Instructions to use Hampetiudo/gemma-2-Ifable-9B-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Hampetiudo/gemma-2-Ifable-9B-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Hampetiudo/gemma-2-Ifable-9B-i1-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": "Hampetiudo/gemma-2-Ifable-9B-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M

Ollama
How to use Hampetiudo/gemma-2-Ifable-9B-i1-GGUF with Ollama:
```
ollama run hf.co/Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
```

Unsloth Studio

How to use Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-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 Hampetiudo/gemma-2-Ifable-9B-i1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Hampetiudo/gemma-2-Ifable-9B-i1-GGUF to start chatting

Docker Model Runner
How to use Hampetiudo/gemma-2-Ifable-9B-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M
```

Lemonade

How to use Hampetiudo/gemma-2-Ifable-9B-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Hampetiudo/gemma-2-Ifable-9B-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.gemma-2-Ifable-9B-i1-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

🛑 Note: not every quant is displayed on the table on the right, you can find everything here.

Using llama.cpp release b3804 for quantization.

Original model: https://huggingface.co/ifable/gemma-2-Ifable-9B

All quants were made using the imatrix option (except BF16, that's the original precision). The imatrix was generated with the dataset from here, using the BF16 GGUF with a context size of 8192 tokens (default is 512 but higher/same as model context size should improve quality) and 13 chunks.

How to make your own quants:

https://github.com/ggerganov/llama.cpp/tree/master/examples/imatrix

https://github.com/ggerganov/llama.cpp/tree/master/examples/quantize

Downloads last month: 163

GGUF

Model size

9B params

Architecture

gemma2

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for Hampetiudo/gemma-2-Ifable-9B-i1-GGUF

Base model

ifable/gemma-2-Ifable-9B

Quantized

(12)

this model