Nucleotide Transformer v3 (legacy 2025)
Collection
21 items β’ Updated β’ 5
How to use InstaDeepAI/ntv3_base_model with Transformers:
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="InstaDeepAI/ntv3_base_model") # pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("InstaDeepAI/ntv3_base_model", device_map="auto")This repo holds the code for the initial NTv3 release (2025). The 2025 checkpoints load their modelling code from here. The revised NTv3 models ship their own code in each repo.
Unified base model repository for NTv3 models.
This repository contains shared modeling code used by both:
Note: This repo should not be used standalone. It provides modeling code that is referenced by individual model checkpoints via trust_remote_code=True.
| File | Purpose |
|---|---|
configuration_ntv3_pretrained.py |
Config class: Ntv3PreTrainedConfig |
configuration_ntv3_posttrained.py |
Config classes: DiscreteConditionedNTv3Config, NTv3PostTrainedConfig |
modeling_ntv3_pretrained.py |
Pre-trained model: NTv3PreTrained |
modeling_ntv3_posttrained.py |
Post-trained model: NTv3PostTrained with conditioned towers and heads |
tokenization_ntv3.py |
Tokenizer: NTv3Tokenizer (DNA) |
<unk> <pad> <mask> <cls> <eos> <bos>)