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

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", device_map="auto")

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 (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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: 18

GGUF

Model size

27B params

Architecture

gemma3

Hardware compatibility

4-bit

6-bit

Model tree for mohanz/gemma-3-27b-it-Q4_K_M-Q6_K-GGUF

Base model

google/gemma-3-27b-pt

Finetuned

google/gemma-3-27b-it

Quantized

(140)

this model