How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
Build from source code
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 server with a web UI:
./build/bin/llama-server -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/ggml-org/embeddinggemma-300m-qat-q8_0-GGUF:Q8_0
Quick Links

embeddinggemma-300m-qat-q8_0 GGUF

Recommended way to run this model:

llama-server -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF --embeddings

Then the endpoint can be accessed at http://localhost:8080/embedding, for example using curl:

curl --request POST \
    --url http://localhost:8080/embedding \
    --header "Content-Type: application/json" \
    --data '{"input": "Hello embeddings"}' \
    --silent

Alternatively, the llama-embedding command line tool can be used:

llama-embedding -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF --verbose-prompt -p "Hello embeddings"

embd_normalize

When a model uses pooling, or the pooling method is specified using --pooling, the normalization can be controlled by the embd_normalize parameter.

The default value is 2 which means that the embeddings are normalized using the Euclidean norm (L2). Other options are:

  • -1 No normalization
  • 0 Max absolute
  • 1 Taxicab
  • 2 Euclidean/L2
  • >2 P-Norm

This can be passed in the request body to llama-server, for example:

    --data '{"input": "Hello embeddings", "embd_normalize": -1}' \

And for llama-embedding, by passing --embd-normalize <value>, for example:

llama-embedding -hf ggml-org/embeddinggemma-300m-qat-q8_0-GGUF  --embd-normalize -1 -p "Hello embeddings"
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