Feature Extraction
Transformers
Safetensors
caduceus_custom
genomics
dna
sequence-modeling
custom_code
Instructions to use micanonsens/bigamba-seq_only-step44000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use micanonsens/bigamba-seq_only-step44000 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="micanonsens/bigamba-seq_only-step44000", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("micanonsens/bigamba-seq_only-step44000", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
BiGamba (MLM, step 44000)
Repository: micanonsens/bigamba-seq_only-step44000
The Gamba models are a family of DNA language models from the ArGamba paper that jointly model DNA sequence and evolutionary rate information.
This repository includes the model weights and code for the Caduceus-based bidirectional variant model (BiGamba) trained to predict the human genome sequence using the Masked Language Modelling (MLM) task. For more details, see the GitHub repo.
Model family
All Gamba family models have checkpoints available at 44,000 steps:
| Checkpoint name | Architecture | Training task |
|---|---|---|
| ArGamba-dual | ArGamba (Jamba autoregressive) | NTP + CEP |
| ArGamba-seq_only | ArGamba (Jamba autoregressive) | NTP |
| ArGamba-cons_only | ArGamba (Jamba autoregressive) | CEP |
| BiGamba-dual | BiGamba (Mamba bidirectional) | MLM + MEM |
| BiGamba-seq_only | BiGamba (Mamba bidirectional) | MLM |
| BiGamba-cons_only | BiGamba (Mamba bidirectional) | MEM |
Load
from transformers import AutoModel
model = AutoModel.from_pretrained(
"micanonsens/bigamba-seq_only-step44000",
trust_remote_code=True
)
Notes
- This repository includes custom modeling code;
trust_remote_code=Trueis required. - Ensure your environment has the necessary project dependencies installed (see GitHub).
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