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AI4PD
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ProtGPT3-1.3B is a single-sequence autoregressive protein language model for protein sequence generation. It is part of the ProtGPT3 family, an open-source suite of promptable and aligned protein language models ranging from 112M to 10B parameters. ProtGPT3 models use a causal Mixtral-style Mixture-of-Experts architecture and are trained for causal language modeling on protein sequences.
The single-sequence ProtGPT3 models can generate proteins in either N-to-C or C-to-N direction using special directional tokens. The model is intended for unconditional or prefix-conditioned protein sequence generation and can be used as a base model for downstream protein design workflows.
Also consider using the ProtGPT3-1.3B -dpo version for an equivalent model size, but with improved sequence generation.
We provide some examples below.
Install dependencies:
bash
pip install transformers accelerate torch
Load the model and tokenizer:
python
import torchfrom transformers import AutoTokenizer, AutoModelForCausalLMmodel_id = "AI4PD/ProtGPT3-1.3B" # Replace with the final checkpoint name# Load tokenizer for generationtokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True,add_bos_token=True, add_eos_token=False, padding_side="left")model = AutoModelForCausalLM.from_pretrained(model_id,torch_dtype=torch.bfloat16,device_map="auto",trust_remote_code=True,)model.eval()
python
import torchprompt = "" # Optionally provide an amino-acid prefix or model-specific directioninputs = tokenizer(prompt, return_tensors="pt").to(model.device)with torch.no_grad():output_ids = model.generate(inputs["input_ids"],max_new_tokens=512,do_sample=True,temperature=0.8,top_p=0.9,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.pad_token_id,num_return_sequences=20, # set to desired number of protein sequences to be generated in parallel)sequence = tokenizer.decode(output_ids[0], skip_special_tokens=True)print(sequence) # output includes directional token "1" or "2" to denote if sequence was generated N-to-C or C-to-N
python
import torch# forward N-to-C generation with special token "1"prefix = "1MKT" # use special token "2" instead of "1" for reverse C-to-N generationinputs = tokenizer(prefix, return_tensors="pt").to(model.device)with torch.no_grad():output_ids = model.generate(inputs["input_ids"],max_new_tokens=256,do_sample=True,temperature=0.8,top_p=0.9,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.eos_token_id,num_return_sequences=20, # set to desired number of protein sequences to be generated in parallel)sequence = tokenizer.decode(output_ids[0], skip_special_tokens=True)print(sequence)
python
import torchprompts = ["","1MKT", # N-to-C generation"2MAV", # C-to-N generation]inputs = tokenizer(prompts,return_tensors="pt",padding=True,).to(model.device)with torch.no_grad():output_ids = model.generate(inputs["input_ids"],max_new_tokens=256,do_sample=True,temperature=0.8,top_p=0.9,eos_token_id=tokenizer.eos_token_id,pad_token_id=tokenizer.bos_token_id,num_return_sequences=20, # set to desired number of batch sequences to be generated in parallel)sequences = tokenizer.batch_decode(output_ids, skip_special_tokens=True)for sequence in sequences:print(sequence)
The model may be fine-tuned or incorporated into protein design workflows, including family-specific generation, protein variant generation, and computational screening pipelines.
The model should not be used as the sole basis for experimental, clinical, environmental, or safety-critical decisions. Generated proteins require downstream computational and experimental validation. The model is not guaranteed to generate functional, soluble, safe, or synthesizable proteins.
ProtGPT3-1.3B learns from public protein sequence datasets and may reproduce biases present in those datasets. Generated sequences may be low-complexity, nonfunctional, unstable, insoluble, or biologically implausible. Protein generation models may also present dual-use risks if used irresponsibly.
Users should apply appropriate computational filters, expert review, and experimental validation before using generated sequences. Users should also consider responsible-use practices for generative protein design.
ProtGPT3-1.3B is a decoder-only causal language model using a Mixtral-style sparse Mixture-of-Experts architecture. It was trained with a causal language modeling objective on protein sequences.
BibTeX:
bibtex
@article{protgpt3,title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models},author={Anonymous Authors},year={2026}}
All models and code are released through the Hugging Face ecosystem and accompanying code repository.