Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'list' object", 'Conversion failed for column fal-ai/ultrashape with type object')
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 151, in _generate_tables
                  pa_table = paj.read_json(
                             ^^^^^^^^^^^^^^
                File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 503, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 350, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 181, in _generate_tables
                  pa_table = pa.Table.from_pandas(df, preserve_index=False)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 4795, in pyarrow.lib.Table.from_pandas
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 637, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                            ^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 625, in convert_column
                  raise e
                File "/usr/local/lib/python3.12/site-packages/pyarrow/pandas_compat.py", line 619, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/array.pxi", line 365, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'list' object", 'Conversion failed for column fal-ai/ultrashape with type object')

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.

fal.ai Model Database

Complete catalog of 1,094 AI models from fal.ai with pricing, categories, and API documentation links.

Why This Dataset?

fal.ai hosts 1,094+ AI models across 25 categories, but there's no easy way to browse, compare, or search them programmatically. This dataset solves that.

Quick Stats

Metric Value
Total Models 1,094
Categories 25
Last Updated January 2025
Crawl Time ~5 seconds

Top Categories

Category Count
text-to-image 386
image-to-image 217
text-to-video 78
image-to-video 72
training 61
vision 55

Data Fields

{
  "model_id": "fal-ai/flux/dev",
  "title": "FLUX.1 [dev]",
  "category": "text-to-image",
  "description": "FLUX.1 [dev] is a 12 billion parameter flow transformer...",
  "api_docs": "https://fal.ai/models/fal-ai/flux/dev/api",
  "pricing": "$0.025 per megapixel",
  "tags": ["text-to-image", "flux"]
}

Usage

Python

import json
from datasets import load_dataset

# Load from Hugging Face
ds = load_dataset("PHY041/fal-ai-models")

# Or load JSON directly
with open("data/fal_models.json") as f:
    models = json.load(f)

# Search for video generation models
video_models = [m for m in models.values() if "video" in m.get("category", "")]

With the Crawler (Fresh Data)

# Clone the companion repo for the crawler
# https://github.com/PHY041/fal-ai-model-database

from fal_models import refresh_models, search

# Re-crawl fal.ai for latest models
refresh_models()

# Search
results = search("flux")

Companion Resources

How It Was Built

Instead of scraping HTML, we discovered fal.ai's internal API:

https://fal.ai/api/models?limit=500&categories={category}

Query all 25 categories = 1,094 unique models in ~5 seconds.

License

MIT - Use freely, attribution appreciated.

Citation

@misc{fal-ai-models-2025,
  author = {Haoyang Pang},
  title = {fal.ai Model Database},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/PHY041/fal-ai-models}
}
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