esm2 t33 650M UR50D

Providerfacebook
Categoryfill-mask
Licensemit
Downloads979
Stars1

Overview

ESM-2 t33 650M is a transformer-based protein language model trained on evolutionary sequence data. Unlike general LLMs, this model treats amino acid sequences as a language, enabling developers to extract high-dimensional embeddings that capture structural and functional properties of proteins without requiring explicit 3D coordinates. It is particularly effective for predicting the effects of mutations, identifying conserved functional sites, and accelerating protein engineering workflows. For developers, it integrates easily into PyTorch pipelines via Hugging Face, offering a computationally efficient balance between parameter count and predictive accuracy compared to larger ESM variants.

Highlights

  • High-performance protein sequence embeddings for downstream ML tasks
  • Efficient 650M parameter architecture for faster inference
  • Predicts structural properties from primary amino acid sequences
  • Seamless integration with PyTorch and Hugging Face Transformers
  • Permissive MIT license for commercial and research use

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("facebook/esm2_t33_650M_UR50D")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download facebook/esm2_t33_650M_UR50D

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download facebook/esm2_t33_650M_UR50D config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('facebook/esm2_t33_650M_UR50D')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/facebook/esm2_t33_650M_UR50D

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/facebook/esm2_t33_650M_UR50D

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('facebook/esm2_t33_650M_UR50D')
tokenizer = AutoTokenizer.from_pretrained('facebook/esm2_t33_650M_UR50D')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model facebook/esm2_t33_650M_UR50D

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model facebook/esm2_t33_650M_UR50D README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('facebook/esm2_t33_650M_UR50D')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/facebook/esm2_t33_650M_UR50D.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/facebook/esm2_t33_650M_UR50D.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'facebook/esm2_t33_650M_UR50D')

Full Documentation

来源: HuggingFace

---
license: mit
widget:
- text: "MQIFVKTLTGKTITLEVEPS<mask>TIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"
---

ESM-2

ESM-2 is a state-of-the-art protein model trained on a masked language modelling objective. It is suitable for fine-tuning on a wide range of tasks that take protein sequences as input. For detailed information on the model architecture and training data, please refer to the accompanying paper. You may also be interested in some demo notebooks (PyTorch, TensorFlow) which demonstrate how to fine-tune ESM-2 models on your tasks of interest.

Several ESM-2 checkpoints are available in the Hub with varying sizes. Larger sizes generally have somewhat better accuracy, but require much more memory and time to train:

| Checkpoint name | Num layers | Num parameters |
|------------------------------|----|----------|
| esm2_t48_15B_UR50D | 48 | 15B |
| esm2_t36_3B_UR50D | 36 | 3B |
| esm2_t33_650M_UR50D | 33 | 650M |
| esm2_t30_150M_UR50D | 30 | 150M |
| esm2_t12_35M_UR50D | 12 | 35M |
| esm2_t6_8M_UR50D | 6 | 8M |

View official source
Join our Telegram