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Datasets:
meshllm
/
moe-rankings

Tasks:
Text Generation
Languages:
English
Tags:
Mixture of Experts
gguf
routing
metadata
mesh-llm
License:
Dataset card Files
xet
 Community
5
Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
all_files: list<item: string>
  child 0, item: string
all_layers: bool
analysis_tool: string
analyzer_id: string
command: struct<analyzer_id: string, context_size: int64, token_count: int64>
  child 0, analyzer_id: string
  child 1, context_size: int64
  child 2, token_count: int64
created_at: timestamp[s]
distribution_id: string
file_hashes: struct<Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf: string>
  child 0, Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf: string
format: string
primary_file: string
prompt_count: null
prompt_set: null
ranking_path: string
schema_version: int64
source_repo: string
source_revision: string
status: string
token_count: int64
ranking: struct<mass_checkpoints: list<item: struct<mass_fraction: double, top_n: int64>>, rows: int64, sha25 (... 10 chars omitted)
  child 0, mass_checkpoints: list<item: struct<mass_fraction: double, top_n: int64>>
      child 0, item: struct<mass_fraction: double, top_n: int64>
          child 0, mass_fraction: double
          child 1, top_n: int64
  child 1, rows: int64
  child 2, sha256: string
model: struct<expert_count: int64, expert_used_count: int64>
  child 0, expert_count: int64
  child 1, expert_used_count: int64
memory: struct<base_resident_bytes: int64, expert_bytes: struct<bytes_per_expert: int64, kind: string>, expe (... 88 chars omitted)
  child 0, base_resident_bytes: int64
  child 1, expert_bytes: struct<bytes_per_expert: int64, kind: string>
      child 0, bytes_per_expert: int64
      child 1, kind: string
  child 2, expert_tensor_bytes_total: int64
  child 3, full_model_bytes: int64
  child 4, shard_file_overhead_bytes: int64
to
{'memory': {'base_resident_bytes': Value('int64'), 'expert_bytes': {'bytes_per_expert': Value('int64'), 'kind': Value('string')}, 'expert_tensor_bytes_total': Value('int64'), 'full_model_bytes': Value('int64'), 'shard_file_overhead_bytes': Value('int64')}, 'model': {'expert_count': Value('int64'), 'expert_used_count': Value('int64')}, 'ranking': {'mass_checkpoints': List({'mass_fraction': Value('float64'), 'top_n': Value('int64')}), 'rows': Value('int64'), 'sha256': Value('string')}, 'schema_version': Value('int64')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                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 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, 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 295, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              all_files: list<item: string>
                child 0, item: string
              all_layers: bool
              analysis_tool: string
              analyzer_id: string
              command: struct<analyzer_id: string, context_size: int64, token_count: int64>
                child 0, analyzer_id: string
                child 1, context_size: int64
                child 2, token_count: int64
              created_at: timestamp[s]
              distribution_id: string
              file_hashes: struct<Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf: string>
                child 0, Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf: string
              format: string
              primary_file: string
              prompt_count: null
              prompt_set: null
              ranking_path: string
              schema_version: int64
              source_repo: string
              source_revision: string
              status: string
              token_count: int64
              ranking: struct<mass_checkpoints: list<item: struct<mass_fraction: double, top_n: int64>>, rows: int64, sha25 (... 10 chars omitted)
                child 0, mass_checkpoints: list<item: struct<mass_fraction: double, top_n: int64>>
                    child 0, item: struct<mass_fraction: double, top_n: int64>
                        child 0, mass_fraction: double
                        child 1, top_n: int64
                child 1, rows: int64
                child 2, sha256: string
              model: struct<expert_count: int64, expert_used_count: int64>
                child 0, expert_count: int64
                child 1, expert_used_count: int64
              memory: struct<base_resident_bytes: int64, expert_bytes: struct<bytes_per_expert: int64, kind: string>, expe (... 88 chars omitted)
                child 0, base_resident_bytes: int64
                child 1, expert_bytes: struct<bytes_per_expert: int64, kind: string>
                    child 0, bytes_per_expert: int64
                    child 1, kind: string
                child 2, expert_tensor_bytes_total: int64
                child 3, full_model_bytes: int64
                child 4, shard_file_overhead_bytes: int64
              to
              {'memory': {'base_resident_bytes': Value('int64'), 'expert_bytes': {'bytes_per_expert': Value('int64'), 'kind': Value('string')}, 'expert_tensor_bytes_total': Value('int64'), 'full_model_bytes': Value('int64'), 'shard_file_overhead_bytes': Value('int64')}, 'model': {'expert_count': Value('int64'), 'expert_used_count': Value('int64')}, 'ranking': {'mass_checkpoints': List({'mass_fraction': Value('float64'), 'top_n': Value('int64')}), 'rows': Value('int64'), 'sha256': Value('string')}, 'schema_version': Value('int64')}
              because column names don't match

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MoE Rankings

meshllm/moe-rankings is a public dataset of derived Mixture-of-Experts routing metadata for published model artifacts.

The dataset stores ranking artifacts produced by llama-moe-analyze so tools such as mesh-llm can discover expert-hotness rankings for exact model revisions without recomputing them locally.

Purpose

This dataset exists to provide:

  • immutable MoE expert rankings keyed by exact source model revision
  • a canonical archive of published ranking artifacts
  • reusable metadata for routing, sharding, and MoE placement experiments

The dataset is not a model mirror and does not store original model weights.

Identity Model

Each artifact is identified by:

  • source_repo
  • source_revision
  • format
  • distribution_id
  • analyzer_id

For GGUF models, distribution_id is the normalized model distribution name, usually the GGUF filename stem with any shard suffix removed.

Layout

Artifacts are stored under:

data/<source_namespace>/<source_repo_name>/<source_revision>/<format>/<distribution_id>/<analyzer_id>/

Each artifact directory contains:

  • metadata.json
  • ranking.csv
  • run.log

Example:

data/Flexan/kshitijthakkar-qwen3.5-moe-0.87B-d0.8B-GGUF/a9b8adbec2cc87479c772dac1944f313b4036c26/gguf/qwen3.5-moe-0.87B-d0.8B.Q2_K/micro-v1/

Artifact Semantics

ranking.csv

Normalized expert ranking output with columns:

expert_id,total_mass,mass_fraction,selection_count

Sorted by hottest experts first.

metadata.json

Validation and provenance metadata, including:

  • exact source repo and commit
  • analyzed distribution id
  • file list and hashes
  • analyzer id
  • prompt set id
  • token count
  • local analyzer source details

run.log

Raw execution log for debugging and auditing.

Analyzer Policy

Current canonical analyzer:

  • micro-v1

micro-v1 is tied to a fixed built-in prompt set and should be comparable across runs. Any meaningful change to prompts or semantics should produce a new analyzer id such as micro-v2.

Immutability

Artifacts in this dataset are intended to be immutable.

  • A new source model commit uses a new source_revision path.
  • A new analysis method or incompatible prompt set uses a new analyzer_id.
  • Existing published artifacts should not be overwritten with different content.

Intended Consumers

  • mesh-llm
  • MoE sharding and routing tools
  • benchmarking and evaluation pipelines
  • researchers comparing expert distributions across quantizations and revisions

Notes

  • This dataset stores derived metadata, not original model weights.
  • Some logs may be verbose because they preserve upstream tool output for reproducibility.
  • Model-repo colocated sidecars may exist separately, but this dataset is the canonical system of record.
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