Instructions to use HoangHa/Smiles_ChemBL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use HoangHa/Smiles_ChemBL with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="HoangHa/Smiles_ChemBL")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("HoangHa/Smiles_ChemBL") model = AutoModelForMaskedLM.from_pretrained("HoangHa/Smiles_ChemBL", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from HoangHa/Smiles_ChemBL: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/HoangHa/Smiles_ChemBL/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://HoangHa/Smiles_ChemBL/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/HoangHa/Smiles_ChemBL/resolve/main/flax_model.msgpack
499 MB
- Xet hash:
- 6cc86e80b29992b736bfadbddb0fc19bc69627a16edd884a24daf5c62b1c236c
- Size of remote file:
- 499 MB
- SHA256:
- f1c957953dd705cc6d0c1632a0e111443de4b19f44489e7d1711c2e0fe10e1c7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.