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license: apache-2.0
tags:
- protein
- biology
- protein-language-model
- continual-learning
library_name: transformers
---
# CoPeP Continual Learning Checkpoints
This repository contains **90 checkpoints** from continual
learning experiments with the [AMPLIFY](https://huggingface.co/chandar-lab/AMPLIFY_120M)
protein language model (120M parameters).
## Loading a checkpoint
```python
from transformers import AutoModel
model = AutoModel.from_pretrained(
"chandar-lab/copep-checkpoints",
subfolder="replay/task_5",
trust_remote_code=True,
)
```
## Available checkpoints
| Method | Tasks |
|--------|-------|
| `continual` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `gradient_ascent` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `hare_tortoise` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `joint` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `match` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `random_labels` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `replay` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `shrink_perturb` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
| `single_year` | task_0, task_1, task_2, task_3, task_4, task_5, task_6, task_7, task_8, task_9 |
Each `task_N` subfolder contains a `config.json` and `model.safetensors`.
### Task mapping
- **task_0** : pre-2004 (base model)
- **task_1** – **task_9** : successive temporal splits of UniRef data
For methods that start from task_1 (continual, gradient_ascent, match,
random_labels, replay, shrink_perturb), `task_0` is the same checkpoint as
`single_year/task_0` (the base pre-trained model).
## Model architecture
- **Architecture:** Transformer encoder with RoPE + SwiGLU
- **Parameters:** ~120M
- **Config:** hidden_size=640, num_hidden_layers=24, num_attention_heads=10, intermediate_size=2560
- **Vocab size:** 32 (amino acid tokens + special tokens)
- **Max length:** 512 (training), 50000 (inference with RoPE extrapolation)
|