---
title: esm2_t48_15B_UR50D
canonical_url: "https://www.modelscope.cn/models/facebook/esm2_t48_15B_UR50D"
md_url: "https://www.modelscope.cn/models/facebook/esm2_t48_15B_UR50D.md"
repository: facebook/esm2_t48_15B_UR50D
last_updated: 2025-05-20
license: mit
pipeline_tag: fill-mask
tasks:
  - fill-mask
model_type:
  - esm
architectures:
  - EsmForMaskedLM
library_name:
  - pytorch
frameworks:
  - pytorch
downloads: 484
stars: 0
---

# esm2_t48_15B_UR50D

> esm2_t48_15B_UR50D - facebook 在 ModelScope 开源的模型。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…

facebook/esm2_t48_15B_UR50D 是 ModelScope 魔搭社区上的fill-mask模型，采用 mit 许可。

- **Repository**: facebook/esm2_t48_15B_UR50D
- **License**: mit
- **Tasks**: fill-mask
- **Downloads**: 484
- **Stars**: 0
- **Last updated**: 2025-05-20

Source: https://www.modelscope.cn/models/facebook/esm2_t48_15B_UR50D

---

## 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](https://www.biorxiv.org/content/10.1101/2022.07.20.500902v2). You may also be interested in some demo notebooks ([PyTorch](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling.ipynb), [TensorFlow](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/protein_language_modeling-tf.ipynb)) 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](https://huggingface.co/facebook/esm2_t48_15B_UR50D) | 48 | 15B     |
| [esm2_t36_3B_UR50D](https://huggingface.co/facebook/esm2_t36_3B_UR50D) | 36 | 3B      | 
| [esm2_t33_650M_UR50D](https://huggingface.co/facebook/esm2_t33_650M_UR50D) | 33 | 650M    | 
| [esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) | 30 | 150M    | 
| [esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) | 12 | 35M     | 
| [esm2_t6_8M_UR50D](https://huggingface.co/facebook/esm2_t6_8M_UR50D)  | 6  | 8M      |