Datasets:

happynood
/

quantcall-results

Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 11 new columns ({'dataset_sha256', 'seed', 'git_commit', 'config_sha256', 'tsa', 'svr', 'abstention', 'ac', 'timestamp', 'fcr', 'sample_size'}) and 12 missing columns ({'fcr_ci_low', 'fcr_mean', 'tsa_mean', 'fcr_ci_high', 'ac_mean', 'n_seeds', 'svr_mean', 'delta_fcr_rel', 'baseline_quant', 'abstention_mean', 'delta_ac_rel', 'eta'}).

This happened while the csv dataset builder was generating data using

hf://datasets/happynood/quantcall-results/data/runs.csv (at revision a2c3368c5693580552ea20f7958003c01013ed48), ['hf://datasets/happynood/quantcall-results@a2c3368c5693580552ea20f7958003c01013ed48/data/leaderboard.csv', 'hf://datasets/happynood/quantcall-results@a2c3368c5693580552ea20f7958003c01013ed48/data/runs.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              model: string
              quant: string
              backend: string
              decoding: string
              tier: string
              seed: int64
              sample_size: int64
              svr: double
              tsa: double
              ac: double
              abstention: double
              fcr: double
              vram_gb: double
              git_commit: string
              config_sha256: string
              dataset_sha256: string
              timestamp: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2214
              to
              {'model': Value('string'), 'quant': Value('string'), 'backend': Value('string'), 'decoding': Value('string'), 'tier': Value('string'), 'n_seeds': Value('int64'), 'fcr_mean': Value('float64'), 'fcr_ci_low': Value('float64'), 'fcr_ci_high': Value('float64'), 'svr_mean': Value('float64'), 'tsa_mean': Value('float64'), 'ac_mean': Value('float64'), 'abstention_mean': Value('float64'), 'vram_gb': Value('float64'), 'eta': Value('float64'), 'delta_fcr_rel': Value('float64'), 'delta_ac_rel': Value('float64'), 'baseline_quant': Value('string')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 11 new columns ({'dataset_sha256', 'seed', 'git_commit', 'config_sha256', 'tsa', 'svr', 'abstention', 'ac', 'timestamp', 'fcr', 'sample_size'}) and 12 missing columns ({'fcr_ci_low', 'fcr_mean', 'tsa_mean', 'fcr_ci_high', 'ac_mean', 'n_seeds', 'svr_mean', 'delta_fcr_rel', 'baseline_quant', 'abstention_mean', 'delta_ac_rel', 'eta'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/happynood/quantcall-results/data/runs.csv (at revision a2c3368c5693580552ea20f7958003c01013ed48), ['hf://datasets/happynood/quantcall-results@a2c3368c5693580552ea20f7958003c01013ed48/data/leaderboard.csv', 'hf://datasets/happynood/quantcall-results@a2c3368c5693580552ea20f7958003c01013ed48/data/runs.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

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.

