Feature Extraction
Transformers
Joblib
Safetensors
MOJO
bulk RNA-seq
DNA methylation
biology
transcriptomics
epigenomics
multimodal
custom_code
Instructions to use InstaDeepAI/MOJO with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InstaDeepAI/MOJO with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="InstaDeepAI/MOJO", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InstaDeepAI/MOJO", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload MOJO
Browse files
mojo.py
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@@ -16,7 +16,7 @@ class RotaryEmbeddingConfig:
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Parameters to initialize the RotaryEmbedding layer. The rescaling factor allows
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to adapt the rotary embeddings to larger lengths than what was used for training.
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One of this strategy is presented in the Yarn paper: https://arxiv.org/pdf/2309.00071.pdf. # noqa
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Args:
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"""
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rescaling_factor: Optional[float]
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Parameters to initialize the RotaryEmbedding layer. The rescaling factor allows
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to adapt the rotary embeddings to larger lengths than what was used for training.
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One of this strategy is presented in the Yarn paper: https://arxiv.org/pdf/2309.00071.pdf. # noqa
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Args:b
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"""
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rescaling_factor: Optional[float]
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