Instructions to use contributor-anonymous/Mol2Pro-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use contributor-anonymous/Mol2Pro-base with Transformers:

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

pipe = pipeline("text-generation", model="contributor-anonymous/Mol2Pro-base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("contributor-anonymous/Mol2Pro-base")
model = AutoModelForSeq2SeqLM.from_pretrained("contributor-anonymous/Mol2Pro-base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use contributor-anonymous/Mol2Pro-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "contributor-anonymous/Mol2Pro-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "contributor-anonymous/Mol2Pro-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/contributor-anonymous/Mol2Pro-base

SGLang

How to use contributor-anonymous/Mol2Pro-base 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 "contributor-anonymous/Mol2Pro-base" \
    --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": "contributor-anonymous/Mol2Pro-base",
		"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 "contributor-anonymous/Mol2Pro-base" \
        --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": "contributor-anonymous/Mol2Pro-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use contributor-anonymous/Mol2Pro-base with Docker Model Runner:
```
docker model run hf.co/contributor-anonymous/Mol2Pro-base
```

Mol2Pro-base

Model description

Architecture: T5-efficient-base https://huggingface.co/google/t5-efficient-base
Tokenization: https://huggingface.co/contributor-anonymous/Mol2Pro-tokenizer
Code: https://github.com/contributor-anonymous/Mol2Pro-tools
Training data https://huggingface.co/datasets/contributor-anonymous/Mol2Pro-Binder-Dataset

How to use

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

model_id = "contributor-anonymous/Mol2Pro-base"
tokenizer_id = "contributor-anonymous/Mol2Pro-tokenizer"

# Load tokenizers
tokenizer_mol = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="smiles")
tokenizer_aa  = AutoTokenizer.from_pretrained(tokenizer_id, subfolder="aa")

# Load model
model = AutoModelForSeq2SeqLM.from_pretrained(model_id)

Intended use

Research use only. The model generates candidate sequences conditioned on small-molecule inputs; it does not guarantee binding or function and must be validated experimentally.

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contributor-anonymous
/

Mol2Pro-base

Mol2Pro-base

Model description

How to use

Intended use

Dataset used to train contributor-anonymous/Mol2Pro-base