How to use rizkysulaeman/Embedding-Gemma-300m with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("rizkysulaeman/Embedding-Gemma-300m") 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]
How to use rizkysulaeman/Embedding-Gemma-300m with llama.cpp:
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible embeddings server: llama serve -hf rizkysulaeman/Embedding-Gemma-300m:Q4_K_M --embeddings
winget install llama.cpp # Start a local OpenAI-compatible embeddings server: llama serve -hf rizkysulaeman/Embedding-Gemma-300m: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 rizkysulaeman/Embedding-Gemma-300m: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 rizkysulaeman/Embedding-Gemma-300m:Q4_K_M --embeddings
How to use rizkysulaeman/Embedding-Gemma-300m with Ollama:
ollama run hf.co/rizkysulaeman/Embedding-Gemma-300m:Q4_K_M
How to use rizkysulaeman/Embedding-Gemma-300m with Docker Model Runner:
docker model run hf.co/rizkysulaeman/Embedding-Gemma-300m:Q4_K_M
How to use rizkysulaeman/Embedding-Gemma-300m with Lemonade:
# Download Lemonade from https://lemonade-server.ai/ lemonade pull rizkysulaeman/Embedding-Gemma-300m:Q4_K_M
lemonade run user.Embedding-Gemma-300m-Q4_K_M
lemonade list
This sentence-transformers model was finetuned and converted to GGUF format using Unsloth.
embeddinggemma-300m.Q5_K_M.gguf
embeddinggemma-300m.Q8_0.gguf
embeddinggemma-300m.Q4_K_M.gguf
This was trained 2x faster with Unsloth
Files info
Base model
