Instructions to use AI4PD/ZymCTRL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AI4PD/ZymCTRL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AI4PD/ZymCTRL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("AI4PD/ZymCTRL") model = AutoModelForCausalLM.from_pretrained("AI4PD/ZymCTRL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AI4PD/ZymCTRL with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AI4PD/ZymCTRL" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AI4PD/ZymCTRL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AI4PD/ZymCTRL
- SGLang
How to use AI4PD/ZymCTRL 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/ZymCTRL" \ --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/ZymCTRL", "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/ZymCTRL" \ --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/ZymCTRL", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AI4PD/ZymCTRL with Docker Model Runner:
docker model run hf.co/AI4PD/ZymCTRL
Update README.md
Browse files
README.md
CHANGED
|
@@ -10,7 +10,7 @@ inference:
|
|
| 10 |
---
|
| 11 |
# **ZymCTRL**
|
| 12 |
|
| 13 |
-
ZymCTRL (Enzyme Control) ([
|
| 14 |
is a conditional language model for the generation of artificial functional enzymes. It was trained on the entire BRENDA database of enzymes, comprising over 37 M sequences.
|
| 15 |
Given a user-defined Enzymatic Commission (EC) number, the model generates protein sequences that fulfill that catalytic reaction.
|
| 16 |
The generated sequences are ordered, globular, and distant to natural ones, while their intended catalytic properties match those defined by users.
|
|
|
|
| 10 |
---
|
| 11 |
# **ZymCTRL**
|
| 12 |
|
| 13 |
+
ZymCTRL (Enzyme Control) ([ see preprint ](https://www.biorxiv.org/content/10.1101/2024.05.03.592223v1))
|
| 14 |
is a conditional language model for the generation of artificial functional enzymes. It was trained on the entire BRENDA database of enzymes, comprising over 37 M sequences.
|
| 15 |
Given a user-defined Enzymatic Commission (EC) number, the model generates protein sequences that fulfill that catalytic reaction.
|
| 16 |
The generated sequences are ordered, globular, and distant to natural ones, while their intended catalytic properties match those defined by users.
|