The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<128000: int64, 128040: int64>
to
{'32013': Value('int64'), '32021': Value('int64')}
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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<128000: int64, 128040: int64>
to
{'32013': Value('int64'), '32021': Value('int64')}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.
Deterministic Random Models
This dataset contains ten small, deterministic language-model fixtures for model-format, loader, inference, compatibility, and conformance testing. They are not trained models and must not be used for language-model quality evaluation.
All weights are synthetic and deterministically generated. No original model checkpoint weights are included.
Cases
| Case | Architecture | Parameters | Hugging Face | GGUF | Notable feature |
|---|---|---|---|---|---|
tinyllama-chat |
Llama | 303,744 | F32 | Q4_K_M | GQA, query/KV ratio 8 |
smollm2-instruct |
Llama | 46,320 | F32 | Q4_K_M | GQA, query/KV ratio 3 |
mobilellama-chat |
Llama | 9,296 | F32 | Q4_K_M | MHA |
minicpm5 |
Llama | 1,409,664 | F32 | Q4_K_M | explicit head dimension, multiple EOS IDs |
deepseek-coder |
Llama | 9,296 | F32 | Q4_K_M | linear RoPE scaling |
hermes3-llama31 |
Llama | 86,336 | F32 | Q4_K_M | Llama 3 RoPE scaling |
livekit-turn-detector |
Llama | 132,336 | F32 | Q4_K_M | explicit head dimension, GQA |
gemma4-random-model |
Gemma 4 | 1,519,168 | BF16 | Q4_0 | five-local/one-global attention schedule |
qwen3-random-model |
Qwen 3 | 508,800 | BF16 | Q4_0 | wide Q projection and Q/K head norms |
smollm3-random-model |
SmolLM3 | 4,917,504 | BF16 | Q4_0 | three-RoPE/one-no-RoPE layer schedule |
The seven Llama cases are derived from real Hugging Face configuration files by
a preservation-first shrinker. Gemma 4, Qwen 3, and SmolLM3 retain
architecture-specific reduced geometries that preserve important
ratios, tensor inventories, and layer schedules observed in locally downloaded
upstream GGUF models. Published case names use random-model rather than
tiny-model to avoid collision with a separately maintained TinyModel collection.
Formats and layout
The Llama cases retain the original dataset layout:
<llama-case>/
|-- package/model.safetensors # canonical F32 weights
|-- gguf/model-Q4_K_M.gguf
|-- tokenizer/
|-- reference/outputs.safetensors
|-- inputs.safetensors
|-- case.json
|-- provenance.json
|-- source-config.json
|-- shrunk-config.json
|-- config-diff.json
`-- validation.json
The architecture-specific cases use:
<random-model-case>/
|-- hf-bf16/
| |-- config.json
| |-- model.safetensors
| |-- tokenizer.json
| `-- tokenizer_config.json
|-- gguf-q4_0/
| |-- <case>-Q4_0.gguf
| `-- quantize.log
|-- reference/
| |-- inputs.json
| |-- hf-outputs.safetensors
| `-- gguf-native.json
|-- CONFIG_DECISION.md
`-- metadata.json
manifest.json is the machine-readable index of all ten model packages and
their SHA-256 hashes and sizes.
Synthetic weights and tokenizers
Weights use the tlfloat::LCG64 recurrence with multiplier
6364136223846793005, increment 1442695040888963407, and ten warm-up steps.
Each case records its seed and provenance.
The reduced models use deterministic 128-token auxiliary vocabularies. These
tokenizers cover token IDs 0..127 and preserve each case's special-token
semantics, but they do not reproduce the linguistic behavior of the original
tokenizer. Explicit token IDs are the primary numerical-test interface.
GGUF generation and validation
GGUF files were generated with upstream ggml-org/llama.cpp commit
40b740ad05c531b9d57aca6698c3ed553a9e784c.
Every retained GGUF was loaded through that revision and exercised with direct token IDs for prefill, cached decode, logit extraction, finite-value checks, and repeated-execution checks. The effective EOG token set was checked against the model EOS semantics. Per-case metadata records the actual tensor-type histogram, hashes, commands, and informational comparison with the corresponding Transformers reference.
Q4_K_M and Q4_0 are lossy formats. Their logits are not required to equal the F32 or BF16 reference exactly.
Reproducibility and scope
The Hugging Face weights, configs, and GGUF outputs for Gemma 4, Qwen 3, and SmolLM3 were independently regenerated and found byte-identical. The Llama cases retain their source revisions, source-config hashes, shrink decisions, and generation provenance in each case directory.
This dataset is not a pretrained-model collection, a model-quality benchmark, or a reproduction of upstream weights or tokenizers. Source-derived configuration and metadata files may remain subject to terms of their respective upstream repositories; consult their recorded provenance before redistribution.
See REPORT.md, GGUF_Q4_K_M_REPORT.json, and
ARCHITECTURE_RANDOM_MODELS_REPORT.json for collection-level summaries.
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