Qwen2.5 Technical Report
Paper • 2412.15115 • Published • 381
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
alora_invocation_tokens: null
alpha_pattern: struct<>
arrow_config: null
auto_mapping: null
base_model_name_or_path: string
bias: string
corda_config: null
ensure_weight_tying: bool
eva_config: null
exclude_modules: null
fan_in_fan_out: bool
inference_mode: bool
init_lora_weights: bool
kasa_config: null
layer_replication: null
layers_pattern: null
layers_to_transform: null
loftq_config: struct<>
lora_alpha: int64
lora_bias: bool
lora_dropout: double
lora_ga_config: null
megatron_config: null
megatron_core: string
modules_to_save: null
monteclora_config: null
peft_type: string
peft_version: string
qalora_group_size: int64
r: int64
rank_pattern: struct<>
revision: null
target_modules: list<item: string>
child 0, item: string
target_parameters: null
task_type: string
trainable_token_indices: null
use_bdlora: null
use_dora: bool
use_qalora: bool
use_rslora: bool
velora_config: null
calibration: list<item: struct<answer: int64, id: int64, question: string, solution: string>>
child 0, item: struct<answer: int64, id: int64, question: string, solution: string>
child 0, answer: int64
child 1, id: int64
child 2, question: string
child 3, solution: string
calibration_pool: list<item: struct<answer: int64, id: int64, question: string, solution: string>>
child 0, item: struct<answer: int64, id: int64, question: string, solution: string>
child 0, answer: int64
child 1, id: int64
child 2, question: string
child 3, solution: string
runs:
...
m: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
child 1, int4: struct<counts: list<item: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
child 2, int4_cal: struct<counts: list<item: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
runs_dev: struct<S0: struct<finished: list<item: bool>, output_tokens: list<item: int64>, prompt_tokens: list< (... 39 chars omitted)
child 0, S0: struct<finished: list<item: bool>, output_tokens: list<item: int64>, prompt_tokens: list<item: int64 (... 27 chars omitted)
child 0, finished: list<item: bool>
child 0, item: bool
child 1, output_tokens: list<item: int64>
child 0, item: int64
child 2, prompt_tokens: list<item: int64>
child 0, item: int64
child 3, raw: list<item: string>
child 0, item: string
test200: list<item: int64>
child 0, item: int64
lora_train: list<item: struct<assistant: string, id: int64, question: string>>
child 0, item: struct<assistant: string, id: int64, question: string>
child 0, assistant: string
child 1, id: int64
child 2, question: string
to
{'audit': List(Value('int64')), 'calibration': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'calibration_pool': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'dev': List(Value('int64')), 'exemplars': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'full': List(Value('int64')), 'log_likelihood': {'base': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}, 'int4': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}, 'int4_cal': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}}, 'lora_train': List({'assistant': Value('string'), 'id': Value('int64'), 'question': Value('string')}), 'problems': {'0': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '10': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '100': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1000': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1001': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1002': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1
...
ens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1@64': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1~1': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1~2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2-self': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'runs_200': {'S0': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'runs_dev': {'S0': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'source': Value('string'), 'test200': List(Value('int64'))}
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
alora_invocation_tokens: null
alpha_pattern: struct<>
arrow_config: null
auto_mapping: null
base_model_name_or_path: string
bias: string
corda_config: null
ensure_weight_tying: bool
eva_config: null
exclude_modules: null
fan_in_fan_out: bool
inference_mode: bool
init_lora_weights: bool
kasa_config: null
layer_replication: null
layers_pattern: null
layers_to_transform: null
loftq_config: struct<>
lora_alpha: int64
lora_bias: bool
lora_dropout: double
lora_ga_config: null
megatron_config: null
megatron_core: string
modules_to_save: null
monteclora_config: null
peft_type: string
peft_version: string
qalora_group_size: int64
r: int64
rank_pattern: struct<>
revision: null
target_modules: list<item: string>
child 0, item: string
target_parameters: null
task_type: string
trainable_token_indices: null
use_bdlora: null
use_dora: bool
use_qalora: bool
use_rslora: bool
velora_config: null
calibration: list<item: struct<answer: int64, id: int64, question: string, solution: string>>
child 0, item: struct<answer: int64, id: int64, question: string, solution: string>
child 0, answer: int64
child 1, id: int64
child 2, question: string
child 3, solution: string
calibration_pool: list<item: struct<answer: int64, id: int64, question: string, solution: string>>
child 0, item: struct<answer: int64, id: int64, question: string, solution: string>
child 0, answer: int64
child 1, id: int64
child 2, question: string
child 3, solution: string
runs:
...
