Instructions to use AI4PD/ProtGPT3-112M-dpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PD/ProtGPT3-112M-dpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AI4PD/ProtGPT3-112M-dpo")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AI4PD/ProtGPT3-112M-dpo") model = AutoModelForCausalLM.from_pretrained("AI4PD/ProtGPT3-112M-dpo") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AI4PD/ProtGPT3-112M-dpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AI4PD/ProtGPT3-112M-dpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AI4PD/ProtGPT3-112M-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AI4PD/ProtGPT3-112M-dpo
- SGLang
How to use AI4PD/ProtGPT3-112M-dpo with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "AI4PD/ProtGPT3-112M-dpo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AI4PD/ProtGPT3-112M-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "AI4PD/ProtGPT3-112M-dpo" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AI4PD/ProtGPT3-112M-dpo", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AI4PD/ProtGPT3-112M-dpo with Docker Model Runner:
docker model run hf.co/AI4PD/ProtGPT3-112M-dpo
Model Card for ProtGPT3-112M-dpo
Model Description
ProtGPT3-112M-dpo is the DPO-aligned version of ProtGPT3-112M. It is part of the ProtGPT3 family, an open-source suite of promptable and aligned protein language models for protein design.
ProtGPT3-112M-dpo was further aligned with Direct Preference Optimization (DPO) to improve generation quality. The alignment procedure shifts the model toward protein sequences with higher predicted structural confidence and reduced low-complexity content, while preserving sequence diversity. For protein generation, each model -dpo version is recommended over the base model.
For more info and guidance on how to generate sequences with ProtGPT3-112M-dpo check out the extensive description provided in ProtGPT3-1.3B, just replacing the model name (i.e., model_name=AI4PD/ProtGPT3-112M-dpo).
Out-of-Scope Use
The model should not be used as the sole basis for experimental, clinical, environmental, or safety-critical decisions. Generated sequences require downstream computational and experimental validation. The model is not guaranteed to generate functional, soluble, safe, synthesizable, or experimentally successful proteins.
The model should not be used for irresponsible or harmful biological design applications.
Bias, Risks, and Limitations
ProtGPT3-112M-dpo learns from public protein sequence datasets and may reproduce biases present in those datasets. Although DPO alignment reduces low-complexity generations and improves generation quality according to the alignment objectives (pLDDT and reduction of lcr, as a binary objective, see main manuscript), generated sequences may still be nonfunctional, unstable, insoluble, repetitive, biologically implausible, or unsuitable for a user’s intended application.
The DPO alignment objective uses predicted structural confidence and low-complexity filtering as proxy objectives. These proxies do not guarantee biological function, experimental success, safety, solubility, or manufacturability.
As with other generative protein models, ProtGPT3-112M-dpo may present dual-use risks if applied irresponsibly.
Citation
BibTeX:
@article{protgpt3,
title={ProtGPT3: an Open-source family of Promptable and Aligned Protein Language Models},
author={Anonymous Authors},
year={2026}
}
More Information
For more info and guidance on how to generate sequences with ProtGPT3-112M-dpo check out the extensive description provided in ProtGPT3-1.3B, just replacing the model name (i.e., model_name=AI4PD/ProtGPT3-112M-dpo).
All models and code are released through the Hugging Face ecosystem and accompanying code repository.
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