Instructions to use Ghani-25/LF_enrich_sim with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ghani-25/LF_enrich_sim with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Ghani-25/LF_enrich_sim")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Ghani-25/LF_enrich_sim") model = AutoModel.from_pretrained("Ghani-25/LF_enrich_sim", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from Ghani-25/LF_enrich_sim: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/Ghani-25/LF_enrich_sim/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://Ghani-25/LF_enrich_sim/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/Ghani-25/LF_enrich_sim/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- bb6d2e68031c42de7e9785d0bb47b5bff188aa77d67e106ded4aaca013732271
- Size of remote file:
- 1.11 GB
- SHA256:
- 29d10eabb079d11355d4405789dd9d43e229ccbc47bc2581d0639edaa1721380
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