Instructions to use InstructPLM/MPNN-ProGen2-xlarge-CATH42 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InstructPLM/MPNN-ProGen2-xlarge-CATH42 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="InstructPLM/MPNN-ProGen2-xlarge-CATH42", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("InstructPLM/MPNN-ProGen2-xlarge-CATH42", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use InstructPLM/MPNN-ProGen2-xlarge-CATH42 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InstructPLM/MPNN-ProGen2-xlarge-CATH42"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InstructPLM/MPNN-ProGen2-xlarge-CATH42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InstructPLM/MPNN-ProGen2-xlarge-CATH42

SGLang

How to use InstructPLM/MPNN-ProGen2-xlarge-CATH42 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 "InstructPLM/MPNN-ProGen2-xlarge-CATH42" \
    --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": "InstructPLM/MPNN-ProGen2-xlarge-CATH42",
		"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 "InstructPLM/MPNN-ProGen2-xlarge-CATH42" \
        --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": "InstructPLM/MPNN-ProGen2-xlarge-CATH42",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InstructPLM/MPNN-ProGen2-xlarge-CATH42 with Docker Model Runner:
```
docker model run hf.co/InstructPLM/MPNN-ProGen2-xlarge-CATH42
```

InstrcutPLM

InstructPLM is a state-of-the-art protein design model based on ProGen2 and ProteinMPNN and trained on CATH 4.2 dataset. It can design protein sequences that accurately conform to specified backbone structures.

drawing

Please visit our repo and paper for more information.

@article {Qiu2024.04.17.589642,
    author = {Jiezhong Qiu and Junde Xu and Jie Hu and Hanqun Cao and Liya Hou and Zijun Gao and Xinyi Zhou and Anni Li and Xiujuan Li and Bin Cui and Fei Yang and Shuang Peng and Ning Sun and Fangyu Wang and Aimin Pan and Jie Tang and Jieping Ye and Junyang Lin and Jin Tang and Xingxu Huang and Pheng Ann Heng and Guangyong Chen},
    title = {InstructPLM: Aligning Protein Language Models to Follow Protein Structure Instructions},
    elocation-id = {2024.04.17.589642},
    year = {2024},
    doi = {10.1101/2024.04.17.589642},
    publisher = {Cold Spring Harbor Laboratory},
    URL = {https://www.biorxiv.org/content/early/2024/04/20/2024.04.17.589642},
    eprint = {https://www.biorxiv.org/content/early/2024/04/20/2024.04.17.589642.full.pdf},
    journal = {bioRxiv}
}

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