Feature Extraction
Transformers
Safetensors
GenoJEPA
genomics
dna
representation-learning
custom_code
Instructions to use ChengsenWang/GenoJEPA-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ChengsenWang/GenoJEPA-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ChengsenWang/GenoJEPA-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForPreTraining model = AutoModelForPreTraining.from_pretrained("ChengsenWang/GenoJEPA-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from ChengsenWang/GenoJEPA-Base: direct link, hf CLI and curl.
- Browser
- Download file 744 Bytes
-
https://huggingface.co/ChengsenWang/GenoJEPA-Base/resolve/main/config.json
- Command line
-
hf download hf://ChengsenWang/GenoJEPA-Base/config.json
-
curl -L -o config.json https://huggingface.co/ChengsenWang/GenoJEPA-Base/resolve/main/config.json
744 Bytes
| { | |
| "alpha": 0.05, | |
| "architectures": [ | |
| "GenoJEPAForPreTraining" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "model.GenoJEPAConfig", | |
| "AutoModel": "model.GenoJEPAForSequenceEmbedding", | |
| "AutoModelForSequenceClassification": "model.GenoJEPAForSequenceClassification" | |
| }, | |
| "dtype": "float32", | |
| "embed_size": 16, | |
| "global_num": 2, | |
| "global_scale": [ | |
| 0.65, | |
| 0.8 | |
| ], | |
| "head_num": 16, | |
| "head_size": 16, | |
| "hidden_size": 512, | |
| "intermediate_size": 2048, | |
| "layer_num": 12, | |
| "local_num": 6, | |
| "local_scale": [ | |
| 0.35, | |
| 0.4 | |
| ], | |
| "max_t": 3.0, | |
| "model_type": "GenoJEPA", | |
| "pad_token_id": 4, | |
| "pad_value": 4, | |
| "patch_size": 16, | |
| "point_num": 17, | |
| "slice_num": 256, | |
| "transformers_version": "4.57.6", | |
| "vocab_num": 5 | |
| } | |