Instructions to use hardcoremoore/gemma-3-270m-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hardcoremoore/gemma-3-270m-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="hardcoremoore/gemma-3-270m-GGUF",
	filename="gemma-3-270m-Q8_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use hardcoremoore/gemma-3-270m-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 hardcoremoore/gemma-3-270m-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf hardcoremoore/gemma-3-270m-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf hardcoremoore/gemma-3-270m-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf hardcoremoore/gemma-3-270m-GGUF:Q8_0

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 hardcoremoore/gemma-3-270m-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf hardcoremoore/gemma-3-270m-GGUF:Q8_0

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 hardcoremoore/gemma-3-270m-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf hardcoremoore/gemma-3-270m-GGUF:Q8_0

Use Docker

docker model run hf.co/hardcoremoore/gemma-3-270m-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use hardcoremoore/gemma-3-270m-GGUF with Ollama:
```
ollama run hf.co/hardcoremoore/gemma-3-270m-GGUF:Q8_0
```

Unsloth Studio

How to use hardcoremoore/gemma-3-270m-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 hardcoremoore/gemma-3-270m-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 hardcoremoore/gemma-3-270m-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for hardcoremoore/gemma-3-270m-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use hardcoremoore/gemma-3-270m-GGUF with Docker Model Runner:
```
docker model run hf.co/hardcoremoore/gemma-3-270m-GGUF:Q8_0
```

Lemonade

How to use hardcoremoore/gemma-3-270m-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull hardcoremoore/gemma-3-270m-GGUF:Q8_0

Run and chat with the model

lemonade run user.gemma-3-270m-GGUF-Q8_0

List all available models

lemonade list

gemma-3-270m GGUF

Recommended way to run this model:

llama-cli -hf ggml-org/gemma-3-270m-GGUF -c 0 -fa -p "hello"

Then, access http://localhost:8080

Downloads last month: 6

GGUF

Model size

0.3B params

Architecture

gemma3

Hardware compatibility

8-bit

Inference Providers NEW

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Model tree for hardcoremoore/gemma-3-270m-GGUF

Base model

google/gemma-3-270m

Quantized

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