Relapse: runs, logs and probe sets
Collection
Per-question results, AuditKIT runs, CircuitKIT scores and CuratorKIT-gated probe sets. • 2 items • Updated
Dataset Viewer
The dataset viewer is not available for this subset.
Cannot get the split names for the config 'default' of the dataset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 291, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 364, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 306, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Relapse: runs, per-question results and logs
Everything behind Relapse: is the forgotten knowledge still gone in the 4-bit file? Cohere's Tiny Aya Global, unlearned with Lexsi SafeTune's RMU, scored in seven storage formats.
| Folder | What is in it |
|---|---|
raw/grid_*.json |
One row per model and format: WMDP-bio and MMLU accuracy, probability on the right answer, and accuracy on each probe set (probe:hi, probe:ar, probe:es, probe:sw, probe:en_para). grid_seed1.json is the second training seed |
raw/sweep_*.json |
The tuning sweeps: SafeTune's RMU defaults, the retune, the CircuitKIT-layer settings, and NPO |
raw/robustness_*.json |
Prompt attacks (five worked examples, an expert persona) and the fine-tuning attack (25 LoRA steps on 64 questions), scored on the 1,204 WMDP-bio questions not used as examples |
raw/items_*.json |
Per question: the four answer probabilities on WMDP-bio and the probability on the right answer for MMLU, keyed model|format|set |
raw/audit_*.json |
AuditKIT paired comparisons: forgetting fp16 against Q4_K_M, and the general-knowledge price against the original at the same format (delta, 95% bootstrap interval, p-value, flips) |
audit_runs/ |
The full AuditKIT runs (every prediction, log-likelihoods, fingerprint) |
raw/circuitkit_layers.json, circuitkit/ |
CircuitKIT EAP-IG attribution on WMDP-bio: per-layer MLP and attention scores, node scores, faithfulness report |
raw/bench_*.json |
llama.cpp CPU and transformers T4 speed measurements used in the cost table |
logs/ |
Kaggle kernel logs of the unlearning runs, the sweeps, the attacks and the final grids |
Run names map to the write-up's methods: base = original, rmu_default_layers = RMU at SafeTune's default layers 5 to 7,
rmu_circuit_layers = RMU at the CircuitKIT layers 7 to 9, rmu_circuit_layers_qa = the same trained through a 4-bit
quantizer, rmu_L7_strong = RMU at default layers with 2.5x the learning rate, *_seed1 = the second seed. results/methods.json in the code repository maps every sweep run name to its settings; the edited weights of all 16 runs are in itssaharsh/tiny-aya-global-unlearn-bio-sweep. Formats: fp16, bf16, Q8_0, Q4_0 and nf4 are
exact weight round trips; Q6_K and Q4_K_M are real llama.cpp files.
Rebuild every number on the Space: python scripts/build_results.py --fetch in github.com/itssaharsh/relapse.
- Downloads last month
- -