Instructions to use hogru/MolReactGen-USPTO50K-Reaction-Templates with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hogru/MolReactGen-USPTO50K-Reaction-Templates with Transformers:

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

pipe = pipeline("text-generation", model="hogru/MolReactGen-USPTO50K-Reaction-Templates")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hogru/MolReactGen-USPTO50K-Reaction-Templates")
model = AutoModelForCausalLM.from_pretrained("hogru/MolReactGen-USPTO50K-Reaction-Templates")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use hogru/MolReactGen-USPTO50K-Reaction-Templates with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hogru/MolReactGen-USPTO50K-Reaction-Templates"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hogru/MolReactGen-USPTO50K-Reaction-Templates",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hogru/MolReactGen-USPTO50K-Reaction-Templates

SGLang

How to use hogru/MolReactGen-USPTO50K-Reaction-Templates 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 "hogru/MolReactGen-USPTO50K-Reaction-Templates" \
    --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": "hogru/MolReactGen-USPTO50K-Reaction-Templates",
		"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 "hogru/MolReactGen-USPTO50K-Reaction-Templates" \
        --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": "hogru/MolReactGen-USPTO50K-Reaction-Templates",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hogru/MolReactGen-USPTO50K-Reaction-Templates with Docker Model Runner:
```
docker model run hf.co/hogru/MolReactGen-USPTO50K-Reaction-Templates
```

Model Card for Model hogru/MolReactGen-USPTO50K-Reaction-Templates

MolReactGen is a model that generates reaction templates in SMARTS format (this model) and molecules in SMILES format.

Model Details

Model Description

MolReactGen is based on the the GPT-2 transformer decoder architecture and has been trained on a pre-processed version of the USPTO-50K dataset. More information can be found in these introductory slides.

Developed by: Stephan Holzgruber
Model type: Transformer decoder
License: MIT

Model Sources

Repository: https://github.com/hogru/MolReactGen
Presentation: https://github.com/hogru/MolReactGen/blob/main/presentations/Slides%20(A4%20size).pdf
Poster: https://github.com/hogru/MolReactGen/blob/main/presentations/Poster%20(A0%20size).pdf

Uses

The main use of this model is to pass the master's examination of the author ;-)

Direct Use

The model can be used in a Hugging Face text generation pipeline. For the intended use case a wrapper around the raw text generation pipeline is needed. This is the generate.py from the repository. The model has a default GenerationConfig() (generation_config.json) which can be overwritten. Depending on the number of molecules to be generated (num_return_sequences in the JSON file) this might take a while. The generation code above shows a progress bar during generation.

Bias, Risks, and Limitations

The model generates reaction templates that are similar to the USPTO-50K training data. Any checks of the reaction templates, e.g. chemical feasiblitly, must be adressed by the user of the model.

Training Details

Training Data

Pre-processed version of the USPTO-50K dataset, originally introduced by Schneider et al..

Training Procedure

The default Hugging Face Trainer() has been used, with an EarlyStoppingCallback().

Preprocessing

The training data was pre-processed with a PreTrainedTokenizerFast() trained on the training data with a bespoke RegEx pre-tokenizer which "understands" the SMARTS syntax.

Training Hyperparameters

Batch size: 8
Gradient accumulation steps: 4
Mixed precision: fp16, native amp
Learning rate: 0.0005
Learning rate scheduler: Cosine
Learning rate scheduler warmup: 0.1
Optimizer: AdamW with betas=(0.9,0.95) and epsilon=1e-08
Number of epochs: 43 (early stopping)

More configuration (options) can be found in the conf directory of the repository.

Evaluation

Please see the slides / the poster mentioned above.

Metrics

Please see the slides / the poster mentioned above.

Results

Please see the slides / the poster mentioned above.

Technical Specifications

Framework versions

Transformers 4.27.1
Pytorch 1.13.1
Datasets 2.10.1
Tokenizers 0.13.2

Hardware

Local PC running Ubuntu 22.04
NVIDIA GEFORCE RTX 3080Ti (12GB)

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