How to use smshahbaj/embeddinggemma-2-ecommerce with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("smshahbaj/embeddinggemma-2-ecommerce") sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4]
How to use smshahbaj/embeddinggemma-2-ecommerce with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible embeddings server: ./llama-server -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible embeddings server: ./build/bin/llama-server -hf smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M --embeddings
How to use smshahbaj/embeddinggemma-2-ecommerce with Ollama:
ollama run hf.co/smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
How to use smshahbaj/embeddinggemma-2-ecommerce with Docker Model Runner:
docker model run hf.co/smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
How to use smshahbaj/embeddinggemma-2-ecommerce with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull smshahbaj/embeddinggemma-2-ecommerce:Q4_K_M
lemonade run user.embeddinggemma-2-ecommerce-Q4_K_M
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