Text Generation
PEFT
Safetensors
sentence-transformers
English
scientific-summarization
lora
llama
corpus-level
research-clusters
Instructions to use jimnoneill/BSG_CyLlama with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use jimnoneill/BSG_CyLlama with PEFT:
Task type is invalid.
- sentence-transformers
How to use jimnoneill/BSG_CyLlama with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("jimnoneill/BSG_CyLlama") 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 model/tokenizer.json from jimnoneill/BSG_CyLlama: direct link, hf CLI and curl.
- Browser
- Download file 17.2 MB
-
https://huggingface.co/jimnoneill/BSG_CyLlama/resolve/main/model/tokenizer.json
- Command line
-
hf download hf://jimnoneill/BSG_CyLlama/model/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/jimnoneill/BSG_CyLlama/resolve/main/model/tokenizer.json
17.2 MB
- Xet hash:
- 2e56cad9ac2a38f4b7de211953881b613c19e51838c05adf8f7984c139c2e8f5
- Size of remote file:
- 17.2 MB
- SHA256:
- 6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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