mradermacher/lantern-1.7b-GGUF
2B • Updated • 456
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
id: string
task: string
seed: int64
source: string
use_lessons: bool
finished_at: double
outcome: string
steps: int64
coached: bool
events: list<item: string>
child 0, item: string
model: string
bench: string
journal: string
lessons_in: int64
to
{'id': Value('string'), 'task': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'bench': Value('string'), 'model': Value('string'), 'use_lessons': Value('bool'), 'journal': Value('string'), 'finished_at': Value('float64'), 'outcome': Value('string'), 'steps': Value('int64'), 'lessons_in': Value('int64'), 'events': List(Json(decode=True))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
id: string
task: string
seed: int64
source: string
use_lessons: bool
finished_at: double
outcome: string
steps: int64
coached: bool
events: list<item: string>
child 0, item: string
model: string
bench: string
journal: string
lessons_in: int64
to
{'id': Value('string'), 'task': Value('string'), 'seed': Value('int64'), 'source': Value('string'), 'bench': Value('string'), 'model': Value('string'), 'use_lessons': Value('bool'), 'journal': Value('string'), 'finished_at': Value('float64'), 'outcome': Value('string'), 'steps': Value('int64'), 'lessons_in': Value('int64'), 'events': List(Json(decode=True))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Lantern episodes
Every episode played in Lantern: Beam (and later a distilled 1.7B student) exploring MiniHack dungeon levels through tool calls, with its full reasoning, plans, notes and lessons.
Layout
episodes/{level}/{id}.json, one file per episode, plus journal.json (the Space's current lessons per level).
Each episode has id, task (level), seed, outcome (success, failure, timeout, budget, stalled, stopped, error), steps, use_lessons (journal on/off), source, model, and events:
start: goal, legal actions, the exact system prompt and tool schema, model, prompt version, and the journal lessons read before the run.turn(one per model call): the observation prompt, the model's reasoning, the tool calls it made (planned moves, moves actually done, whether the plan was cut short, the hero's position after every move), its notes, the resulting map, running token usage, and any advice a viewer sent.lessons: the lessons written after the run.end: outcome, steps, token usage.
Sources
bench: the benchmark (tagbench-v2): 8 levels × 5 seeds × journal off/on, all with Beam and prompt version 2. These 80 episodes are the main data.space: live runs started by visitors to the Space.coached: truemarks runs where the visitor sent advice, and that advice text is included.student-eval: the distilled student and the untrained base model on the benchmark seeds.
Episodes with outcome error (infrastructure failures, replayed afterwards) or stopped aren't results; leave them out when computing win rates.
Using it for training
To build supervised training rows, take winning episodes made with the current prompt version and skip coached runs. Each turn becomes one example: the system prompt from start plus that turn's observation as input, and the tool calls the model made as output. This is how Lantern-1.7B was trained.
Privacy
Visitors' IP addresses are never stored. Space episodes contain only the run itself and any advice text a visitor chose to send.
Limitations
- Small: 80 benchmark episodes, 5 seeds per level and condition.
- Beam's reasoning is included as returned by the provider; it isn't edited.
- Level goals include some strategy hints (see the Space README).
License
Apache-2.0. Episodes contain outputs generated by Beam 1.0 (Reflection, Apache-2.0) and Lantern-1.7B, on levels from MiniHack (Apache-2.0).
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