Datasets:

SitongGong
/

GST_EGOSCHEMA

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
additional_special_tokens: list<item: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>>
  child 0, item: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
      child 0, content: string
      child 1, lstrip: bool
      child 2, normalized: bool
      child 3, rstrip: bool
      child 4, single_word: bool
bos_token: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
  child 0, content: string
  child 1, lstrip: bool
  child 2, normalized: bool
  child 3, rstrip: bool
  child 4, single_word: bool
eos_token: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
  child 0, content: string
  child 1, lstrip: bool
  child 2, normalized: bool
  child 3, rstrip: bool
  child 4, single_word: bool
pad_token: string
megatron_config: null
init_lora_weights: bool
bias: string
fan_in_fan_out: bool
lora_alpha: int64
base_model_name_or_path: string
loftq_config: struct<>
revision: null
megatron_core: string
target_modules: string
rank_pattern: struct<>
peft_type: string
alpha_pattern: struct<>
modules_to_save: list<item: string>
  child 0, item: string
use_dora: bool
layers_to_transform: null
r: int64
layers_pattern: null
use_rslora: bool
lora_dropout: double
inference_mode: bool
task_type: string
auto_mapping: null
layer_replication: null
to
{'alpha_pattern': {}, 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_dropout': Value('float64'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': List(Value('string')), 'peft_type': Value('string'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': Value('string'), 'task_type': Value('string'), 'use_dora': Value('bool'), 'use_rslora': Value('bool')}
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
              additional_special_tokens: list<item: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>>
                child 0, item: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
                    child 0, content: string
                    child 1, lstrip: bool
                    child 2, normalized: bool
                    child 3, rstrip: bool
                    child 4, single_word: bool
              bos_token: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
                child 0, content: string
                child 1, lstrip: bool
                child 2, normalized: bool
                child 3, rstrip: bool
                child 4, single_word: bool
              eos_token: struct<content: string, lstrip: bool, normalized: bool, rstrip: bool, single_word: bool>
                child 0, content: string
                child 1, lstrip: bool
                child 2, normalized: bool
                child 3, rstrip: bool
                child 4, single_word: bool
              pad_token: string
              megatron_config: null
              init_lora_weights: bool
              bias: string
              fan_in_fan_out: bool
              lora_alpha: int64
              base_model_name_or_path: string
              loftq_config: struct<>
              revision: null
              megatron_core: string
              target_modules: string
              rank_pattern: struct<>
              peft_type: string
              alpha_pattern: struct<>
              modules_to_save: list<item: string>
                child 0, item: string
              use_dora: bool
              layers_to_transform: null
              r: int64
              layers_pattern: null
              use_rslora: bool
              lora_dropout: double
              inference_mode: bool
              task_type: string
              auto_mapping: null
              layer_replication: null
              to
              {'alpha_pattern': {}, 'auto_mapping': Value('null'), 'base_model_name_or_path': Value('string'), 'bias': Value('string'), 'fan_in_fan_out': Value('bool'), 'inference_mode': Value('bool'), 'init_lora_weights': Value('bool'), 'layer_replication': Value('null'), 'layers_pattern': Value('null'), 'layers_to_transform': Value('null'), 'loftq_config': {}, 'lora_alpha': Value('int64'), 'lora_dropout': Value('float64'), 'megatron_config': Value('null'), 'megatron_core': Value('string'), 'modules_to_save': List(Value('string')), 'peft_type': Value('string'), 'r': Value('int64'), 'rank_pattern': {}, 'revision': Value('null'), 'target_modules': Value('string'), 'task_type': Value('string'), 'use_dora': Value('bool'), 'use_rslora': Value('bool')}
              because column names don't match

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Model Card for Model ID

https://showlab.github.io/videollm-online/

Model Details

LLM: meta-llama/Meta-Llama-3-8B-Instruct
Vision Strategy:
- Frame Encoder: google/siglip-large-patch16-384
- Frame Tokens: CLS Token + Avg Pooled 3x3 Tokens
- Frame FPS: 2 for training, 2~10 for inference
- Frame Resolution: max resolution 384, with zero-padding to keep aspect ratio
- Video Length: 10 minutes
Training Data: Ego4D Narration Stream 113K + Ego4D GoalStep Stream 21K

Model Sources

Repository: https://github.com/showlab/videollm-online
Paper: https://arxiv.org/abs/2406.11816

Uses

First, clone the github repository and follow the installation instruction:

git clone https://github.com/showlab/videollm-online

Ensure you have Miniconda and Python version >= 3.10 installed, then run:

conda install -y pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia
pip install transformers accelerate deepspeed peft editdistance Levenshtein tensorboard gradio moviepy submitit
pip install flash-attn --no-build-isolation

PyTorch source will make ffmpeg installed, but it is an old version and usually make very low quality preprocessing. Please install newest ffmpeg following:

wget https://johnvansickle.com/ffmpeg/releases/ffmpeg-release-amd64-static.tar.xz
tar xvf ffmpeg-release-amd64-static.tar.xz
rm ffmpeg-release-amd64-static.tar.xz
mv ffmpeg-7.0.1-amd64-static ffmpeg

If you want to try our model with the audio in real-time streaming, please also clone ChatTTS.

pip install omegaconf vocos vector_quantize_pytorch cython
git clone git+https://github.com/2noise/ChatTTS
mv ChatTTS demo/rendering/

Launch the gradio demo locally with:

python -m demo.app --resume_from_checkpoint chenjoya/videollm-online-8b-v1plus

Or launch the CLI locally with:

python -m demo.cli --resume_from_checkpoint chenjoya/videollm-online-8b-v1plus

Citation

@inproceedings{videollm-online,
  author       = {Joya Chen and Zhaoyang Lv and Shiwei Wu and Kevin Qinghong Lin and Chenan Song and Difei Gao and Jia-Wei Liu and Ziteng Gao and Dongxing Mao and Mike Zheng Shou},
  title        = {VideoLLM-online: Online Video Large Language Model for Streaming Video},
  booktitle    = {CVPR},
  year         = {2024},
}

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Paper for SitongGong/GST_EGOSCHEMA

VideoLLM-online: Online Video Large Language Model for Streaming Video

Paper • 2406.11816 • Published Jun 17, 2024 • 26