m: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
child 1, int4: struct<counts: list<item: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
child 2, int4_cal: struct<counts: list<item: double>, sums: list<item: double>>
child 0, counts: list<item: double>
child 0, item: double
child 1, sums: list<item: double>
child 0, item: double
runs_dev: struct<S0: struct<finished: list<item: bool>, output_tokens: list<item: int64>, prompt_tokens: list< (... 39 chars omitted)
child 0, S0: struct<finished: list<item: bool>, output_tokens: list<item: int64>, prompt_tokens: list<item: int64 (... 27 chars omitted)
child 0, finished: list<item: bool>
child 0, item: bool
child 1, output_tokens: list<item: int64>
child 0, item: int64
child 2, prompt_tokens: list<item: int64>
child 0, item: int64
child 3, raw: list<item: string>
child 0, item: string
test200: list<item: int64>
child 0, item: int64
lora_train: list<item: struct<assistant: string, id: int64, question: string>>
child 0, item: struct<assistant: string, id: int64, question: string>
child 0, assistant: string
child 1, id: int64
child 2, question: string
to
{'audit': List(Value('int64')), 'calibration': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'calibration_pool': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'dev': List(Value('int64')), 'exemplars': List({'answer': Value('int64'), 'id': Value('int64'), 'question': Value('string'), 'solution': Value('string')}), 'full': List(Value('int64')), 'log_likelihood': {'base': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}, 'int4': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}, 'int4_cal': {'counts': List(Value('float64')), 'sums': List(Value('float64'))}}, 'lora_train': List({'assistant': Value('string'), 'id': Value('int64'), 'question': Value('string')}), 'problems': {'0': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '10': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '100': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1000': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1001': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1002': {'answer': Value('int64'), 'question': Value('string'), 'solution': Value('string')}, '1
...
ens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1@64': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1~1': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1~2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2-self': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'runs_200': {'S0': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S1': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}, 'S2': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'runs_dev': {'S0': {'finished': List(Value('bool')), 'output_tokens': List(Value('int64')), 'prompt_tokens': List(Value('int64')), 'raw': List(Value('string'))}}, 'source': Value('string'), 'test200': List(Value('int64'))}
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.
CV & NLP Lab 2: language model systems
Teaching data for Lab 2 of the MIT CV & NLP MicroMasters course (6.4600.2x), "Language Model Systems". Learners prompt, fine-tune and quantize Qwen2.5-0.5B-Instruct on grade-school math problems and compare the systems on the same problems. The bundle holds the problems and the outputs of every system the lab compares, so a learner can read and score any system without rerunning it.
Files
| File | Holds |
|---|---|
lab2_data.json.gz |
gzipped JSON: the problems and their splits, the solutions used for fine-tuning and calibration, every cached run, and the log-likelihoods of the gold solutions |
reference_adapter/ |
a LoRA adapter for Qwen2.5-0.5B-Instruct, in the PEFT format, for a learner whose own training run fails |
Key in lab2_data.json.gz |
Holds |
|---|---|
problems |
1,319 GSM8K test problems by ID: question, written solution and final answer |
full, test200, dev |
the IDs of the 1,269 scored problems, the 200-problem panel and the 50 development problems |
exemplars |
the 4 worked examples the few-shot prompt shows |
lora_train |
330 GSM8K training problems with the model's own correct solution to each |
calibration, calibration_pool |
GSM8K training problems with their written solutions: the shared 128 and the pool of 500 |
runs |
19 systems on the 1,269 problems: each output's raw text, prompt and output token counts, and whether it finished |
runs_200, runs_dev |
the prompting systems on the 200-problem panel and on the development problems |
log_likelihood |
the summed log-likelihood and token count of each gold solution, under the base model and two int4 versions |
audit |
the IDs of ten outputs the lab reads closely |
Sources and licenses
- GSM8K (Cobbe et al. 2021, openai/grade-school-math): MIT license. Calculator annotations are stripped.
- Qwen2.5-0.5B-Instruct (Qwen team, Alibaba Cloud), revision 7ae557604adf67be50417f59c2c2f167def9a775: Apache 2.0. The model outputs and the adapter are derived from it.
How it was built
The problems are split with seed 6460. Every output was generated on an NVIDIA T4 during the course's pilot runs. The adapter has rank 16 and alpha 32 on every attention and MLP projection, and was trained for two epochs on the model's own correct solutions to the training problems, with seed 0.
Citations
@article{cobbe2021gsm8k,
title = {Training Verifiers to Solve Math Word Problems},
author = {Cobbe, Karl and Kosaraju, Vineet and Bavarian, Mohammad and Chen, Mark and Jun, Heewoo and
Kaiser, Lukasz and Plappert, Matthias and Tworek, Jerry and Hilton, Jacob and Nakano, Reiichiro and
Hesse, Christopher and Schulman, John},
journal = {arXiv preprint arXiv:2110.14168},
year = {2021}
}
@article{qwen2.5,
title = {Qwen2.5 Technical Report},
author = {{Qwen Team}},
journal = {arXiv preprint arXiv:2412.15115},
year = {2024}
}
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