model string	quant string	backend string	decoding string	tier string	n_seeds int64	fcr_mean float64	fcr_ci_low float64	fcr_ci_high float64	svr_mean float64	tsa_mean float64	ac_mean float64	abstention_mean float64	vram_gb float64	eta float64	delta_fcr_rel float64	delta_ac_rel float64	baseline_quant string
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T1+T6	3	0.300899	0.276592	0.326621	0.326667	0.64	0.187853	0.049074	2.84082	0.10592	null	null	fp16
Llama-3.2-1B-Instruct	Q8_0	llama-cpp	free	T1+T6	3	0.283601	0.266309	0.302032	0.305	0.636667	0.176223	0.016515	1.768555	0.160358	0.057486	0.061912	fp16
Llama-3.2-1B-Instruct	Q5_K_M	llama-cpp	free	T1+T6	3	0.29067	0.277739	0.315105	0.313333	0.621667	0.188715	0.038964	1.387695	0.209462	0.033994	-0.004589	fp16
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T1+T6	3	0.283203	0.258195	0.305244	0.28	0.663333	0.173521	0.015958	1.291341	0.219309	0.058809	0.076296	fp16
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T2+T3	3	0.55409	0.54521	0.558846	0.571667	0.515	0.129694	1	2.861654	0.193626	null	null	fp16
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T2+T3	3	0.535611	0.523819	0.547372	0.338333	0.715	0.089111	1	1.312826	0.407984	0.03335	0.312911	fp16
Qwen3-0.6B	fp16	llama-cpp	constrained	T1+T6	1	0.822918	0.822918	0.822918	0.865	0.93	0.623656	0.873016	3.253906	0.252902	null	null	fp16
Qwen3-0.6B	Q8_0	llama-cpp	constrained	T1+T6	1	0.824262	0.824262	0.824262	0.865	0.93	0.629032	0.873016	1.454102	0.566853	-0.001633	-0.008621	fp16
Qwen3-0.6B	Q5_K_M	llama-cpp	constrained	T1+T6	1	0.809254	0.809254	0.809254	0.86	0.94	0.595745	0.84127	2.646484	0.305784	0.016605	0.044754	fp16
Qwen3-0.6B	Q4_K_M	llama-cpp	constrained	T1+T6	1	0.786971	0.786971	0.786971	0.86	0.915	0.579235	0.793651	2.507813	0.313808	0.043682	0.071227	fp16
Qwen3-0.6B	fp16	llama-cpp	free	T1+T6	3	0.822449	0.797418	0.847012	0.876667	0.93	0.605444	0.877687	2.147786	0.382929	null	null	fp16
Qwen3-0.6B	Q8_0	llama-cpp	free	T1+T6	3	0.8258	0.803507	0.84963	0.878333	0.931667	0.609664	0.883535	1.454102	0.567911	-0.004074	-0.00697	fp16
Qwen3-0.6B	Q5_K_M	llama-cpp	free	T1+T6	3	0.819553	0.797217	0.852189	0.878333	0.935	0.609471	0.855409	1.273763	0.643411	0.003521	-0.006651	fp16
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T1+T6	3	0.79763	0.778961	0.826957	0.873333	0.93	0.575002	0.812183	1.228841	0.649091	0.030178	0.05028	fp16
Qwen3-0.6B	fp16	llama-cpp	free	T2+T3	3	0.764602	0.756786	0.771758	0.686667	0.898333	0.473407	1	1.983398	0.385501	null	null	fp16
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T2+T3	3	0.753743	0.74387	0.764085	0.691667	0.891667	0.431641	1	1.237305	0.609182	0.014201	0.088225	fp16
Qwen3-0.6B	bf16	transformers	free	T1+T6	3	0.82332	0.796967	0.848321	0.876667	0.935	0.603925	0.877687	3.195964	0.257612	null	null	bf16
Qwen3-1.7B	Q8_0	llama-cpp	constrained	T1+T6	1	0.846607	0.846607	0.846607	0.87	0.945	0.68254	0.888889	2.567383	0.329755	null	null	Q8_0
Qwen3-1.7B	Q5_K_M	llama-cpp	constrained	T1+T6	1	0.833829	0.833829	0.833829	0.86	0.93	0.688172	0.857143	2.032227	0.410303	0.015094	-0.008252	Q8_0
Qwen3-1.7B	Q4_K_M	llama-cpp	constrained	T1+T6	1	0.841641	0.841641	0.841641	0.87	0.93	0.693548	0.873016	1.893555	0.444477	0.005866	-0.016129	Q8_0
Qwen3-1.7B	Q8_0	llama-cpp	free	T1+T6	3	0.841553	0.804722	0.873328	0.88	0.933333	0.681123	0.871754	2.567383	0.327786	null	null	Q8_0
Qwen3-1.7B	Q5_K_M	llama-cpp	free	T1+T6	3	0.842979	0.820976	0.874134	0.88	0.93	0.689522	0.872396	2.031576	0.414939	-0.001696	-0.012331	Q8_0
Qwen3-1.7B	Q4_K_M	llama-cpp	free	T1+T6	3	0.843509	0.813501	0.875384	0.883333	0.926667	0.686348	0.877687	1.892904	0.445616	-0.002325	-0.007672	Q8_0
gemma-4-e4b	Q4_0	openai	free	T1+T6	3	0.838347	0.827264	0.854293	0.878333	0.946667	0.639174	0.889212	null	null	null	null	Q4_0
gemma-4-e4b	Q4_0	openai	free	T1+T6	null	null	null	null	null	null	null	null	null	null	null	null	null
gemma-4-e4b	Q4_0	openai	free	T1+T6	null	null	null	null	null	null	null	null	null	null	null	null	null
gemma-4-e4b	Q4_0	openai	free	T1+T6	null	null	null	null	null	null	null	null	null	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.291992	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.291992	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.290039	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.307617	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.323242	null	null	null	null
