals.core.types import EvalRecord
|
|
|
|
try:
|
|
from datasets import load_dataset as _load_dataset # noqa: F401
|
|
|
|
_HAS_DATASETS = True
|
|
except ImportError:
|
|
_HAS_DATASETS = False
|
|
|
|
_HF_PATH = "terminal-bench/terminal-bench"
|
|
|
|
|
|
class TerminalBenchDataset(DatasetProvider):
|
|
"""TerminalBench agentic terminal benchmark (HuggingFace variant)."""
|
|
|
|
dataset_id = "terminalbench"
|
|
dataset_name = "TerminalBench"
|
|
|
|
_hf_path = _HF_PATH
|
|
_default_split = "test"
|
|
|
|
def __init__(self) -> None:
|
|
self._records: List[EvalRecord] = []
|
|
|
|
def load(
|
|
self,
|
|
*,
|
|
max_samples: Optional[int] = None,
|
|
split: Optional[str] = None,
|
|
seed: Optional[int] = None,
|
|
) -> None:
|
|
if not _HAS_DATASETS:
|
|
raise ImportError(
|
|
"The 'datasets' package is required for TerminalBenchDataset. "
|
|
"Install it with: pip install datasets"
|
|
)
|
|
from datasets import load_dataset
|
|
|
|
use_split = split or self._default_split
|
|
dataset = load_dataset(self._hf_path, split=use_split)
|
|
|
|
rows: Sequence[MutableMapping[str, object]]
|
|
if hasattr(dataset, "to_list"):
|
|
rows = dataset.to_list()
|
|
else:
|
|
rows = list(dataset)
|
|
|
|
if seed is not None:
|
|
rng = random.Random(seed)
|
|
rows = list(rows)
|
|
rng.shuffle(rows)
|
|
|
|
if max_samples is not None:
|
|
rows = rows[:max_samples]
|
|
|
|
self._records = []
|
|
for idx, raw in enumerate(rows):
|
|
record = self._convert_row(raw, idx)
|
|
if record is not None:
|
|
self._records.append(record)
|
|
|
|
def iter_records(self) -> Iterable[EvalRecord]:
|
|
return iter(self._records)
|
|
|
|
def size(self) -> int:
|
|
return len(self._records)
|
|
|
|
def _convert_row(
|
|
self, raw: MutableMapping[str, object], idx: int,
|
|
) -> Optional[EvalRecord]:
|
|
# Try multiple field name variants for question
|
|
question = str(
|
|
raw.get("prompt")
|
|
or raw.get("question")
|
|
or raw.get("instruction")
|
|
or ""
|
|
).strip()
|
|
|
|
# Try multiple field name variants for answer
|
|
answer = str(
|
|
raw.get("answer")
|
|
or raw.get("expected_output")
|
|
or raw.get("gold_answer")
|
|
or ""
|
|
).strip()
|
|
|
|
if not question:
|
|
return None
|
|
|
|
# Category / type
|
|
category_raw = raw.get("category", raw.get("type", "terminal"))
|
|
category_str = str(category_raw) if category_raw else "terminal"
|
|
|
|
# Task identifier
|
|
task_id_raw = raw.get("id", raw.get("task_id"))
|
|
task_id = str(task_id_raw) if task_id_raw else f"tb_{idx}"
|
|
|
|
metadata = {
|
|
"task_id": task_id,
|
|
"original_category": category_str,
|
|
}
|
|
|
|
return EvalRecord(
|
|
record_id=f"terminalbench-{task_id}",
|
|
problem=question,
|
|
reference=answer,
|
|
category="agentic",
|
|
subject=category_str,
|
|
metadata=metadata,
|
|
)
|
|
|
|
|
|
__all__ = ["TerminalBenchDataset"]
|