Instructions to use InstaDeepAI/nucleotide-transformer-2.5b-1000g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/nucleotide-transformer-2.5b-1000g with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="InstaDeepAI/nucleotide-transformer-2.5b-1000g")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("InstaDeepAI/nucleotide-transformer-2.5b-1000g") model = AutoModelForMaskedLM.from_pretrained("InstaDeepAI/nucleotide-transformer-2.5b-1000g") - Notebooks
- Google Colab
- Kaggle
Add TF weights
#2
by Rocketknight1 HF Staff - opened
Model converted by the transformers' pt_to_tf CLI. All converted model outputs and hidden layers were validated against its PyTorch counterpart.
Maximum crossload output difference=2.098e-04; Maximum crossload hidden layer difference=3.662e-04;
Maximum conversion output difference=2.098e-04; Maximum conversion hidden layer difference=3.662e-04;
CAUTION: The maximum admissible error was manually increased to 0.0005!
tpierrot changed pull request status to merged