- Libraries
- sentence-transformers
How to use SMARTICT/embeddinggemma_deik_finetune_GGUF with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("SMARTICT/embeddinggemma_deik_finetune_GGUF")
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] - llama-cpp-python
How to use SMARTICT/embeddinggemma_deik_finetune_GGUF with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="SMARTICT/embeddinggemma_deik_finetune_GGUF",
filename="embeddinggemma-300m.Q4_K_M.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 SMARTICT/embeddinggemma_deik_finetune_GGUF with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
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 SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
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 SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
Use Docker
docker model run hf.co/SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use SMARTICT/embeddinggemma_deik_finetune_GGUF with Ollama:
ollama run hf.co/SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
- Unsloth Studio new
How to use SMARTICT/embeddinggemma_deik_finetune_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 SMARTICT/embeddinggemma_deik_finetune_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 SMARTICT/embeddinggemma_deik_finetune_GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SMARTICT/embeddinggemma_deik_finetune_GGUF to start chatting
- Docker Model Runner
How to use SMARTICT/embeddinggemma_deik_finetune_GGUF with Docker Model Runner:
docker model run hf.co/SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
- Lemonade
How to use SMARTICT/embeddinggemma_deik_finetune_GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull SMARTICT/embeddinggemma_deik_finetune_GGUF:Q4_K_M
Run and chat with the model
lemonade run user.embeddinggemma_deik_finetune_GGUF-Q4_K_M
List all available models
lemonade list
from sentence_transformers import SentenceTransformer model = SentenceTransformer("SMARTICT/embeddinggemma_deik_finetune_GGUF") 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]