---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- model
paper: https://arxiv.org/abs/2502.09135
---

Vanilla Sparse AutoEncoder trained on embeddings from layer 3 of esm2_t6_8M_UR50D.

For more details check the [arxiv preprint](https://arxiv.org/abs/2502.09135) and 
the [github repository](https://github.com/edithvillegas/plm-sae).

**To use:**

Download the class defining the sparse autoencoder from github.
```bash
git clone git@github.com:edithvillegas/plm-sae.git
cd plm-sae
```

Load the base ESM2 model and the sparse autoencoder from huggingface.

```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
from sae.SAE_methods import AutoEncoder #import sparse autoencoder from local definition

#load ESM2 model
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t6_8M_UR50D")
model = AutoModelForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
model = model.to("cuda")

#load SAE (GPU-only)
sparse_autoencoder = AutoEncoder.from_pretrained("evillegasgarcia/sae_esm2_6_l3")
```

Prepare auxiliary functions to extract embeddings from a specific point in the ESM2 model.

```python
#setup to extract ESM2 embeddings
layer_name = "esm.encoder.layer.3.output"
#define hook
intermediate_embs = dict()
def hook(module, input, output):
    intermediate_embs[layer_name] = output
return hook
#attach hook
hook_handle = model.esm.encoder.layer[3].output.register_forward_hook(l3_hook)
```

Extract embeddings from the ESM2 model and then from the sparse autoencoder.

```python
#Inference
sequence = "MALWMRLLPLLALLALWGPDPAAAFVNQHLCGSHLVEALYLVCGERGFFYTPKTRREAEDLQVGQVELGGGPGAGSLQPL"

#PLM Inference
tokenized = tokenizer.encode(sequence, return_tensors="pt")
tokenized = tokenized.to("cuda")
outputs = model(tokenized)
embeddings = intermediate_embs[layer_name][0]

#SAE Inference
_, _, sae_embeddings, _, _ = sparse_autoencoder(embeddings)
```