AMPLIFY 120M
FLAIR Lab · Website · GitHub · Paper
A 120M-parameter protein language model pre-trained on UR100P using masked language modeling. Trained for 1M steps (~2T tokens) with context length 2,048.
This model was trained using the AMPLIFY training codebase. The original models and code were released under chandar-lab/AMPLIFY. See also flair-bio/AMPLIFY_350M.
| Property | Value |
|---|---|
| Architecture | BERT-style encoder (RoPE, SwiGLU, RMSNorm) |
| Parameters | 120M |
| Training tokens | ~2T |
| Vocabulary size | 32 (amino acid alphabet + special tokens) |
| Context length | 2,048 |
| Training steps | 1,000,000 |
| License | Apache 2.0 |
Quick Start
from transformers import AutoModelForMaskedLM, AutoTokenizer
model = AutoModelForMaskedLM.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
model.eval()
How to Use
Extract Embeddings
import torch
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
model = AutoModel.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
sequences = ["MKTAYIAK", "MVLSPADKTNVK"]
inputs = tokenizer(sequences, return_tensors="pt", padding=True, truncation=True, max_length=2048)
with torch.no_grad():
outputs = model(**inputs)
embeddings = outputs.last_hidden_state # [batch, seq_len, 640]
Masked Language Modeling
from transformers import AutoTokenizer, AutoModelForMaskedLM
import torch
tokenizer = AutoTokenizer.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
model = AutoModelForMaskedLM.from_pretrained("flair-bio/amplify-120m", trust_remote_code=True)
sequence = "MKTAY<mask>AKQRQISFVK"
inputs = tokenizer(sequence, return_tensors="pt")
with torch.no_grad():
logits = model(**inputs).logits
mask_idx = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
predicted = tokenizer.decode(logits[0, mask_idx].argmax(dim=-1))
print(predicted)
Model Description
Architecture
AMPLIFY 120M is a BERT-style transformer encoder with 24 layers, 640-dimensional hidden states, and 10 attention heads. It uses rotary positional embeddings (RoPE), SwiGLU feed-forward blocks, and RMSNorm. Tokenization is at the amino acid level with a vocabulary of 32 tokens.
| Config | Layers | Hidden dim | Heads | FFN dim | Context |
|---|---|---|---|---|---|
| AMPLIFY 120M | 24 | 640 | 10 | 1,712 | 512 → 2,048 |
Intended Use
This model is intended for extracting per-residue or per-sequence representations for downstream tasks, zero-shot variant effect prediction via pseudo-log-likelihood scoring, and fine-tuning on protein fitness, stability, binding, or functional annotation tasks.
Training
Data
Pre-trained on UR100P (chandar-lab/UR100P), a deduplicated union of UniRef100, OAS, and SCOPe.
Training Procedure
| Hyperparameter | Value |
|---|---|
| Hardware | 8× H100 80GB |
| Optimizer | AdamW |
| Learning rate | 1e-3 (peak) |
| LR schedule | Linear warmup + cosine decay |
| Batch size (tokens) | ~2M per step |
| Masking rate | 15% |
| Training objective | Masked language modeling |
| Precision | BF16 |
| Framework | PyTorch + HuggingFace Transformers |
Training logs are available on Weights & Biases.
Citation
If you use this model in your work, please cite:
@article{Fournier2024.09.23.614603,
title = {Protein Language Models: Is Scaling Necessary?},
author = {Fournier, Quentin and Vernon, Robert M. and van der Sloot, Almer and Schulz, Benjamin and Chandar, Sarath and Langmead, Christopher James},
year = {2024},
journal = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
doi = {10.1101/2024.09.23.614603},
url = {https://www.biorxiv.org/content/early/2024/09/23/2024.09.23.614603}
}