Instructions to use hkqiu/PolymerGenerationPretrainedModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hkqiu/PolymerGenerationPretrainedModel with Transformers:

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

pipe = pipeline("text-generation", model="hkqiu/PolymerGenerationPretrainedModel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("hkqiu/PolymerGenerationPretrainedModel")
model = AutoModelForSeq2SeqLM.from_pretrained("hkqiu/PolymerGenerationPretrainedModel")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hkqiu/PolymerGenerationPretrainedModel with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/hkqiu/PolymerGenerationPretrainedModel

SGLang

How to use hkqiu/PolymerGenerationPretrainedModel 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 "hkqiu/PolymerGenerationPretrainedModel" \
    --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": "hkqiu/PolymerGenerationPretrainedModel",
		"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 "hkqiu/PolymerGenerationPretrainedModel" \
        --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": "hkqiu/PolymerGenerationPretrainedModel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hkqiu/PolymerGenerationPretrainedModel with Docker Model Runner:
```
docker model run hf.co/hkqiu/PolymerGenerationPretrainedModel
```

hkqiu commited on Dec 2, 2024

Commit

ae1b627

verified ·

1 Parent(s): ef299af

Update README.md

Browse files

Files changed (1) hide show

README.md +19 -1

README.md CHANGED Viewed

@@ -29,4 +29,22 @@ RingCount
 Once one supplied these prompts, PolyTAO can generate polymers based on these characteristics.
-__Try it, such as: "264,19,0,4,1,0,1,0,0,0,4,0,6,5,1"!__

 Once one supplied these prompts, PolyTAO can generate polymers based on these characteristics.
+__Try it, such as: "264,19,0,4,1,0,1,0,0,0,4,0,6,5,1"!__
+If you find this work helpful, please kindly cite this work :):
+```
+@article{qiuOndemandReverseDesign2024,
+  title = {On-Demand Reverse Design of Polymers with {{PolyTAO}}},
+  author = {Qiu, Haoke and Sun, Zhao-Yan},
+  year = {2024},
+  month = nov,
+  journal = {npj Computational Materials},
+  volume = {10},
+  number = {1},
+  pages = {273},
+  issn = {2057-3960},
+  doi = {10.1038/s41524-024-01466-5}
+}
+```