| --- |
| pretty_name: ESMC embeddings |
| tags: [protein, embeddings, esmc] |
| viewer: false |
| --- |
| # Synthyra/esmc_embeddings |
| |
| Embeddings of the public ESMC models, |
| computed under an embedding profile: a pinned container, pinned numerics, one fixed batch shape per protein and a pinned |
| SAE readout, so every row is a function of its protein alone and bitwise reproducible on the hardware class that made it |
| (Hopper: GH200, H100, H200). Each store is a FastPLMs feature store at `<model>/<kind>/`, the one canonical place for that |
| model and kind; the `profile` column names the profile it was computed under, and a store computed under a new profile |
| replaces it there; `backbone` is the precision the model's forward pass ran in under that profile. `catalog.json` lists |
| them all. Each store holds every protein of one revision of its sequence list: a |
| `pooled` store every row, a `token` store |
| the rows flagged `full`, which need per-token rows. The `sequences` column names that revision (and the flag), and the |
| store's `coverage.json` records the exact check that passed it: every protein's rows present, and a sample of proteins |
| recomputed in new batches, equal bit for bit. |
| |
| | store | model | kind | proteins | GiB | streams | profile | backbone | sequences | |
| |---|---|---|---:|---:|---|---|---|---| |
| | `esmc_300/pooled` | ESMC-300 | pooled | 3,567,544 | 59.3 | `final_mean_var`, `layer_mean_var`, `structural_mean_var` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) | |
| | `esmc_300/token` | ESMC-300 | token | 330,937 | 589.1 | `final_hidden`, `layer_hidden`, `structural` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) `full` | |
| | `esmc_300_random/pooled` | ESMC-300 random init | pooled | 3,567,544 | 26.1 | `final_mean_var` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) | |
| | `esmc_300_random/token` | ESMC-300 random init | token | 330,937 | 519.8 | `final_hidden`, `layer_hidden` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) `full` | |
| | `esmc_600/pooled` | ESMC-600 | pooled | 3,567,544 | 69.5 | `final_mean_var`, `layer_mean_var`, `structural_mean_var` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) | |
| | `esmc_600/token` | ESMC-600 | token | 330,937 | 693.0 | `final_hidden`, `layer_hidden`, `structural` | `embedding_profile_v3` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) `full` | |
| | `esmc_6b/pooled` | ESMC-6B | pooled | 3,567,544 | 144.4 | `final_mean_var`, `layer_mean_var`, `structural_mean_var` | `embedding_profile_v2` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) | |
| | `esmc_6b/token` | ESMC-6B | token | 330,937 | 1,455.2 | `final_hidden`, `layer_hidden`, `structural` | `embedding_profile_v2` | BF16 | [`3effea8f`](https://huggingface.co/datasets/Synthyra/canonical_embedding_sequences/tree/3effea8f57f33eb97626d5d838f5aac273f5475e) `full` | |
|
|
| `esmc_300_random` is a control, not a protein model: the esmc_small architecture with random weights, drawn once from seed 20261001 and saved at `esmc_300_random/weights/model.safetensors` (SHA-256 `a7e5427b460aafbc3d99cb830a90a9d6aa37414154ffea4f48474cb714dca316`), which every key of its stores names. |
| |
| | stream | per protein | |
| |---|---| |
| | `layer_hidden` | (l + 2, d) bfloat16: the hidden state at the SAE's layer | |
| | `final_hidden` | (l + 2, d) bfloat16: the last layer, after the final norm | |
| | `structural` | (l + 2, 256): the structure fusion, a learned mixture of every layer projected to 256 | |
| | `sae_codes` | (l + 2, 64) indices and values: each token's top 64 SAE codes, stored sparse | |
| | `sae_max` | sparse (c,) float32, c the SAE codebook: each code's maximum over the tokens | |
| | `final_mean_var`, `layer_mean_var`, `structural_mean_var` | (2w,) float32: the mean then the variance over the l + 2 tokens | |
|
|
| A row is keyed by the SHA-256 of the uppercased, whitespace-stripped sequence. Per-token streams hold l + 2 rows per protein |
| (row 0 CLS, rows 1..l residues, row l + 1 EOS, cropped at 2046 residues); pooled streams hold one vector. |
|
|
| Each store carries `index/rows.parquet` (per protein: the segment, part and row of each stream, sorted by key) and |
| `index/parts.parquet` (per file: path, rows, bytes, SHA-256). A few proteins are read by HTTP range requests, so only their |
| bytes are downloaded however large the store is; a whole store is a folder download: |
|
|
| ```python |
| from foundry.embedding.hub import read_rows |
| |
| rows = read_rows("Synthyra/esmc_embeddings", "esmc_600/pooled", ["MKTAYIAKQR"], revision="<commit>", |
| streams=["sae_max", "final_mean_var"]) |
| vector = rows["MKTAYIAKQR"]["final_mean_var"] # a (2d,) float32 tensor |
| |
| from huggingface_hub import snapshot_download |
| snapshot_download("Synthyra/esmc_embeddings", repo_type="dataset", allow_patterns=["esmc_600/pooled/**"]) |
| ``` |
|
|
| Without the workspace: filter `index/rows.parquet` on `sha256`; each part is a safetensors file, whose first 8 bytes give |
| its JSON header's length and whose header gives each tensor's byte span. A dense stream's row r is row r of `values`; a |
| per-token or sparse stream's row r spans `offsets[r]:offsets[r + 1]` (`indptr` for `sae_max`) of `values` (and `indices`). |
|
|
