Instructions to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF") messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF", dtype="auto") - llama-cpp-python
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF", filename="gemma-3-27b-it-q4_k_m.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-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 mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-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 mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-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 mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker
docker model run hf.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
- SGLang
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }' - Ollama
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with Ollama:
ollama run hf.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
- Unsloth Studio
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-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 mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-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 mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with Docker Model Runner:
docker model run hf.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
- Lemonade
How to use mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-3-27b-it-Q4_K_M-Q6_K-GGUF-Q4_K_M
List all available models
lemonade list
Produced by Antigma Labs
llama.cpp quantization
Using llama.cpp release b4944 for quantization. Original model: https://huggingface.co/google/gemma-3-27b-it Run them directly with llama.cpp, or any other llama.cpp based project
Prompt format
<|begin▁of▁sentence|>{system_prompt}<|User|>{prompt}<|Assistant|><|end▁of▁sentence|><|Assistant|>
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split |
|---|---|---|---|
| gemma-3-27b-it-q4_k_m.gguf | Q4_K_M | 15.41 GB | False |
| gemma-3-27b-it-q6_k.gguf | Q6_K | 20.64 GB | False |
Downloading using huggingface-cli
Click to view download instructions
First, make sure you have hugginface-cli installed: ``` pip install -U "huggingface_hub[cli]" ``` Then, you can target the specific file you want: ``` huggingface-cli download https://huggingface.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF --include "gemma-3-27b-it-q4_k_m.gguf" --local-dir ./ ``` If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: ``` huggingface-cli download https://huggingface.co/mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF --include "gemma-3-27b-it-q4_k_m.gguf/*" --local-dir ./ ``` You can either specify a new local-dir (deepseek-ai_DeepSeek-V3-0324-Q8_0) or download them all in place (./)- Downloads last month
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