Datasets:
Add dataset card for MoE rankings
Browse files
README.md
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---
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pretty_name: MoE Rankings
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license: apache-2.0
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task_categories:
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- text-generation
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language:
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- en
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tags:
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- moe
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- gguf
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- routing
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- metadata
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- mesh-llm
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---
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# MoE Rankings
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`meshllm/moe-rankings` is a public dataset of derived Mixture-of-Experts routing metadata for published model artifacts.
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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.
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## Purpose
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This dataset exists to provide:
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- immutable MoE expert rankings keyed by exact source model revision
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- a canonical archive of published ranking artifacts
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- reusable metadata for routing, sharding, and MoE placement experiments
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The dataset is not a model mirror and does not store original model weights.
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## Identity Model
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Each artifact is identified by:
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- `source_repo`
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- `source_revision`
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- `format`
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- `distribution_id`
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- `analyzer_id`
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For GGUF models, `distribution_id` is the normalized model distribution name, usually the GGUF filename stem with any shard suffix removed.
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## Layout
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Artifacts are stored under:
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```text
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data/<source_namespace>/<source_repo_name>/<source_revision>/<format>/<distribution_id>/<analyzer_id>/
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```
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Each artifact directory contains:
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- `metadata.json`
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- `ranking.csv`
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- `run.log`
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Example:
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```text
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data/Flexan/kshitijthakkar-qwen3.5-moe-0.87B-d0.8B-GGUF/a9b8adbec2cc87479c772dac1944f313b4036c26/gguf/qwen3.5-moe-0.87B-d0.8B.Q2_K/micro-v1/
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```
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## Artifact Semantics
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### `ranking.csv`
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Normalized expert ranking output with columns:
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```text
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expert_id,total_mass,mass_fraction,selection_count
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```
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Sorted by hottest experts first.
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### `metadata.json`
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Validation and provenance metadata, including:
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- exact source repo and commit
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- analyzed distribution id
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- file list and hashes
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- analyzer id
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- prompt set id
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- token count
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- local analyzer source details
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### `run.log`
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Raw execution log for debugging and auditing.
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## Analyzer Policy
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Current canonical analyzer:
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- `micro-v1`
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`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`.
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## Immutability
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Artifacts in this dataset are intended to be immutable.
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- A new source model commit uses a new `source_revision` path.
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- A new analysis method or incompatible prompt set uses a new `analyzer_id`.
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- Existing published artifacts should not be overwritten with different content.
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## Intended Consumers
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- `mesh-llm`
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- MoE sharding and routing tools
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- benchmarking and evaluation pipelines
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- researchers comparing expert distributions across quantizations and revisions
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## Notes
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- This dataset stores derived metadata, not original model weights.
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- Some logs may be verbose because they preserve upstream tool output for reproducibility.
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- Model-repo colocated sidecars may exist separately, but this dataset is the canonical system of record.
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