Mirror Models
Collection
This xaitalk collection contains all mirror models for demo purposes of xaitalk. β’ 23 items β’ Updated
This is a bit-identical mirror of the canonical artifact from Meta AI / FAIR.
The mirror exists only as a resilience fallback for the xaitalk library β the upstream remains authoritative. All credit and licensing for the model belong to the original authors.
| Field | Value |
|---|---|
| Original authors | Meta AI / FAIR |
| Upstream (authoritative) | https://huggingface.co/facebook/esm2_t6_8M_UR50D |
| Source repo | https://github.com/facebookresearch/esm |
| Paper | https://www.science.org/doi/10.1126/science.ade2574 (Lin et al. 2022) |
| License | mit (inherited from upstream β please respect upstream's terms) |
| Mirror file | pytorch_model.bin |
| SHA-256 | 9edcf393212f3a26684cd68ca8095ec43c2c341ee0fcc3ba7a4d3a47c5dc138f |
| Size | 31,406,877 bytes (30.0 MB) |
from xaitalk.hub import ensure_model
weights_path = ensure_model("esm2-t6-8m-pt")
# Tries the canonical upstream first; falls back to this xaitalk mirror
# automatically if upstream is unreachable.
xaitalk's research-grade reproducibility claim relies on every weight file
being recoverable years from now. We mirror artifacts β€ 2.5 GB under
xaitalk/*-mirror so the pipeline survives upstream URL changes, repo
renames, or deletions. Bit-level parity with the canonical is asserted in
CI via python -m xaitalk.hub verify-mirrors.
If you use this model, please cite the original paper (not the mirror):
https://www.science.org/doi/10.1126/science.ade2574 (Lin et al. 2022)