Instructions to use mossaic-candle/adaptive-lm-molecules with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mossaic-candle/adaptive-lm-molecules with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="mossaic-candle/adaptive-lm-molecules")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("mossaic-candle/adaptive-lm-molecules") model = AutoModelForMaskedLM.from_pretrained("mossaic-candle/adaptive-lm-molecules") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d03b5b6ac2317e1970da3e68b2aedddcbbdef09e08e34c37d35c04656aad11c9
- Size of remote file:
- 223 MB
- SHA256:
- b8f66965ff40410c17ddad8c7c3ee0fd419bed61719e253b556b6fedfc3712c6
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.