Instructions to use dispatchAI/EmbeddingGemma-300M-mobile with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dispatchAI/EmbeddingGemma-300M-mobile with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("dispatchAI/EmbeddingGemma-300M-mobile", dtype="auto")

llama-cpp-python

How to use dispatchAI/EmbeddingGemma-300M-mobile with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dispatchAI/EmbeddingGemma-300M-mobile",
	filename="model.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use dispatchAI/EmbeddingGemma-300M-mobile 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 dispatchAI/EmbeddingGemma-300M-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/EmbeddingGemma-300M-mobile

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dispatchAI/EmbeddingGemma-300M-mobile
# Run inference directly in the terminal:
llama cli -hf dispatchAI/EmbeddingGemma-300M-mobile

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 dispatchAI/EmbeddingGemma-300M-mobile
# Run inference directly in the terminal:
./llama-cli -hf dispatchAI/EmbeddingGemma-300M-mobile

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 dispatchAI/EmbeddingGemma-300M-mobile
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dispatchAI/EmbeddingGemma-300M-mobile

Use Docker

docker model run hf.co/dispatchAI/EmbeddingGemma-300M-mobile

LM Studio
Jan
Ollama
How to use dispatchAI/EmbeddingGemma-300M-mobile with Ollama:
```
ollama run hf.co/dispatchAI/EmbeddingGemma-300M-mobile
```

Unsloth Studio

How to use dispatchAI/EmbeddingGemma-300M-mobile 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 dispatchAI/EmbeddingGemma-300M-mobile 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 dispatchAI/EmbeddingGemma-300M-mobile to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dispatchAI/EmbeddingGemma-300M-mobile to start chatting

Atomic Chat new
Docker Model Runner
How to use dispatchAI/EmbeddingGemma-300M-mobile with Docker Model Runner:
```
docker model run hf.co/dispatchAI/EmbeddingGemma-300M-mobile
```

Lemonade

How to use dispatchAI/EmbeddingGemma-300M-mobile with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dispatchAI/EmbeddingGemma-300M-mobile

Run and chat with the model

lemonade run user.EmbeddingGemma-300M-mobile-{{QUANT_TAG}}

List all available models

lemonade list

EmbeddingGemma-300M-mobile

🔢 Embedding — This is not a chat model.

Embedding model — use for vector embeddings, not chat. Use sentence-transformers or similar.

Model Details

Attribute	Value
Base Model	google/embeddinggemma-300m
Type	EMBEDDING
License	gemma

Usage

from sentence_transformers import SentenceTransformer; model = SentenceTransformer('dispatchAI/EmbeddingGemma-300M-mobile')

🚀 dispatchAI

Downloads last month: 432

GGUF

Model size

0.3B params

Architecture

gemma-embedding

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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