Instructions to use ggml-org/embeddinggemma-300M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ggml-org/embeddinggemma-300M-GGUF with 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-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf ggml-org/embeddinggemma-300M-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-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf ggml-org/embeddinggemma-300M-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-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf ggml-org/embeddinggemma-300M-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-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0
Use Docker
docker model run hf.co/ggml-org/embeddinggemma-300M-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use ggml-org/embeddinggemma-300M-GGUF with Ollama:
ollama run hf.co/ggml-org/embeddinggemma-300M-GGUF:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use ggml-org/embeddinggemma-300M-GGUF with Docker Model Runner:
docker model run hf.co/ggml-org/embeddinggemma-300M-GGUF:Q8_0
- Lemonade
How to use ggml-org/embeddinggemma-300M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ggml-org/embeddinggemma-300M-GGUF:Q8_0
Run and chat with the model
lemonade run user.embeddinggemma-300M-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
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-GGUF:Q8_0# Run inference directly in the terminal:
llama cli -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0Use 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-GGUF:Q8_0# Run inference directly in the terminal:
./llama-cli -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0Build 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-GGUF:Q8_0# Run inference directly in the terminal:
./build/bin/llama-cli -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0Use Docker
docker model run hf.co/ggml-org/embeddinggemma-300M-GGUF:Q8_0embeddinggemma-300M GGUF
Recommended way to run this model:
llama-server -hf ggml-org/embeddinggemma-300M-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-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-GGUF --embd-normalize -1 -p "Hello embeddings"
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Base model
google/embeddinggemma-300m
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0# Run inference directly in the terminal: llama cli -hf ggml-org/embeddinggemma-300M-GGUF:Q8_0