Feature Extraction
Transformers
Safetensors
code
ntv3_annotate
genomics
dna
gene-annotation
ntv3
long-range
custom_code
Instructions to use InstaDeepAI/NTv3_annot_mammals with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/NTv3_annot_mammals with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/NTv3_annot_mammals", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/NTv3_annot_mammals", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
fix: store residual_scale as a scalar and drop the unused conditioning
#1
by Yanisadel - opened
residual_scale was stored as [1] where the model declares [], which transformers 5 refuses to load (and ignore_mismatched_sizes=True silently resets it to 1.0). The unused species-conditioning tensors are dropped too, as in the JAX export, so all three annotation repos take an empty conditions_tokens list. No value changes: produced by ntv3_huggingface/repack_annotation_safetensors.py, genomix-research commit c100b5e0a2.
Verified under transformers 4.57.1 and 5.17.0 (the latter with the NTv3_pretrained rotary PR): default load, the seven trained residual_scale values, and jax_model/roundtrip_fixture.npz reproduced at max abs <= 4.6e-05, 100% argmax.
bernardo-de-almeida changed pull request status to merged