Instructions to use snxtyle/gemma3-27b-ft-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use snxtyle/gemma3-27b-ft-q8 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="snxtyle/gemma3-27b-ft-q8",
	filename="model/Dp_Merged_Gemma3-27B-Q8.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use snxtyle/gemma3-27b-ft-q8 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf snxtyle/gemma3-27b-ft-q8
# Run inference directly in the terminal:
llama-cli -hf snxtyle/gemma3-27b-ft-q8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf snxtyle/gemma3-27b-ft-q8
# Run inference directly in the terminal:
llama-cli -hf snxtyle/gemma3-27b-ft-q8

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 snxtyle/gemma3-27b-ft-q8
# Run inference directly in the terminal:
./llama-cli -hf snxtyle/gemma3-27b-ft-q8

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 snxtyle/gemma3-27b-ft-q8
# Run inference directly in the terminal:
./build/bin/llama-cli -hf snxtyle/gemma3-27b-ft-q8

Use Docker

docker model run hf.co/snxtyle/gemma3-27b-ft-q8

LM Studio
Jan
Ollama
How to use snxtyle/gemma3-27b-ft-q8 with Ollama:
```
ollama run hf.co/snxtyle/gemma3-27b-ft-q8
```

Unsloth Studio new

How to use snxtyle/gemma3-27b-ft-q8 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 snxtyle/gemma3-27b-ft-q8 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 snxtyle/gemma3-27b-ft-q8 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for snxtyle/gemma3-27b-ft-q8 to start chatting

Docker Model Runner
How to use snxtyle/gemma3-27b-ft-q8 with Docker Model Runner:
```
docker model run hf.co/snxtyle/gemma3-27b-ft-q8
```

Lemonade

How to use snxtyle/gemma3-27b-ft-q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull snxtyle/gemma3-27b-ft-q8

Run and chat with the model

lemonade run user.gemma3-27b-ft-q8-{{QUANT_TAG}}

List all available models

lemonade list

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Gemma3-27B-FT-Q8

This repository contains a fine-tuned version of the Gemma-3-27B model, quantized to 8-bit precision.

Installation

To use this model, you need to install the transformers and torch libraries.

pip install transformers torch

Usage

You can use the model for text generation. Here is an example of how to load the model and generate text:

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("snxtyle/gemma3-27b-ft-q8")
model = AutoModelForCausalLM.from_pretrained("snxtyle/gemma3-27b-ft-q8")

prompt = "Where is DPIP failing?'"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate text
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

This is a basic example. You can find more information about the generate method and its parameters in the Hugging Face documentation.

License

This project is licensed under the MIT License. See the LICENSE file for details.

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GGUF

Model size

27B params

Architecture

gemma3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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