Llama-3.2-1B-Instruct	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.307617	null	null	null	null
Llama-3.2-1B-Instruct	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.387695	null	null	null	null
Llama-3.2-1B-Instruct	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.387695	null	null	null	null
Llama-3.2-1B-Instruct	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.387695	null	null	null	null
Llama-3.2-1B-Instruct	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.768555	null	null	null	null
Llama-3.2-1B-Instruct	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.768555	null	null	null	null
Llama-3.2-1B-Instruct	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.768555	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.84082	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.84082	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.84082	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	2.856445	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	2.87207	null	null	null	null
Llama-3.2-1B-Instruct	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	2.856445	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	2.507813	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.229492	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.229492	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.227539	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.237305	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.237305	null	null	null	null
Qwen3-0.6B	Q4_K_M	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.237305	null	null	null	null
Qwen3-0.6B	Q5_K_M	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	2.646484	null	null	null	null
Qwen3-0.6B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.274414	null	null	null	null
Qwen3-0.6B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.274414	null	null	null	null
Qwen3-0.6B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.272461	null	null	null	null
Qwen3-0.6B	Q8_0	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	1.454102	null	null	null	null
Qwen3-0.6B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.454102	null	null	null	null
Qwen3-0.6B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.454102	null	null	null	null
Qwen3-0.6B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.454102	null	null	null	null
Qwen3-0.6B	bf16	transformers	free	T1+T6	null	null	null	null	null	null	null	null	3.195313	null	null	null	null
Qwen3-0.6B	bf16	transformers	free	T1+T6	null	null	null	null	null	null	null	null	3.197266	null	null	null	null
Qwen3-0.6B	bf16	transformers	free	T1+T6	null	null	null	null	null	null	null	null	3.195313	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	3.253906	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.975586	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.492188	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.975586	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.983398	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.983398	null	null	null	null
Qwen3-0.6B	fp16	llama-cpp	free	T2+T3	null	null	null	null	null	null	null	null	1.983398	null	null	null	null
Qwen3-1.7B	Q4_K_M	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	1.893555	null	null	null	null
Qwen3-1.7B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.893555	null	null	null	null
Qwen3-1.7B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.893555	null	null	null	null
Qwen3-1.7B	Q4_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	1.891602	null	null	null	null
Qwen3-1.7B	Q5_K_M	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	2.032227	null	null	null	null
Qwen3-1.7B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.032227	null	null	null	null
Qwen3-1.7B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.032227	null	null	null	null
Qwen3-1.7B	Q5_K_M	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.030273	null	null	null	null
Qwen3-1.7B	Q8_0	llama-cpp	constrained	T1+T6	null	null	null	null	null	null	null	null	2.567383	null	null	null	null
Qwen3-1.7B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.567383	null	null	null	null
Qwen3-1.7B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.567383	null	null	null	null
Qwen3-1.7B	Q8_0	llama-cpp	free	T1+T6	null	null	null	null	null	null	null	null	2.567383	null	null	null	null

