Instructions to use revanth1996/gte_repro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use revanth1996/gte_repro with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="revanth1996/gte_repro", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("revanth1996/gte_repro", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from revanth1996/gte_repro: direct link, hf CLI and curl.
- Browser
- Download file 229 Bytes
-
https://huggingface.co/revanth1996/gte_repro/resolve/main/modules.json
- Command line
-
hf download hf://revanth1996/gte_repro/modules.json
-
curl -L -o modules.json https://huggingface.co/revanth1996/gte_repro/resolve/main/modules.json
229 Bytes
| [ | |
| { | |
| "idx": 0, | |
| "name": "0", | |
| "path": "", | |
| "type": "sentence_transformers.models.Transformer" | |
| }, | |
| { | |
| "idx": 1, | |
| "name": "1", | |
| "path": "1_Pooling", | |
| "type": "sentence_transformers.models.Pooling" | |
| } | |
| ] |