Feature Extraction
sentence-transformers
Safetensors
English
bert
sentence-similarity
agents
rag
embeddings
llmops
text-embeddings-inference
Instructions to use hharsha/agentic-systems-minilm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use hharsha/agentic-systems-minilm with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("hharsha/agentic-systems-minilm") sentences = [ "hybrid search and reranking for RAG", "RetrievalLab shows advanced RAG with hybrid search and cross-encoder reranking.", "A cooking recipe for pasta carbonara.", "AgentFleet runs multi-agent task DAGs with cost governance." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from hharsha/agentic-systems-minilm: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/hharsha/agentic-systems-minilm/resolve/main/model.safetensors
- Command line
-
hf download hf://hharsha/agentic-systems-minilm/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/hharsha/agentic-systems-minilm/resolve/main/model.safetensors
90.9 MB
- Xet hash:
- 9361bc307f7a2dcccdcbebd632a8c73702bc086b2dbf1eee589f23d1d6a3aadc
- Size of remote file:
- 90.9 MB
- SHA256:
- db4e2cf2a928c51bed39adef48fc18f0b10c2e6c9bab82837729e3b7a7b46098
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.