Instructions to use ggml-org/embeddinggemma-300M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ggml-org/embeddinggemma-300M-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ggml-org/embeddinggemma-300M-GGUF", filename="embeddinggemma-300M-Q8_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use ggml-org/embeddinggemma-300M-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # 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
Install from WinGet (Windows)
winget install llama.cpp # 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
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 Studio new
How to use ggml-org/embeddinggemma-300M-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ggml-org/embeddinggemma-300M-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for ggml-org/embeddinggemma-300M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ggml-org/embeddinggemma-300M-GGUF to start chatting
- 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
embeddinggemma-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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Model tree for ggml-org/embeddinggemma-300M-GGUF
Base model
google/embeddinggemma-300m