Fill-Mask
Transformers
Safetensors
nucengram
feature-extraction
biology
genomics
dna
masked-lm
custom_code
Instructions to use FreakingPotato/NucEngram with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use FreakingPotato/NucEngram with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="FreakingPotato/NucEngram", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("FreakingPotato/NucEngram", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """HuggingFace config for the NucEngram genomic language model.""" | |
| from transformers import PretrainedConfig | |
| class NucEngramConfig(PretrainedConfig): | |
| model_type = "nucengram" | |
| def __init__(self, backbone=None, engram=None, max_length=8192, pad_token_id=0, **kwargs): | |
| # `backbone` = ModernBertGenomicConfig kwargs; `engram` = EngramConfig kwargs. | |
| self.backbone = backbone or {} | |
| self.engram = engram or {} | |
| self.max_length = max_length | |
| super().__init__(pad_token_id=pad_token_id, **kwargs) | |