Feature Extraction
Transformers
Safetensors
scripture_genome_head
scripture
multilingual
information-retrieval
semantic-search
genome-regression
frozen-encoder
custom-head
custom_code
Instructions to use chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download requirements.txt from chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3: direct link, hf CLI and curl.
- Browser
- Download file 178 Bytes
-
https://huggingface.co/chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/resolve/main/requirements.txt
- Command line
-
hf download hf://chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/requirements.txt
-
curl -L -o requirements.txt https://huggingface.co/chainuser1/Scripture_Genome_Multilingual_Head_BGE_M3/resolve/main/requirements.txt
178 Bytes
| # Tested loader API; install the appropriate CPU/CUDA torch build for your machine. | |
| torch>=2.6 | |
| transformers==4.57.6 | |
| safetensors>=0.4.3,<1 | |
| numpy>=1.26,<3 | |
| huggingface_hub>=0.34,<1 | |