andrewdalpino/ESMC-ProtHash-V1-960

ESMC ProtHash

A protein language model that outputs contextual embeddings that align in vector-space according to the protein's underlying biological properties such as structure and function. Distilled from the ESMC family of models and trained on the UniRef50 dataset of over 53 million unique protein sequences, ProtHash embeddings align with the embedding space of ESMC but at a greatly reduced computational cost.

Key Features

• Blazing fast and efficient: ProtHash uses significantly less computational resources than ESMC while matching up to 98% of the embedding space.

• Biologically-relevant: Biologically similar proteins will show up nearby in the embedding space enabling downstream tasks such as clustering, classification, and locality-sensitive hashing.

• Compatible with ESMC: ProtHash can output either ESMC or native embeddings - allowing it to serve as both a faster drop-in replacement for ESMC embeddings or a more compressed representation.

Pretrained Models

These model weights can be loaded using the prothash library using the from_pretrained() method. ONNX versions are also available.

Version 1

Name	Context Length	Embedding Dimensions	Native Dimensionality	Attention Heads	Encoder Layers	Total Params	Library Version
andrewdalpino/ESMC-ProtHash-V1-960	2048	960	512	8	12	43M	1.x

Note: V1 models are trained on the UniRef50 dataset to match the complete contextual embedding space.

Legacy Models

Name	Context Length	Embedding Dimensions	Attention Heads (Q/KV)	Encoder Layers	Total Params	Teacher Model	Teacher Dimensions	Library Version
andrewdalpino/ProtHash-V0-384-Tiny	2048	384	16/4	4	4.2M	esmc_300m	960	0.2.x
andrewdalpino/ProtHash-V0-384	2048	384	16/4	10	10M	esmc_300m	960	0.2.x
andrewdalpino/ProtHash-V0-512-Tiny	2048	512	16/4	4	7.4M	esmc_600m	1152	0.2.x
andrewdalpino/ProtHash-V0-512	2048	512	16/4	10	18M	esmc_600m	1152	0.2.x

Note: The V0 models were trained on the SwissProt dataset and only trained to match the embedding space of the classification token.

Example

First, you'll need the prothash and esm packages installed into your environment. For ProtHash version 1 use library version 1.x and for version 0 install library version 0.2.x. We recommend using a virtual environment such as Python's venv module to prevent version conflicts with other packages.

pip install prothash~=1.0.0 esm

Then, load the weights from HuggingFace Hub, tokenize a protein sequence, and pass it to the model. The out is an Embeddings object that contains the contextual embeddings from four different stages of the encoder. Stage 1 contains the earliest encoder embeddings and stage 4 contains the latest embeddings.

import torch

from esm.tokenization import EsmSequenceTokenizer

from prothash.model import ESMCProtHash

tokenizer = EsmSequenceTokenizer()

model_name = "andrewdalpino/ESMC-ProtHash-V1-960"

model = ProtHash.from_pretrained(model_name)

sequence = input("Enter a sequence: ")

out = tokenizer(sequence, max_length=2048)

tokens = out["input_ids"]

# Input is a [1, T] tensor of token indices. 
x = torch.tensor(tokens, dtype=torch.int64).unsqueeze(0)

# Output ESMC embeddings.
embeddings = model.embed(x)

# Output the sequence embeddings in native dimensionality.
embeddings = model.embed_native(x)

# You can access all 4 stages from the embeddings object.
print(embeddings.stage1)
print(embeddings.stage2)
print(embeddings.stage3)
print(embeddings.stage4)

References

• The UniProt Consortium, UniProt: the Universal Protein Knowledgebase in 2025, Nucleic Acids Research, 2025, 53, D609–D617.
• T. Hayes, et al. Simulating 500 million years of evolution with a language model, 2024.
• B. Zhang, et al. Root Mean Square Layer Normalization. 33rd Conference on Neural Information Processing Systems, NeurIPS 2019.
• T. Kim, et al. Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation, 2021.