PREpiBind — demo checkpoints (float16)

The same four models as daylight-00/prepibind, cast to float16. 106.5 MiB each instead of 213.0 MiB. This is what the notebooks and the Colab demo download, and what configs/predict/*.py load by default.

Use these to try the model. Use the float32 ones for anything you intend to report.

• Code: https://github.com/daylight-00/PREpiBind
• Colab: demo/run_colab.ipynb in that repository
• Backbone: daylight-00/esmc-300m-2024-12

The four files

Each is 106.5 MiB, a one-key {"model_state_dict": ...} dict of 64 float16 tensors, 55,820,161 parameters. Nothing is quantised or pruned: this is tensor.half() of the float32 release, produced by demo/build_demo_assets.py checkpoint, which is a pure function of the training checkpoint and can be re-derived at any time.

All four are the val_loss argmin of their arm.

How these four were chosen

Each arm was trained as 15 runs — three seeds by five cross-validation folds — and every number in the paper is a mean over those 15. A released checkpoint is one of the fifteen, the one with the lowest validation loss in its arm, the value in the table. No test-set quantity took part. So a single checkpoint does not reproduce a paper number, and this float16 copy reproduces one less exactly still; the research card has the detail.

This does not reproduce the research path bit for bit

Two reasons, both accepted rather than fixed: the head and ESMC both run in float16, against a float32 head and bfloat16 ESMC on the research path; and the demo encodes epitopes with ESMC at run time instead of reading the precomputed store training and evaluation used, which makes bit-exactness impossible at any precision. On Colab's free T4 flash-attn is off as well, and the runtime says so.

Measured, on 2,000 demo rows, this float16 path against the as-trained path:

Ranking is essentially preserved. If a decision turns on the third decimal, use the float32 checkpoints and the as-trained precision.

Input format

Two columns. The MHC field is the two chain names joined by an underscore, and both must be keys in the mapping table the config points at. The datasets and the demo write beta first, then alpha; the order does not change the score, the head having no positional encoding, but it keeps inputs comparable with the published ones.

MHC,Epitope
HLA-DRB1*01:01_HLA-DRA*01:01,PKYVKQNTLKLATA
HLA-DPB1*03:01_HLA-DPA1*01:03,AAAARYPNVTIAAAA
H2-IAbB_H2-IAbA,AAAAPAAAATTAAPA

Output is the input with Logits and Score (sigmoid) appended. Every training epitope is a 15-mer; other lengths run but are outside the training distribution.

The demo ships its own HLA store, demo/data/emb_hla_esmc_small_demo_fp16.h5 (16 MB, float16, 116 alleles, cut to the peptide-binding window) - every allele the four dataset arms use, which is 98 human HLA class II chains and 18 mouse H2 chains. That file is in the GitHub repository, not here, and is a verified derivation of the full-length research store emb_hla_esmc_small_0430.h5: PREPIBIND_EMB_ROOT=<its directory> python demo/build_demo_assets.py hla-store --check.

Usage

git clone https://github.com/daylight-00/PREpiBind && cd PREpiBind
pip install --extra-index-url https://download.pytorch.org/whl/cu126 -e ".[demo]"

hf download daylight-00/prepibind-demo      prepibind_qualitative_s100_f0_fp16.pt --local-dir models
hf download daylight-00/esmc-300m-2024-12   esmc_300m_2024_12_v0_fp16.pth         --local-dir models

python -m prepibind.inference configs/predict/config_demo.py --plot

Predictions land in outputs/prediction.csv; --plot also writes a KDE of the scores. The config already points at models/prepibind_qualitative_s100_f0_fp16.pt; switch models by switching config, or override any single key:

python -m prepibind.inference configs/predict/config_ms.py --test_path my_input.csv --out_path out

The same thing from Python, which is what the notebooks do:

from prepibind.inference import load_config, main

cfg = load_config("configs/predict/config_demo.py", test_path="my_input.csv", out_path="outputs")
df = main(cfg)

The ESMC encoder is vendored into prepibind/esmc/, so the demo needs only torch and huggingface_hub, both already present on Colab. Do not pip install esm.

No GPU is required. Without CUDA the runtime falls back to float32 throughout and turns off flash-attn; on a pre-Ampere card it turns off flash-attn and keeps float16. Either way it prints what it changed, because a silently downgraded run is one whose numbers cannot be compared with anything.

Limitations

Everything on the research card applies — one run rather than the reported mean, class II only, the disclosed H2 chain correction in the embedding store, binding rather than immunogenicity — plus the precision deviation measured above.

Licence

MIT for these weights, same as the repository. The ESMC 300M backbone is not ours but is also MIT: it moved to biohub/esmc-300m-2024-12 (ungated, tagged mit + other), and the vendored encoder source is copied from esm 3.4.0, whose licence is plain MIT, "Copyright 2026 Chan Zuckerberg Biohub, Inc.". Checked 2026-09-10; see THIRD_PARTY_NOTICES.md in the repository.

Citation

@article{jang2026prepibind,
  title   = {PREpiBind: Protein Representation-integrated Epitope-MHC Class II Binding Prediction},
  author  = {Jang, David Hyunyoo and Kim, Dongwoo and Hwang, Untaek and Park, Byungho and Choi, Yoonjoo and Lee, Juyong},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.09.15.751749},
  url     = {https://www.biorxiv.org/content/10.64898/2026.09.15.751749v1}
}