Datasets:

aws-neuron
/

torch-neuronx-model-support

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
model_id: string
library: string
tp: int32
runner: string
result: string
notes: string
error: string
exclusion: string
wall_s: int32
hbm_peak_gb: double
hbm_capacity_gb: double
cpu_match: string
english: string
neuronx_cc: string
library_version: string
transformers: string
accelerate: string
torch_neuronx: string
torch: string
nki: string
python: string
neuron_driver: string
tested_at: timestamp[ms, tz=UTC]
run_py: string
to
{'model_id': Value('string'), 'library': Value('string'), 'tp': Value('int32'), 'runner': Value('string'), 'result': Value('string'), 'notes': Value('string'), 'error': Value('string'), 'wall_s': Value('int32'), 'hbm_peak_gb': Value('float64'), 'hbm_capacity_gb': Value('float64'), 'cpu_match': Value('string'), 'english': Value('string'), 'neuronx_cc': Value('string'), 'library_version': Value('string'), 'transformers': Value('string'), 'accelerate': Value('string'), 'torch_neuronx': Value('string'), 'torch': Value('string'), 'nki': Value('string'), 'python': Value('string'), 'neuron_driver': Value('string'), 'tested_at': Value('timestamp[ms, tz=UTC]'), 'run_py': Value('string')}
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 2951, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, 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 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, 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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
              model_id: string
              library: string
              tp: int32
              runner: string
              result: string
              notes: string
              error: string
              exclusion: string
              wall_s: int32
              hbm_peak_gb: double
              hbm_capacity_gb: double
              cpu_match: string
              english: string
              neuronx_cc: string
              library_version: string
              transformers: string
              accelerate: string
              torch_neuronx: string
              torch: string
              nki: string
              python: string
              neuron_driver: string
              tested_at: timestamp[ms, tz=UTC]
              run_py: string
              to
              {'model_id': Value('string'), 'library': Value('string'), 'tp': Value('int32'), 'runner': Value('string'), 'result': Value('string'), 'notes': Value('string'), 'error': Value('string'), 'wall_s': Value('int32'), 'hbm_peak_gb': Value('float64'), 'hbm_capacity_gb': Value('float64'), 'cpu_match': Value('string'), 'english': Value('string'), 'neuronx_cc': Value('string'), 'library_version': Value('string'), 'transformers': Value('string'), 'accelerate': Value('string'), 'torch_neuronx': Value('string'), 'torch': Value('string'), 'nki': Value('string'), 'python': Value('string'), 'neuron_driver': Value('string'), 'tested_at': Value('timestamp[ms, tz=UTC]'), 'run_py': Value('string')}
              because column names don't match

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.

torch-neuronx model support

Which Hugging Face Hub models run unmodified on AWS Trainium, through the native PyTorch backend torch-neuronx, for each neuronx-cc and library version tested. The only library tested so far is transformers.

To look up a model, use the Space.

What is tested

transformers

Each run loads a model on a trn2 instance and generates 32 tokens greedily from the prompt "The future of AI is", sent as a user message for chat models. The model is loaded with tp_plan="auto", or expert-parallel for mixture-of-experts models without a tensor-parallel plan, at the smallest power-of-two degree that fits its bf16 weights. Generation uses torch.compile(backend="neuron") with a static cache and chunked prefill. An encoder-decoder model runs its encoder once on the prompt, then generates from its decoder, at TP=1.

For now, only models that fit on 4 Trainium2 devices are tested: the degree is at most 16.

`result`	Meaning
`PASS`	The model loaded, `generate()` returned, and the output check passed or could not run.
`FAIL(load)`	`from_pretrained` raised.
`FAIL(gen)`	`generate()` raised.
`FAIL(output)`	`generate()` returned, but the output check failed.
`FAIL(other)`	The run crashed, or the model's extra Python packages could not be installed.
`EXCLUDED`	Not tested: the sweep's selection left the model out, for the reason in `notes`. A later run of the model replaces this row.

The output check compares a TP=1 run with a CPU run of the same checkpoint and prompt: the tokens must match, or differ first at a near-tie. At higher TP, or when the CPU run cannot complete, a small instruct model judges whether the text is fluent English. PASS says nothing about throughput or about longer generations.

The models are the top text-generation and image-text-to-text models on the Hub by downloads and trending, reselected before each sweep: the 100 best-ranked form the selection. Each sweep also tests the next 100 best-ranked of these tasks and of translation and summarization, encoder-decoder models included. Quantized checkpoints and models that need trust_remote_code are left out. A model that leaves the list keeps the results it already has.

Layout

There is one file per tested (neuronx-cc, library version) pair:

data/neuronxcc-<version>/<library>-<version>.parquet

Each sweep tests the current Neuron SDK with the library version of the day, so most pairs are never tested. A model missing from a pair was not tested there, which does not mean it fails. A model the sweep left out has an EXCLUDED row instead, with no tp or runner.

selection/<library>.csv lists the 100 best-ranked models of the latest sweep of a library, best-ranked first, with the downloads and the Hub ranks they were selected by.

Column	Content
`model_id`, `library`, `tp`, `runner`	The model, the library that loads it, its tensor- or expert-parallel degree, and the load class (`run_dense`: `AutoModelForCausalLM`, `run_multimodal`: `AutoModelForImageTextToText`, `run_seq2seq`: `AutoModelForSeq2SeqLM`, `run_dense_ep`: expert-parallel).
`result`, `notes`	The outcome, and the start of the generated text or of the error, or why the model was excluded.
`exclusion`	The category of the rule that excluded an `EXCLUDED` model, such as `quantized` or `trust-remote-code`.
`error`	The full exception message of a `FAIL(load)` or `FAIL(gen)` run.
`cpu_match`	TP=1 only: `Y` if the tokens match a CPU run, `~ near-tie …` if they differ only at a near-tie, `N …` if not.
`english`	The judge's verdict, `Y` or `N`, when the CPU comparison did not run.
`wall_s`, `hbm_peak_gb`, `hbm_capacity_gb`	Wall time of the run, including compilation, and peak device memory against what the TP degree provides.
`neuronx_cc`, `library_version`, `transformers`, `accelerate`, `torch_neuronx`, `torch`, `nki`, `python`, `neuron_driver`	The stack the run used. `transformers` is also set for the libraries built on it.
`tested_at`	When the run started (UTC).
`run_py`	The standalone script that ran.

Querying

import pandas as pd

df = pd.read_parquet("hf://datasets/aws-neuron/torch-neuronx-model-support/data")
df[df.model_id == "Qwen/Qwen3-0.6B"][["neuronx_cc", "library", "library_version", "tp", "result"]]

Reproducing a run

Each run_py is a standalone script. Its header lists the Neuron stack it ran on and a pip install line for the packages it adds on top. On a Trainium2 instance with that stack, for example the AWS Neuron Deep Learning Container, install those packages, then:

python3 run.py

On a trn2 instance, the run can skip compilation by first copying the compiled graphs of its compiler, from the public NEFF cache:

hf buckets sync hf://buckets/aws-neuron/torch-neuronx-neff-cache/neuronxcc-<neuronx_cc> /tmp/neff_cache

The script launches its own torch.distributed ranks for TP > 1.

Downloads last month: -