Instructions to use nikcheerla/amd-model-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use nikcheerla/amd-model-v5 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("nikcheerla/amd-model-v5") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - setfit
How to use nikcheerla/amd-model-v5 with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("nikcheerla/amd-model-v5") preds = model.predict(["i loved the spiderman movie!", "pineapple on pizza is the worst"]) print(preds) - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from nikcheerla/amd-model-v5: direct link, hf CLI and curl.
- Browser
- Download file 438 MB
-
https://huggingface.co/nikcheerla/amd-model-v5/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://nikcheerla/amd-model-v5/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/nikcheerla/amd-model-v5/resolve/main/pytorch_model.bin
438 MB
- Xet hash:
- fb1af857466966a4e4483965d577ff020b800d21290f4801dab2949567828894
- Size of remote file:
- 438 MB
- SHA256:
- ad12a3c85699f1861685309feff82c466057be3eee2fc1f907e18955daed6072
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.