README.md

---
license: mit
library_name: transformers
tags:
  - protein-language-model
  - esm2
  - lora
  - masked-language-modeling
  - beta-lactamase
  - TEM-52
base_model: facebook/esm2_t33_650M_UR50D
---

# TEM-52 ESM-2 (LoRA) Masked Language Models

Fine-tuned [ESM-2 650M](https://huggingface.co/facebook/esm2_t33_650M_UR50D)
masked language models (with LoRA adapters) for predicting beneficial
amino-acid substitutions at six saturation-mutagenesis (SSM) positions
(Y103, N168, V214, A235, E237, R241; non-Ambler) of the TEM-52 β-lactamase.

One checkpoint is provided per antibiotic substrate, each in its own subfolder:

| Subfolder | Substrate |
| --- | --- |
| `amp_100` | ampicillin |
| `caz_10000` | ceftazidime |
| `cet_15` | cephalothin |
| `ctx_1125` | cefotaxime |

Each subfolder contains `pytorch_model.bin` (full model state dict: ESM-2 650M +
LoRA) and the tokenizer files.

## Usage

Clone the code repository [`ajoujcb/TEM_ESM`](https://github.com/ajoujcb/TEM_ESM)
and use `predict.py`, which downloads the weights from this Hub repo automatically:

```bash
python predict.py --hf_repo AjouJCB/TEM_ESM --substrate caz_10000 -p 103 --top_k 5
```

Or from Python:

```python
from predict import (load_model, resolve_weights_dir,
                     build_masked_sequence, predict_masked_tokens)

weights_dir = resolve_weights_dir(hf_repo="AjouJCB/TEM_ESM", substrate="caz_10000")
model, tokenizer = load_model(weights_dir, device="cuda")
sequence = build_masked_sequence(103)          # wild-type TEM-52 with Y103 masked
for hit in predict_masked_tokens(model, tokenizer, sequence, top_k=5):
    print(hit)
```

The architecture (LoRA config) used to rebuild the model before loading the
state dict is: `r=4`, `lora_alpha=8`, `target_modules=["query","key","value","out"]`,
applied to all 33 transformer blocks. See the code repository for details.

> Note: ESM-2 uses rotary position embeddings, so the unused
> `position_embeddings.weight` is absent from these checkpoints; load with
> `strict=False` (handled automatically by `predict.py`).