| --- |
| pretty_name: AI4Sci FoldBench frozen data |
| tags: |
| - protein-folding |
| - foldbench |
| - ai4science |
| - int8 |
| size_categories: |
| - 100K<n<1M |
| --- |
| |
| # AI4Sci FoldBench data |
|
|
| This repository hosts the **actual large data payloads**, not a copy of the |
| Harbor task. Task code, instructions, contract, reference implementations, |
| evaluation code and the download/extraction script live in |
| [T0-RSI/ai4sci-tasks](https://github.com/T0-RSI/ai4sci-tasks/tree/main/foldbench). |
|
|
| Release: `foldbench-100k-esmc-int8-v1`. The complete release is identified by |
| the immutable Hub commit pinned in the GitHub task's `download-source.json`; |
| do not treat an intermediate upload commit as a complete release. |
|
|
| ## Contents |
|
|
| | Restored directory | Files | Bytes | Contents | |
| | --- | ---: | ---: | --- | |
| | `manifests/` | 3 | 291,989,362 | Frozen 100K logical training records and supporting manifests | |
| | `objects/` | 90,000 | 7,994,107,456 | Deduplicated training structure NPZ files | |
| | `features/` | 91,366 | 61,910,892,072 | Raw final-layer ESMC-6B features, groupwise INT8 | |
| | `foldbench/` | 4,623 | 2,116,164,115 | Complete native evaluation suite, ground truth and 1,568 feature files | |
| | `models/` | 3 | 755,429,835 | ESMFold2-Fast reference weights, config and upstream model card | |
| | **Total** | **185,995** | **73,068,582,840** | **73.1 GB decimal / 68.0 GiB** | |
|
|
| The evaluation suite contains 1,522 requests and 1,823 target rows across |
| nine tracks. This is the existing frozen task data, repackaged without |
| changing file contents. The feature identity is |
| `esmc6b-final-ln-int8-g64-45b0fa5d-ptsdpa-v1`: INT8 values `[L,2560]` and |
| BF16 scales `[L,40]`, not an ESMFold2-specific learned projection. |
|
|
| There are **no ESMC-6B model weights**, FP16/BF16 feature masters, MSA payloads, |
| experiment logs, credentials, or Harbor source files in this dataset. |
| ESMFold2-Fast weights are **reference-only**: the task forbids submitting |
| them or using them to initialize the trainable folding model. Features are |
| fixed inputs, not trainable submitted model parameters. See the GitHub |
| task contract for the complete research and evaluation rules. |
|
|
| ## Download and restore |
|
|
| The payload is stored as 75 uncompressed, byte-preserving tar shards, with |
| approximately 1 GB of data per shard. `release-manifest.json` records each |
| archive's SHA-256, size and file count. `payload-files.jsonl` records the |
| SHA-256 and size of every restored file. The GitHub task pins both the Hub |
| commit and the archive-manifest checksum. |
|
|
| Use `foldbench/environment/data/materialize.sh DESTINATION` from the |
| GitHub task (with Python 3.11+ and the `hf` CLI installed). It downloads |
| individual pinned shards, verifies archive and payload hashes, restores |
| the original directory layout, and checks the scientific release metadata. |
| Successfully extracted downloaded shards are removed by default; source |
| data are never removed. `--keep-archives` retains the additional archive |
| copy. `--verify-only` rechecks an already materialized release offline. |
|
|
| ## Provenance and rights |
|
|
| This is an AI4Sci task data bundle, not an official upstream release. |
| The original FoldBench benchmark, structure data and reference weights |
| retain their respective upstream attribution and terms. In particular, |
| the ESMFold2-Fast upstream model card and its third-party-notice link are |
| included unchanged alongside the reference weights. No blanket relicensing |
| of these third-party assets is claimed here. Feature files retain their |
| source model revision and sequence provenance in their safetensors metadata. |
|
|
