Instructions to use Labib11/PMC_GIST with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Labib11/PMC_GIST with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Labib11/PMC_GIST")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Labib11/PMC_GIST") model = AutoModel.from_pretrained("Labib11/PMC_GIST", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config_sentence_transformers.json from Labib11/PMC_GIST: direct link, hf CLI and curl.
- Browser
- Download file 171 Bytes
-
https://huggingface.co/Labib11/PMC_GIST/resolve/main/config_sentence_transformers.json
- Command line
-
hf download hf://Labib11/PMC_GIST/config_sentence_transformers.json
-
curl -L -o config_sentence_transformers.json https://huggingface.co/Labib11/PMC_GIST/resolve/main/config_sentence_transformers.json
171 Bytes
| { | |
| "__version__": { | |
| "sentence_transformers": "2.7.0", | |
| "transformers": "4.40.1", | |
| "pytorch": "2.2.0+cu121" | |
| }, | |
| "prompts": {}, | |
| "default_prompt_name": null | |
| } |