Instructions to use YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1") 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] - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1: direct link, hf CLI and curl.
- Browser
- Download file 5.11 kB
-
https://huggingface.co/YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1/resolve/main/training_args.bin
- Command line
-
hf download hf://YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/YeonwooSung/Llama-3-8B-llm2vec-Emb-v0.1/resolve/main/training_args.bin
5.11 kB
- Xet hash:
- c77afe645154d48277511fc2b0b0a77a8646fca64cb585e273cdd408e5c6b102
- Size of remote file:
- 5.11 kB
- SHA256:
- b91e170c5f154f1ba34a89fdf3fe4eb51594deb787cead5c3c1cb8a58ed48415
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