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

Libraries

How to use gokceuludogan/ChemBERTaLM with Transformers:

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

pipe = pipeline("text-generation", model="gokceuludogan/ChemBERTaLM")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
model = AutoModelForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use gokceuludogan/ChemBERTaLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gokceuludogan/ChemBERTaLM

SGLang

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

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

ChemBERTaLM

A molecule generator model finetuned from ChemBERTa checkpoint. It was introduced in the paper, "Exploiting pretrained biochemical language models for targeted drug design", which has been accepted for publication in Bioinformatics Published by Oxford University Press and first released in this repository.

ChemBERTaLM is a RoBERTa model initialized with ChemBERTa checkpoint, and then, finetuned on the MOSES dataset which comprises a collection of drug-like compounds.

How to use

from transformers import RobertaForCausalLM, RobertaTokenizer, pipeline
tokenizer = RobertaTokenizer.from_pretrained("gokceuludogan/ChemBERTaLM")
model = RobertaForCausalLM.from_pretrained("gokceuludogan/ChemBERTaLM")
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
generator("", max_length=128, do_sample=True)
# Sample output
[{'generated_text': 'Cc1ccc(C(=O)N2CCN(C(=O)c3ccc(F)cc3)CC2)cc1'}]

Citation

@article{10.1093/bioinformatics/btac482,
    author = {Uludoğan, Gökçe and Ozkirimli, Elif and Ulgen, Kutlu O. and Karalı, Nilgün Lütfiye and Özgür, Arzucan},
    title = "{Exploiting Pretrained Biochemical Language Models for Targeted Drug Design}",
    journal = {Bioinformatics},
    year = {2022},
    doi = {10.1093/bioinformatics/btac482},
    url = {https://doi.org/10.1093/bioinformatics/btac482}
}

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Citation

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