Instructions to use c2p-cmd/google_gemma_guff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use c2p-cmd/google_gemma_guff with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="c2p-cmd/google_gemma_guff", filename="gemma_snapshot/gemma-2b-it.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 c2p-cmd/google_gemma_guff with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf c2p-cmd/google_gemma_guff # Run inference directly in the terminal: llama-cli -hf c2p-cmd/google_gemma_guff
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf c2p-cmd/google_gemma_guff # Run inference directly in the terminal: llama-cli -hf c2p-cmd/google_gemma_guff
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 c2p-cmd/google_gemma_guff # Run inference directly in the terminal: ./llama-cli -hf c2p-cmd/google_gemma_guff
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 c2p-cmd/google_gemma_guff # Run inference directly in the terminal: ./build/bin/llama-cli -hf c2p-cmd/google_gemma_guff
Use Docker
docker model run hf.co/c2p-cmd/google_gemma_guff
- LM Studio
- Jan
- Ollama
How to use c2p-cmd/google_gemma_guff with Ollama:
ollama run hf.co/c2p-cmd/google_gemma_guff
- Unsloth Studio new
How to use c2p-cmd/google_gemma_guff 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 c2p-cmd/google_gemma_guff 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 c2p-cmd/google_gemma_guff to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for c2p-cmd/google_gemma_guff to start chatting
- Docker Model Runner
How to use c2p-cmd/google_gemma_guff with Docker Model Runner:
docker model run hf.co/c2p-cmd/google_gemma_guff
- Lemonade
How to use c2p-cmd/google_gemma_guff with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull c2p-cmd/google_gemma_guff
Run and chat with the model
lemonade run user.google_gemma_guff-{{QUANT_TAG}}List all available models
lemonade list
Gemma Model Card
Model Page: Gemma
This model card corresponds to the 2B and 7B Instruct versions of the Gemma model's Guff.
Terms of Use: Terms
Description
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models.
Model Usage
Since this is a guff, it can be run locally using
- Ollama
- Llama.cpp
- LM Studio
- And Many More
- I have provided GemmaModelFile that can be used with ollama by:
- Download the model:
pip install huggingface_hub from huggingface_hub import hf_hub_download model_id="c2p-cmd/google_gemma_guff" hf_hub_download(repo_id=model_id, local_dir="gemma_snapshot", local_dir_use_symlinks=False, filename="gemma_snapshot/gemma-2b-it.gguf") - Load the model file to ollama
ollama create gemma -f GemmaModelFile - You change the model name based on needs
- Downloads last month
- 17
Hardware compatibility
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