peft base_model:facebook/esm2_t33_650M_UR50D region:us
---
library_name: peft
base_model: facebook/esm2_t33_650M_UR50D
---

# Model Card for Model ID


This model builds upon [PepMLM](https://github.com/programmablebio/pepmlm/tree/main), aimed at generating peptides from receptor sequences. It incorporates the [ESM model](https://huggingface.co/docs/transformers/model_doc/esm) framework from HuggingFace for its core architecture. The key enhancement in this model is the adoption of LoRA for training, distinguishing it from its predecessor.



Usage:

```
from transformers import AutoTokenizer, AutoModelForMaskedLM
from peft import PeftConfig
import torch
model_name = "littleworth/esm2_t33_650M_UR50D_pepmlm_lora_adapter_merged"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name).to(device)
```


Packages version:

```
{'transformers': '4.36.0', 'peft': '0.9.0', 'torch': '2.0.0'}
```

Training summary:

```
{
  "train/loss": 1.5091,
  "train/grad_norm": 3.6427412033081055,
  "train/learning_rate": 6.773224309612687e-7,
  "train/epoch": 5,
  "train/global_step": 6395,
  "_timestamp": 1709229361.5373268,
  "_runtime": 25556.57973074913,
  "_step": 639,
  "train/train_runtime": 25557.6176,
  "train/train_samples_per_second": 4.003,
  "train/train_steps_per_second": 0.25,
  "train/total_flos": 220903283526564960,
  "train/train_loss": 1.8436848362317955,
  "_wandb": {
    "runtime": 25556
  }
}
```

