Instructions to use cortexso/gemma with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use cortexso/gemma with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="cortexso/gemma", filename="gemma-7b-it-q2_k.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use cortexso/gemma with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/gemma:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/gemma:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf cortexso/gemma:Q4_K_M # Run inference directly in the terminal: llama-cli -hf cortexso/gemma: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 cortexso/gemma:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf cortexso/gemma: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 cortexso/gemma:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf cortexso/gemma:Q4_K_M
Use Docker
docker model run hf.co/cortexso/gemma:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use cortexso/gemma with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "cortexso/gemma" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "cortexso/gemma", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/cortexso/gemma:Q4_K_M
- Ollama
How to use cortexso/gemma with Ollama:
ollama run hf.co/cortexso/gemma:Q4_K_M
- Unsloth Studio new
How to use cortexso/gemma 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 cortexso/gemma 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 cortexso/gemma to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for cortexso/gemma to start chatting
- Docker Model Runner
How to use cortexso/gemma with Docker Model Runner:
docker model run hf.co/cortexso/gemma:Q4_K_M
- Lemonade
How to use cortexso/gemma with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull cortexso/gemma:Q4_K_M
Run and chat with the model
lemonade run user.gemma-Q4_K_M
List all available models
lemonade list
Merge branch 'main' of hf.co:cortexso/gemma into default
Browse files
README.md
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---
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license: gemma
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---
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## Overview
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The [Gemma](https://huggingface.co/microsoft/Gemma-mini-4k-instruct), state-of-the-art open model trained with the Gemma datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Gemma family with the 4B, 7B version in two variants 8K and 128K which is the context length (in tokens) that it can support.
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## Variants
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| No | Variant | Cortex CLI command |
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| --- | --- | --- |
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| 1 | [7b-gguf](https://huggingface.co/cortexso/gemma/tree/7b-gguf) | `cortex run gemma:7b-gguf` |
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| 2 | [7b-onnx](https://huggingface.co/cortexso/gemma/tree/7b-onnx) | `cortex run gemma:7b-onnx` |
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## Use it with Jan (UI)
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1. Install **Jan** using [Quickstart](https://jan.ai/docs/quickstart)
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2. Use in Jan model Hub:
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```
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cortexso/gemma
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```
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## Use it with Cortex (CLI)
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1. Install **Cortex** using [Quickstart](https://cortex.jan.ai/docs/quickstart)
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2. Run the model with command:
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```
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cortex run gemma
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```
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## Credits
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- **Author:** Google
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- **Converter:** [Homebrew](https://www.homebrew.ltd/)
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- **Original License:** [License](https://ai.google.dev/gemma/terms)
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- **Papers:** [Gemma Technical Report](https://arxiv.org/abs/2403.08295)
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