Datasets:

Jakubrd4
/

bielik-q2-sharp-docs

+I0222 13:18:21.574831 42711 utils.py:148] Note: detected 192 virtual cores but NumExpr set to maximum of 64, check "NUMEXPR_MAX_THREADS" environment variable.
+I0222 13:18:21.574902 42711 utils.py:151] Note: NumExpr detected 192 cores but "NUMEXPR_MAX_THREADS" not set, so enforcing safe limit of 16.
+I0222 13:18:21.574939 42711 utils.py:164] NumExpr defaulting to 16 threads.
+I0222 13:18:21.659013 42711 config.py:58] PyTorch version 2.10.0+cu126 available.
+W0222 13:18:21.787467 42711 warnings.py:112] /dev/shm/eval/quip-sharp/lib/codebook/__init__.py:6: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
+  @torch.library.impl_abstract("quip_lib::decode_matvec_e8p")
+W0222 13:18:21.824131 42711 warnings.py:112] /dev/shm/eval/quip-sharp/lib/codebook/__init__.py:25: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
+  @torch.library.impl_abstract("quip_lib::decompress_packed_e8p")
+W0222 13:18:21.833210 42711 warnings.py:112] /dev/shm/eval/quip-sharp/lib/utils/matmul_had.py:96: FutureWarning: `torch.library.impl_abstract` was renamed to `torch.library.register_fake`. Please use that instead; we will remove `torch.library.impl_abstract` in a future version of PyTorch.
+  @torch.library.impl_abstract("quip_lib::hadamard")
+[13:18:41] ============================================================
+[13:18:41]   Polish LLM Leaderboard Eval
+[13:18:41]   Model: QuIP# Bielik-Q2-Sharp Variant A
+[13:18:41]   lm-eval API: new
+[13:18:41]   Tasks: ['polish_eq_bench', 'polish_eq_bench_first_turn']
+[13:18:41]   Few-shot: 5
+[13:18:41] ============================================================
+[13:18:41] Loading QuIP# model from /dev/shm/eval/model...
+I0222 13:18:43.864685 42711 modeling.py:987] We will use 90% of the memory on device 0 for storing the model, and 10% for the buffer to avoid OOM. You can set `max_memory` in to a higher value to use more memory (at your own risk).
+[13:18:45] Model loaded in 3.3s
+[13:18:45] Tokenizer: speakleash/Bielik-11B-v2.3-Instruct (vocab=32000)
+[13:18:45]
+Running 2 tasks...
+I0222 13:18:45.618126 42711 evaluator.py:152] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234
+I0222 13:18:45.618192 42711 evaluator.py:203] Using pre-initialized model
+Traceback (most recent call last):
+  File "/dev/shm/eval/eval_polish_quip.py", line 481, in <module>
+    main()
+  File "/dev/shm/eval/eval_polish_quip.py", line 414, in main
+    results = evaluator.simple_evaluate(
+              ^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/utils.py", line 346, in _wrapper
+    return fn(*args, **kwargs)
+           ^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/evaluator.py", line 221, in simple_evaluate
+    task_dict = get_task_dict(tasks, task_manager)
+                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/tasks/__init__.py", line 423, in get_task_dict
+    task_name_from_string_dict = task_manager.load_task_or_group(
+                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/tasks/__init__.py", line 271, in load_task_or_group
+    collections.ChainMap(*map(self._load_individual_task_or_group, task_list))
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/tasks/__init__.py", line 162, in _load_individual_task_or_group
+    return load_task(task_config, task=name_or_config, group=parent_name)
+           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/tasks/__init__.py", line 151, in load_task
+    task_object = ConfigurableTask(config=config)
+                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/api/task.py", line 809, in __init__
+    self.download(self.config.dataset_kwargs)
+  File "/dev/shm/eval/lm-evaluation-harness/lm_eval/api/task.py", line 916, in download
+    self.dataset = datasets.load_dataset(
+                   ^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 2594, in load_dataset
+    builder_instance = load_dataset_builder(
+                       ^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 2266, in load_dataset_builder
+    dataset_module = dataset_module_factory(
+                     ^^^^^^^^^^^^^^^^^^^^^^^
+  File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 1908, in dataset_module_factory
+    raise e1 from None
+  File "/venv/main/lib/python3.12/site-packages/datasets/load.py", line 1858, in dataset_module_factory
+    raise DatasetNotFoundError(f"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.") from e
+datasets.exceptions.DatasetNotFoundError: Dataset 'speakleash/EQ-Bench-PL' doesn't exist on the Hub or cannot be accessed.