QuantCall Results

Real benchmark results for the QuantCall benchmark, measuring how quantization degrades LLM function-calling reliability. Every row comes from an actual quantcall run execution — no fabricated or hand-edited numbers.

Files

File	Grain	Description
`data/runs.csv`	one row per real run (per seed)	Raw per-seed data with full manifest (git SHA, config/dataset hashes)
`data/leaderboard.csv`	one row per (model, quant, backend, decoding, tier)	Aggregated over seeds, with bootstrap 95% CIs and deltas vs an explicit baseline quant

Schema: `data/runs.csv`

Column	Type	Description
`model`	string	Model identifier (HF repo ID or local path)
`quant`	string	Quantization level: fp16, Q8_0, Q5_K_M, Q4_K_M, AWQ, GPTQ
`backend`	string	Inference backend: llama-cpp, transformers, vllm, openai
`decoding`	string	Decoding mode: free or constrained
`tier`	string	Dataset tier(s) evaluated, `+`-joined (e.g. `T1+T6`)
`seed`	int	Random seed for this run
`sample_size`	int	Number of instances evaluated per tier
`svr`	float	Schema-Validity Rate [0, 1]
`tsa`	float	Tool-Selection Accuracy [0, 1]
`ac`	float	Argument Correctness [0, 1]
`abstention`	float	Abstention Accuracy [0, 1]
`fcr`	float	Function-Calling Reliability — 0.25 × (SVR + TSA + AC + Abst)
`vram_gb`	float	Peak VRAM usage in GB for this run (empty if not measured)
`git_commit`	string	QuantCall repo commit SHA used for this run
`config_sha256`	string	SHA-256 of the run config
`dataset_sha256`	string	SHA-256 of the evaluation sample
`timestamp`	string	ISO-8601 UTC timestamp of the run

Schema: `data/leaderboard.csv`

Column	Type	Description
`model`	string	Model identifier
`quant`	string	Quantization level
`backend`	string	Inference backend
`decoding`	string	Decoding mode
`tier`	string	Dataset tier(s), `+`-joined
`n_seeds`	int	Number of seeds aggregated into this row
`fcr_mean`	float	Mean FCR across seeds
`fcr_ci_low`	float	Bootstrap 95% CI lower bound for FCR
`fcr_ci_high`	float	Bootstrap 95% CI upper bound for FCR
`svr_mean`	float	Mean SVR across seeds
`tsa_mean`	float	Mean TSA across seeds
`ac_mean`	float	Mean AC across seeds
`abstention_mean`	float	Mean Abstention across seeds
`vram_gb`	float	Mean peak VRAM in GB (empty if not measured)
`eta`	float	Efficiency: fcr_mean / vram_gb (empty if vram_gb is empty)
`delta_fcr_rel`	float	Relative FCR delta vs `baseline_quant` in the same scope; empty for the baseline row itself
`delta_ac_rel`	float	Relative AC delta vs `baseline_quant`
`baseline_quant`	string	The Δ reference quant for this scope — fp16 if it fits and was run, otherwise the best-available quant, always labeled explicitly here

These two schemas are generated by quantcall leaderboard <results_dir> (source of truth: src/quantcall/report/published.py, docs/RESULTS_SCHEMA.md in the repo) — this card is kept in sync with that code by a repo test (test_no_schema_drift).

How to Submit

Run the benchmark on your hardware following docs/RUN_REAL.md.
Verify your result.json contains a manifest block with git SHA and hashes.
Open a PR on GitHub adding your result file under results/.
Run quantcall leaderboard results/ --output-dir leaderboard/ and include the regenerated runs.csv / leaderboard.csv in your PR.

Datasets:

happynood
/

quantcall-results

QuantCall Results

Files

Schema: `data/runs.csv`

Schema: `data/leaderboard.csv`

How to Submit

Links

Space using happynood/quantcall-results 1

QuantCall Results

Files

Schema: data/runs.csv

Schema: data/leaderboard.csv

How to Submit

Links

Space using happynood/quantcall-results 1

Schema: `data/runs.csv`

Schema: `data/leaderboard.csv`