Instructions to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="qwp4w3hyb/gemma-3-27b-it-iMat-GGUF", filename="gemma-3-27b-it-bf16-00001-of-00002.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf qwp4w3hyb/gemma-3-27b-it-iMat-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 qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf qwp4w3hyb/gemma-3-27b-it-iMat-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 qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf qwp4w3hyb/gemma-3-27b-it-iMat-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 qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with Ollama:
ollama run hf.co/qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M
- Unsloth Studio
How to use qwp4w3hyb/gemma-3-27b-it-iMat-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 qwp4w3hyb/gemma-3-27b-it-iMat-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 qwp4w3hyb/gemma-3-27b-it-iMat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for qwp4w3hyb/gemma-3-27b-it-iMat-GGUF to start chatting
- Docker Model Runner
How to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with Docker Model Runner:
docker model run hf.co/qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M
- Lemonade
How to use qwp4w3hyb/gemma-3-27b-it-iMat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull qwp4w3hyb/gemma-3-27b-it-iMat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-3-27b-it-iMat-GGUF-Q4_K_M
List all available models
lemonade list
Quant Infos
- Requires llama.cpp b4875
- LLM ONLY (No vision support)
- quants done with an importance matrix for improved quantization loss
- Quantized ggufs & imatrix from hf bf16, through bf16.
safetensors bf16 -> gguf bf16 -> quantfor optimal quant loss. - Wide coverage of different gguf quant types from Q_8_0 down to IQ1_S (WIP)
- Imatrix generated with this multi-purpose dataset by bartowski.
./imatrix -m $model_name-bf16.gguf -f calibration_datav3.txt -o $model_name.imatrix
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Hardware compatibility
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