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
_class_name: string
_diffusers_version: string
text_encoder: list<item: string>
child 0, item: string
tokenizer: list<item: string>
child 0, item: string
video_vae: list<item: string>
child 0, item: string
audio_vae: list<item: string>
child 0, item: string
scheduler: null
transformer: list<item: string>
child 0, item: string
processor: list<item: string>
child 0, item: string
_minimax_h3: struct<schema_version: int64, partition: string, tasks: list<item: string>, task_aliases: struct<>, (... 57 chars omitted)
child 0, schema_version: int64
child 1, partition: string
child 2, tasks: list<item: string>
child 0, item: string
child 3, task_aliases: struct<>
child 4, sigma_shift_scales: struct<video: double, audio: double>
child 0, video: double
child 1, audio: double
metadata: struct<kwargs: struct<attn_proj: bool, decoder_dim: int64, decoder_rates: list<item: int64>, decoder (... 136 chars omitted)
child 0, kwargs: struct<attn_proj: bool, decoder_dim: int64, decoder_rates: list<item: int64>, decoder_type: string, (... 120 chars omitted)
child 0, attn_proj: bool
child 1, decoder_dim: int64
child 2, decoder_rates: list<item: int64>
child 0, item: int64
child 3, decoder_type: string
child 4, encoder_dim: int64
child 5, encoder_rates: list<item: int64>
child 0, item: int64
child 6, latent_dim: int64
child 7, sample_rate: int64
child 8, vae_latent_channels: int64
to
{'metadata': {'kwargs': {'attn_proj': Value('bool'), 'decoder_dim': Value('int64'), 'decoder_rates': List(Value('int64')), 'decoder_type': Value('string'), 'encoder_dim': Value('int64'), 'encoder_rates': List(Value('int64')), 'latent_dim': Value('int64'), 'sample_rate': Value('int64'), 'vae_latent_channels': Value('int64')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
_class_name: string
_diffusers_version: string
text_encoder: list<item: string>
child 0, item: string
tokenizer: list<item: string>
child 0, item: string
video_vae: list<item: string>
child 0, item: string
audio_vae: list<item: string>
child 0, item: string
scheduler: null
transformer: list<item: string>
child 0, item: string
processor: list<item: string>
child 0, item: string
_minimax_h3: struct<schema_version: int64, partition: string, tasks: list<item: string>, task_aliases: struct<>, (... 57 chars omitted)
child 0, schema_version: int64
child 1, partition: string
child 2, tasks: list<item: string>
child 0, item: string
child 3, task_aliases: struct<>
child 4, sigma_shift_scales: struct<video: double, audio: double>
child 0, video: double
child 1, audio: double
metadata: struct<kwargs: struct<attn_proj: bool, decoder_dim: int64, decoder_rates: list<item: int64>, decoder (... 136 chars omitted)
child 0, kwargs: struct<attn_proj: bool, decoder_dim: int64, decoder_rates: list<item: int64>, decoder_type: string, (... 120 chars omitted)
child 0, attn_proj: bool
child 1, decoder_dim: int64
child 2, decoder_rates: list<item: int64>
child 0, item: int64
child 3, decoder_type: string
child 4, encoder_dim: int64
child 5, encoder_rates: list<item: int64>
child 0, item: int64
child 6, latent_dim: int64
child 7, sample_rate: int64
child 8, vae_latent_channels: int64
to
{'metadata': {'kwargs': {'attn_proj': Value('bool'), 'decoder_dim': Value('int64'), 'decoder_rates': List(Value('int64')), 'decoder_type': Value('string'), 'encoder_dim': Value('int64'), 'encoder_rates': List(Value('int64')), 'latent_dim': Value('int64'), 'sample_rate': Value('int64'), 'vae_latent_channels': 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.
Checkpoints
MiniMax-H3
Mirror of MiniMaxAI/MiniMax-H3, in its native per-partition layout. Each variant folder is self-contained (transformer + text encoder + video/audio VAE + tokenizer + processor), so download only the one you need.
| Folder | Upstream partition | Tasks | Size |
|---|---|---|---|
MiniMax-H3/TI2V/ |
FL2VA |
T2VA (text-to-video+audio), FL2VA / I2VA (first/last-frame image-to-video+audio) | ~144 GB |
MiniMax-H3/Ref2V/ |
Ref2VA |
Ref2VA (reference-to-video+audio) | ~144 GB |
Layout of each variant:
transformer/ MiniMaxH3DiTModel, 13 safetensors shards
text_encoder/ Qwen3-VL encoder, 14 safetensors shards
video_vae/ video VAE code + source/model.safetensors
audio_vae/ audio VAE code + model.safetensors
tokenizer/ processor/ model_index.json LICENSE
Download one variant
# T2V / I2V
hf download mingyang-wu/Checkpoints --repo-type dataset \
--include "MiniMax-H3/TI2V/*" --local-dir ./ckpts
# Ref2V
hf download mingyang-wu/Checkpoints --repo-type dataset \
--include "MiniMax-H3/Ref2V/*" --local-dir ./ckpts
Only the transformer weights (skip the shared text encoder / VAE you already have):
hf download mingyang-wu/Checkpoints --repo-type dataset \
--include "MiniMax-H3/Ref2V/transformer/*" --local-dir ./ckpts
Python:
from huggingface_hub import snapshot_download
path = snapshot_download("mingyang-wu/Checkpoints", repo_type="dataset",
allow_patterns="MiniMax-H3/Ref2V/*", local_dir="./ckpts")
# -> ./ckpts/MiniMax-H3/Ref2V
The weights are distributed under the MiniMax-H3 Community License (see LICENSE inside each variant folder).
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