Datasets:

ChengyiX
/

fable-native-mlx-migration

Exception:    SplitsNotFoundError
Message:      The split names could not be parsed from the dataset config.
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 322, in _generate_tables
                  examples = [ujson_loads(line) for line in batch.splitlines()]
                              ~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
                  return pd.io.json.ujson_loads(*args, **kwargs)
                         ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
              ValueError: Expected object or value
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
                  for split_generator in builder._split_generators(
                                         ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 101, in _split_generators
                  pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
                             ~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 329, in _generate_tables
                  raise e
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 294, in _generate_tables
                  pa_table.validate()
                  ~~~~~~~~~~~~~~~~~^^
                File "pyarrow/table.pxi", line 4237, in pyarrow.lib.Table.validate
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Column 8: In chunk 0: Invalid: List child array invalid: Invalid: Struct child array #15 invalid: Invalid: Length spanned by list offsets (7) larger than values array (length 2)
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
                  for split in get_dataset_split_names(
                               ~~~~~~~~~~~~~~~~~~~~~~~^
                      path=dataset,
                      ^^^^^^^^^^^^^
                      config_name=config,
                      ^^^^^^^^^^^^^^^^^^^
                      token=hf_token,
                      ^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
                  info = get_dataset_config_info(
                      path,
                  ...<6 lines>...
                      **config_kwargs,
                  )
                File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
                  raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
              datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.

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From GGUF to Native MLX

A reproducible migration of the TURBO-Fable 27B checkpoint to native MLX on an Apple M4 Max, the speculative-decoding measurements that bounded it, and the serving-runtime memory policy that blocked real agent traffic.

Chengyi Xu and Nexora — Nexora Research, October 2026.

Paper: paper/main.pdf (7 pages)
Source: paper/main.tex
Evidence: results/processed/evidence.json
Protocol: protocol.md

What this report establishes

A bounded, verifiable conversion. A mixed-precision MLX artifact was produced from the pinned BF16 parent without materializing the checkpoint: 128-row chunks, 3.08 GB peak resident memory, 493 s conversion body time. All 686 non-quantized tensors matched the pinned sanitizer element by element; sampled rows of six real modules matched official quantization byte for byte; all 2,180 tensor names and shapes matched.
Speculation helps, but not enough, and not everywhere. On one 8,159-token prompt, DFlash2 reached 8.40 tok/s against 5.34 plain (1.57× decode, ~13% on the whole request) in the best of four interleaved conditions. A newer kernel generation and a wider prefill both erased the gain. Stock MLX-VLM MTP gave 6.05 against 5.08. No condition reached 20 tok/s.
Both real agent sessions timed out. MTP depth 3 and DFlash2 each ran ~900 s with 25,778-token initial input, made 4 and 6 tool calls, left the injected defect unfixed, and failed the independent gate and full suite.
The blocking failure was policy, not the model. A serving-runtime system-memory guard refused an ~25k-token prefill because macOS held 16.7 GiB compressed and free pages sat under a 1.7 GiB floor. Disabling that documented guard — leaving the 36 GiB engine budget intact — turned the identical 25,676-token request into a completed reply in 359.85 s, with no prompt, tool or budget reduction.
Twelve protocol checks pass. OpenAI Chat Completions, OpenAI Responses and Anthropic Messages each answered on both the native backend and the Magpie gateway, streaming and non-streaming, with exact markers and terminal stream events.

What this report does not claim

No peer review. This is a deployment engineering report.
No ranked intelligence. We did not measure reasoning or coding accuracy beyond one injected-defect task, which failed.
No 20 tok/s floor. No configuration reached it.
No near-limit context qualification. 262,144 is allocation metadata here; the largest recorded actual request is 32,014 tokens.
No equivalence claim between the MLX quantization and the BF16 parent, or between the MLX artifact and the GGUF release. Layout and sampling were verified; task accuracy was not.
No endorsement of uncensored/Heretic use. Those are provenance labels; no refusal-removal or harmful-generation evaluation was performed.

Reproducing the evidence

python3 code/export_evidence.py results/processed/evidence.json
python3 code/test_export_evidence.py
cd paper && SOURCE_DATE_EPOCH=1761696000 tectonic --untrusted main.tex

SOURCE_DATE_EPOCH is required for a byte-identical rebuild: without it the PDF embeds the build timestamp and its SHA-256 changes on every run. With it the PDF is reproducible, which is what makes SHA256SUMS meaningful.

The exporter is whitelist-only. It reads finite scalars and enumerated flags, rejects any string carrying a private path or credential marker, and refuses to write an artifact whose serialized form contains one. Prompts, tool output, repository source, agent transcripts and absolute local paths are excluded by construction, and the test suite proves that a poisoned input is refused.

Artifact contents

paper/main.tex        LaTeX source
paper/main.pdf        built paper, 7 pages
paper/nexora-paper.sty
code/export_evidence.py       whitelist-only exporter
code/test_export_evidence.py  contract tests
results/processed/evidence.json
protocol.md
SHA256SUMS

Licence and citation

Code and text: see LICENSE. The model weights are governed by their upstream licences and are not redistributed here. Citation metadata: CITATION.cff.

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